CN117890785B - Automatic detection method, system and equipment for direct-current resistance of motor - Google Patents
Automatic detection method, system and equipment for direct-current resistance of motor Download PDFInfo
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Abstract
The application relates to a motor direct current resistance automatic detection method, a system and equipment, the method respectively inputs the electric monitoring data and the environment monitoring data of a detected motor in the current operation period into a FPGA detection module based on software definition to carry out direct current resistance detection, and after each motor direct current resistance corresponding to each sampling point in the current operation period is obtained by utilizing a deep learning means, each motor direct current resistance is continuously read, a resistance characteristic curve of the detected motor in the current operation period is extracted, so that the resistance characteristic of a higher dimension in the current operation period is mined, the best matched standard curve is searched in a resistance characteristic library, and finally, the real-time direct current resistance and the static direct current resistance corresponding to the standard curve are directly read and output as the direct current resistance detection result of the detected motor, thereby achieving the purpose of ultra-high precision motor direct current resistance online detection integrating the high-dimensional characteristic and knowledge learning, and realizing the digitalized and intelligent efficient automatic detection effect in the resistance detection field.
Description
Technical Field
The invention belongs to the technical field of motor testing, and relates to a method, a system and equipment for automatically detecting direct current resistance of a motor.
Background
Along with development of electric intelligent technology, various types of motors are widely and deeply applied in various industries, the requirements on intelligent technology in the application process are higher, automatic detection of technical indexes such as resistance, current and temperature rise in the motor application process is realized through increasingly intelligent automatic detection technology, the automatic detection of direct current resistance in a traction motor is an urgent requirement for efficient application of the current motor, for example, the automatic detection of direct current resistance in the traction motor is one of the current typical application requirements, and the automatic detection has very important practical significance for efficient, stable and intelligent operation of the traction motor.
The motor winding mainly refers to stator and rotor coils of various types of alternating current and direct current motors, the quality of the stator and rotor coils directly affects the performance and reliability of the motors and even the whole machine, and the stator and rotor winding is an important link for quality control and inspection in the manufacturing and application processes of the motors. At present, the detection technology of the direct current resistance of the motor has been researched for a long time, and a plurality of mature direct current resistance measuring and calculating models are developed. However, with the development of modern industrial technologies, most of the conventional technologies for detecting the direct current resistance of the motor are based on static test or off-line test, which usually requires the motor to be actively stopped or to be detected after stopping due to failure, and cannot support on-line automatic direct current resistance detection, so that the operation efficiency of the industrial system where the motor is located is seriously reduced after stopping, and thus new solutions are urgently needed to cope with the current high-efficiency on-line automatic detection requirement.
Disclosure of Invention
Aiming at the problems in the traditional method, the invention provides an automatic detection method for the direct current resistance of the motor, an automatic detection system for the direct current resistance of the motor and computer equipment, which can greatly improve the detection efficiency of the direct current resistance of the motor.
In order to achieve the above object, the embodiment of the present invention adopts the following technical scheme:
in one aspect, a method for automatically detecting a direct current resistance of a motor is provided, which includes the steps of:
when an input direct-current resistance detection instruction is received, collecting electric monitoring data and environment monitoring data of a motor to be detected; the electrical monitoring data comprise winding current and terminal voltage in the current running period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data;
Respectively inputting the electric monitoring data and the environment monitoring data into an FPGA detection module based on software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type;
reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period;
performing feature retrieval in a resistance feature library according to the resistance feature curve to obtain a standard curve which is the best matched with the resistance feature curve;
And reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
On the other hand, still provide a motor direct current resistance automated inspection system, include:
The data acquisition module is used for acquiring electric monitoring data and environment monitoring data of the tested motor when receiving an input direct-current resistance detection instruction; the electrical monitoring data comprise winding current and terminal voltage in the current running period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data;
The model detection module is used for respectively inputting the electric monitoring data and the environment monitoring data into the FPGA detection module based on the software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type;
The characteristic extraction module is used for reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period;
the characteristic retrieval module is used for carrying out characteristic retrieval in the resistance characteristic library according to the resistance characteristic curve to obtain a standard curve which is the best matched with the resistance characteristic curve;
And the result output module is used for reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
In still another aspect, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method for automatically detecting a dc resistance of a motor described above when executing the computer program.
One of the above technical solutions has the following advantages and beneficial effects:
According to the automatic detection method, system and equipment for the motor direct current resistance, when the input direct current resistance detection instruction is received, the electric monitoring data and the environment monitoring data of the motor to be detected in the current operation period are collected, then the electric monitoring data and the environment monitoring data are respectively input into the FPGA detection module based on software definition to carry out direct current resistance detection, and after the direct current resistance of each motor corresponding to each sampling point in the current operation period is obtained by using a deep learning means, the direct current resistance monitoring model is obtained by semi-supervised learning training by using training data corresponding to the motor of the same type as the motor to be detected, and the training data comprise the electric monitoring data and the environment monitoring data of the motor of the same type in different time periods, so that the latest machine detection direct current resistance can be rapidly and accurately automatically given through the actually collected data.
Compared with the prior art, the machine-check direct-current resistor benefits from the learning and calculation output of a model, and depth detection information of a context is included to be more accurate, but in order to fully meet the requirements of high precision, digitization and intellectualization of motor application scenes, the machine-check direct-current resistor continuously reads the direct-current resistors of each motor and extracts the resistance characteristic curve of a tested motor in the current operation period, so that the resistance characteristic of higher dimension in the period is mined, the characteristic search is carried out in a resistance characteristic library according to the resistance characteristic, the best matching standard curve is found, the real-time direct-current resistor and the static direct-current resistor corresponding to the standard curve are finally read directly and output as the direct-current resistor detection result of the tested motor, the purpose of ultra-high precision motor direct-current resistor on-line detection integrating high-dimensional characteristics and knowledge learning is achieved, and the digitization and intelligent high-efficiency automatic detection effect in the field is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present application, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a schematic diagram of a first process of an automatic detection method of a DC resistance of a motor according to an embodiment;
FIG. 2 is a schematic diagram of a second flow chart of an automatic detection method of the DC resistance of the motor according to an embodiment;
FIG. 3 is a schematic diagram of a third flow chart of an automatic detection method for the DC resistance of the motor according to an embodiment;
FIG. 4 is a schematic diagram of a fourth flow chart of an automatic detection method for the DC resistance of the motor according to an embodiment;
FIG. 5 is a schematic diagram of a fifth flow chart of an automatic detection method for DC resistance of a motor according to an embodiment;
Fig. 6 is a schematic block diagram of an automatic motor dc resistance detection system according to an embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It is noted that reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Those skilled in the art will appreciate that the embodiments described herein may be combined with other embodiments. The term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The direct current resistance of the motor refers to the direct current resistance of the conductors in the motor windings. In motor windings, the direct current resistance of a conductor is the resistance encountered when current flows, and this resistance is typically determined by factors such as the material, cross-sectional area, and length of the conductor. Specifically, three-phase asynchronous motor: the dc resistance is the dc resistance of the conductors in the designated sub-windings and stator windings. Double-fed asynchronous motor: similar to a three-phase asynchronous motor, it includes a main winding and an additional winding, both of which have a direct current resistance. Permanent magnet synchronous motor: in permanent magnet synchronous motors, the direct current resistance generally specifies the resistance of the stator windings, as permanent magnets on the rotor generally do not provide resistance. Dc resistance is an important parameter that plays a critical role in the performance analysis and control of the motor. By measuring the direct current resistance, the resistance characteristics of the motor winding can be known, and the thermal analysis, the electrical characteristic analysis and the design of a control system of the motor are facilitated.
Embodiments of the present invention will be described in detail below with reference to the attached drawings in the drawings of the embodiments of the present invention.
Referring to fig. 1, in one embodiment, a method for automatically detecting a dc resistance of a motor is provided, which may include the following processing steps S12 to S20:
S12, when an input direct-current resistance detection instruction is received, collecting electric monitoring data and environment monitoring data of a motor to be detected; the electrical monitoring data comprises winding current and terminal voltage in the current operation period of the tested motor, and the environment monitoring data comprises winding temperature, winding humidity, vibration data and magnetic field data.
It can be understood that the direct current resistance detection instruction may refer to a direct current resistance detection control instruction input by an administrator through an upper computer and transmitted to a main control system of the tested motor, or may be a control instruction generated by directly operating a direct current resistance detection operation switch reserved on the tested motor by the administrator, or may be a direct current resistance detection control instruction automatically generated by a software-defined FPGA detection module designed and assembled for the tested motor according to a set detection time sequence, where the direct current resistance detection instruction may be used to trigger a sensor system of the tested motor to perform operations such as data monitoring and acquisition, for example, but not limited to, electrical monitoring data such as winding current and terminal voltage of the tested motor in a current operation period, and environmental monitoring data such as winding temperature, winding humidity, vibration data and magnetic field data. The current operation period can be, but not limited to, a resistance detection period of the order of milli-seconds, minutes, hours or days, and the length of the period can be set according to the field detection requirement.
S14, respectively inputting the electric monitoring data and the environment monitoring data into an FPGA detection module based on software definition to detect the direct current resistance, and obtaining the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type.
It can be understood that the FPGA detection module based on software definition is a professional software and hardware module designed and assembled for the tested motor, and uses a general FPGA chip circuit as a general hardware basis, and then carries a detection data processing software system/application adopting a software definition implementation manner on the special software and hardware module, so that the FPGA detection module can be quickly compatible with the assembly and use of the tested motor in a manner of defining parameters and software, and can achieve the customized application configuration for the tested motor in the current model, and can also support the quick transplanting and application of the FPGA detection module on different types of motors in an application scene, thereby strengthening the universality of software and hardware. The software definition development of the detection data processing software system/application of different motors to be detected can be specifically performed in a corresponding software function module mode according to the model number, the working parameter type, the processing requirement and the like of the motors to be detected, and the development tool can adopt various existing software definition platforms as long as the FPGA detection module can effectively execute the data processing function of the motors to be detected.
The direct current resistance monitoring model based on deep learning can adopt one or more mature deep learning models, training data corresponding to the motors of the same type of the motor to be tested are collected in advance, for example, electric monitoring data and environment monitoring data which are continuously collected on line under the working condition similar to that of the motor to be tested for the motors of the same type, and then the direct current resistance monitoring model with stable performance is obtained through semi-supervised learning training by utilizing the training data, wherein part of the training data can be marked manually or by a machine, for example, part of winding current, terminal voltage, winding temperature, winding humidity, vibration data and magnetic field data can be selected to be subjected to labeling processing with corresponding direct current resistance, and the training data and other untagged training data are used for training the direct current resistance monitoring model, so that the model can provide high-accuracy resistance prediction output while training efficiency is improved.
Specifically, for monitoring a motor to be tested, before monitoring, the FPGA detection module based on software definition can be assembled on the motor to be tested, for example, the FPGA detection module is installed in a main control system of the motor to be tested in an additional module mode, and electrical connection and data connection are established so as to meet the power supply of the FPGA detection module, the access of the electrical monitoring data and the environmental monitoring data, an administrator can configure motor parameters for the FPGA detection module in a software definition mode, for example, set working parameters such as motor type, rated voltage, rated power and winding specification of the motor to be tested, configure data transmission protocol format between the FPGA detection module and the main control system of the motor to be tested, and after the initialization operation is completed, the FPGA detection module can be normally used for detecting direct current resistance of the motor to be tested. The configuration of the FPGA detection module can be completed on the corresponding operation interface through interaction modes such as keyboard and mouse operation, touch operation, gesture operation or voice control operation and the like by an administrator.
The direct current resistance of each motor corresponding to each sampling point in the current operation period is a series of direct current resistance values predicted after learning by using the trained direct current resistance monitoring model and electric monitoring data and environment monitoring data corresponding to each sampling point in the current operation period, wherein the electric monitoring data and the environment monitoring data in the current operation period can be continuous in time or can be divided into a plurality of sections according to the acquisition time interval, and then the data sampling can be carried out from the directly acquired electric monitoring data and environment monitoring data by setting a proper sampling rate, so that input sample points suitable for model processing are obtained, and the model can learn and predict output according to the input sample points.
S16, reading the direct current resistance of each motor and extracting the characteristics to obtain the resistance characteristic curve of the motor to be tested in the current operation period.
It can be understood that, since the dc resistance of each motor output by the model is not completely equal to the resistance of the measured motor, although the dc resistance of each motor at this time has higher precision and reliability due to the deep learning ability of the model, the dc resistance of each motor can be used as the predicted resistance output of the deep learning for the manager to use, but because the measured motor is automatically detected without stopping the machine in the detection process, there may be local drift of the predicted dc resistance due to some occasional disturbance of the operating condition, so in order to sufficiently reduce the deviation existing in the on-line automatic detection, after obtaining the dc resistance of each motor corresponding to each sampling point in the current operating period, feature extraction needs to be performed on the time series data to mine trend and change information with higher reliability in the data, for example, but not limited by statistics features (such as a mean resistance value, a variance resistance, a maximum resistance value or a minimum resistance value), frequency domain features of the resistor (such as a fourier transform method), time domain features of the resistor (such as a sliding window) or periodic analysis of the resistance change, etc., so that the feature is automatically extracted from the time domain features or the time domain features of the resistor, such as a statistical feature, that is a statistical feature is generated by the system, and the feature is a corresponding to these feature curve.
And S18, performing feature retrieval in a resistance feature library according to the resistance feature curve to obtain a standard curve which is the best matched with the resistance feature curve.
It can be understood that the resistance characteristic library is a standard curve database pre-established by a pointer for the same type of motor to be tested, and may include standard curves of one or more characteristics focused by an administrator, where the standard curves are pre-measured and generated under typical working conditions of actual operation of the motor, and may be generated by, but not limited to, synchronously recording direct current resistance data of the motor during a durability test or a reliability test before shipping the same type of motor, performing characteristic extraction, and also may be generated and stored after learning in advance according to a large amount of monitoring data of a large amount of motors of the same type by using a direct current resistance monitoring model with higher maturity and higher performance. These standard curves are usually subjected to a comparison test, and can correspondingly display the resistance value at each sampling point with a very small error. The standard curve that best matches the resistance characteristic curve may be determined by the system using similarity calculations or nearest distance, etc.
S20, reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
It can be understood that, for the tested motor which is required to perform online automatic detection at present, the standard curve of the resistance characteristic curve which is most matched with the resistance characteristic curve in the resistance characteristic library is found out through retrieval, the corresponding real-time direct current resistance and static direct current resistance under each calibrated sampling point are directly read from the standard curve, and finally the real-time direct current resistance and static direct current resistance are output as a main direct current resistance detection result. Therefore, on the basis of obtaining the direct current resistance of each motor of the intermediate prediction output, the direct current resistance detection result with higher precision and reliability is read out through a standard curve matching mode to serve as a final automatic detection result, the possible interference of the prediction output is effectively isolated, meanwhile, the high precision of detection is ensured, an administrator can conveniently carry out mutual verification, reference use and motor supervision decision making according to the two detection results, and as the model is continuously and iteratively optimized along with the data accumulation of the detection process, the precision of the detection result is improved, and finally, the online detection of the direct current resistance of the motor with ultra-high precision can be continuously and automatically realized. In addition, the detection process can detect the motor without stopping the motor to be detected or providing special excitation, and the sensing of the motor to be detected is directly used for collecting data, so that the operation benefit of the motor to be detected can be improved.
According to the automatic detection method for the motor direct current resistance, when the input direct current resistance detection instruction is received, the electric monitoring data and the environment monitoring data of the motor to be detected in the current operation period are collected, then the electric monitoring data and the environment monitoring data are respectively input into the FPGA detection module based on software definition to carry out direct current resistance detection, and after the direct current resistance of each motor corresponding to each sampling point in the current operation period is obtained by using a deep learning means, the direct current resistance monitoring model is obtained by semi-supervised learning training by using training data corresponding to the motor of the same type as the motor to be detected, and the training data comprise the electric monitoring data and the environment monitoring data of the motor of the same type in different time periods, so that the latest machine detection direct current resistance can be rapidly and accurately automatically given through the actually collected data.
Compared with the prior art, the machine-check direct-current resistor benefits from the learning and calculation output of a model, and depth detection information of a context is included to be more accurate, but in order to fully meet the requirements of high precision, digitization and intellectualization of motor application scenes, the machine-check direct-current resistor continuously reads the direct-current resistors of each motor and extracts the resistance characteristic curve of a tested motor in the current operation period, so that the resistance characteristic of higher dimension in the period is mined, the characteristic search is carried out in a resistance characteristic library according to the resistance characteristic, the best matching standard curve is found, the real-time direct-current resistor and the static direct-current resistor corresponding to the standard curve are finally read directly and output as the direct-current resistor detection result of the tested motor, the purpose of ultra-high precision motor direct-current resistor on-line detection integrating high-dimensional characteristics and knowledge learning is achieved, and the digitization and intelligent high-efficiency automatic detection effect in the field is realized.
In one embodiment, as shown in fig. 2, further, the automatic detection method of the direct current resistance of the motor may further include the following processing step S21:
S21, carrying out peak search on the resistance characteristic curve, and determining influence factors corresponding to the direct current resistance of each motor; the influencing factors include at least one of current, temperature, humidity, vibration, and magnetic field.
It may be appreciated that in this embodiment, the peak search may be performed on the obtained resistance characteristic curve, so as to find the position of each peak (which may include a valley point, a peak point and a distortion point), and then the corresponding monitoring data at these positions are extracted, so that the system may analyze which one or more data play a main role in the formation of the peak according to the monitoring data at these positions, so that the data playing a main role is determined as the corresponding influencing factor, for example, the peak point of a certain resistance characteristic change is caused by current and vibration, so that it may be output to instruct an administrator to focus on whether the working current and vibration of the motor have occurred or whether the abnormality will occur, so as to facilitate making a coping strategy in time or in advance to prevent the measured motor from being damaged.
In one embodiment, as shown in fig. 2, further, the automatic detection method of the direct current resistance of the motor may further include the following processing step S22:
s22, marking each influence factor to a corresponding position on the resistance characteristic curve and performing visual display.
It can be understood that in this embodiment, each determined influence factor may be further indicated visually at a corresponding position on the resistance characteristic curve, so that an administrator can more conveniently and intuitively see, through the detected real-time data display interface, important points on the resistance characteristic curve and influence factors thereof in the current operation period, so that the administrator can take countermeasures at any time according to the detection situation, and the monitoring efficiency of the direct current resistance of the motor is improved.
In one embodiment, as shown in fig. 3, further, the automatic detection method of the direct current resistance of the motor may further include the following processing steps S23 and S24:
S23, scoring and counting corresponding influence factors according to the peak value size sequence of the resistance characteristic curve, and determining the most obvious influence factor with the highest score;
And S24, marking the scored influence factors to corresponding positions on the resistance characteristic curve respectively and performing visual display.
It can be understood that, preferably, the peak values of the found resistance characteristic curves can be output and stored in the corresponding array for standby, then the peak values are sequentially scored according to the order of magnitude, for example, the first echelon is scored for each influence factor corresponding to the largest peak value, then the second echelon is scored for each influence factor corresponding to the next largest peak value, and so on until the scoring of each influence factor corresponding to all peak values is completed. Wherein the score of the same influencing factor in the first scoring of the ladder is higher than the score in the second scoring of the ladder, and so on. Finally, counting the number and the scores of the influence factors corresponding to all peaks, reserving one influence factor with the highest score under each peak as the most obvious influence factor at the peak position for highlighting, wherein other influence factors at the peak position can be displayed in a hidden mode (namely, the influence factors are displayed only when a cursor operated by an administrator stays at the peak position, and the rest of the influence factors are displayed only at the most obvious influence factor). For the same influencing factor which is the most significant influencing factor, the same influencing factor can be displayed cooperatively at each corresponding peak position in a mode of highlighting color (such as red or yellow) or giving a mark frame. Therefore, the presentation and indication efficiency of the motor direct current resistance detection result can be greatly improved, and the monitoring efficiency of the motor direct current resistance is further improved.
In one embodiment, the deep learning based direct current resistance monitoring model includes a recurrent neural network, or an automatic encoder.
It can be understood that in this embodiment, any one network model of the cyclic neural network, the recurrent neural network or the automatic encoder may be adopted to train and obtain the required direct current resistance monitoring model, so that the model technology is high in maturity and controllable in cost, and helps to improve the detection efficiency and reduce the maintenance cost.
In one embodiment, as shown in fig. 4, further, the automatic detection method of the direct current resistance of the motor may further include the following processing steps S17 to S19:
S17, when an input active excitation instruction is received, controlling the FPGA detection module to output characteristic current to the motor to be detected;
s18, collecting the winding end voltage variation of the tested motor under the excitation of characteristic current;
S19, calculating and outputting the direct current resistance of the motor to be tested according to the characteristic current and the terminal voltage variation.
It can be understood that in some application scenarios, the FPGA detection module can be directly controlled by an instruction to perform simple measurement on the tested motor, for example, when the tested motor is stopped, an active excitation instruction can be directly input into the FPGA detection module to control the output of the tested motor to set characteristic current, so that the tested motor generates corresponding winding end voltage variation under the action of the characteristic current, the winding end voltage variation is collected by a sensing system of the tested motor, and finally, the direct current resistance of the tested motor at the moment is directly calculated by utilizing ohm law according to the characteristic current and the winding end voltage variation, thereby meeting the direct current resistance rapid measurement of single data point under the stopping condition and improving the direct current resistance measurement flexibility and efficiency of the tested motor.
In one embodiment, the current operating period includes a plurality of discrete data sampling and detection slots. It can be understood that in this embodiment, a discontinuous detection time period may also be adopted for the current operation period of the detected motor to execute the detection task, for example, the data sampling and detection time slots of the FPGA detection module may be set online in a software-defined manner, and a plurality of such data sampling and detection time slots may be set separately in different time periods during the operation of the detected motor, so that the FPGA detection module monitors the data sampling and detection activity only in these data sampling and detection time slots, so as to cover the efficient detection requirement under the longer period of attention of the administrator, on one hand, to avoid the consumption of resources calculated in a huge amount under the continuous data flow, and on the other hand, to continuously track the direct current resistance change condition of the motor caused under the detection main influencing factors by elongating the detection time range, thereby improving the accuracy of the judgment of the fault condition or the life condition of the motor. The length of each data sampling and detecting time slot can be set according to different detecting requirements, and the lengths of different data sampling and detecting time slots can be the same or different, and can be specifically selected according to the detecting time period/working condition to be concerned.
In one embodiment, after outputting the dc resistance detection result of the motor to be tested, as shown in fig. 5, the automatic motor dc resistance detection method may further include the following processing steps S25 and S26:
s25, comparing the direct-current resistance detection result with a pre-stored resistance alarm threshold value;
S26, if the direct current resistance detection result deviates from the tolerance corresponding to the resistance alarm threshold, generating a direct current resistance abnormality alarm signal and associating the most obvious influence factors for visual display.
It can be understood that, in order to send an alarm to an administrator more directly and timely when an abnormality may occur in a detected motor, in this embodiment, the resistance alarm threshold of the detected motor may be written into the FPGA detection module in advance by a software-defined manner, so that the FPGA detection module may perform a task of detecting a direct current resistance while monitoring whether the direct current resistance detection result deviates significantly from the resistance alarm threshold, if so, a direct current resistance abnormality alarm signal is generated and associated with the most significant influence factor at the peak position corresponding to the direct current resistance detection result, and finally the direct current resistance abnormality alarm signal and the corresponding most significant influence factor are visually displayed together. The corresponding resistance alarm threshold values of different motors to be tested can be set according to the normal direct current resistance of the motor of the type, so long as the direct current resistance can be effectively distinguished whether the direct current resistance is still within the normal value range.
Through the processing steps, whether the direct current resistance of the motor to be detected is normal or not can be monitored in real time in the whole process of automatic detection of the direct current resistance of the motor, if not, abnormal alarming can be quickly and intuitively generated, and the most obvious influence factors most likely to cause the occurrence of the abnormality are displayed, so that the detection efficiency and quality of the direct current resistance of the motor are more effectively improved. The technical scheme of the application has been fully tested and simulated in the traction motor test system products of the applicant, and has better expected use effect and higher industrial popularization value.
It should be understood that, although the steps in the flowcharts 1 to 5 described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of the flowcharts 1 through 5 described above may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 6, an automatic motor direct current resistance detection system 100 is provided, which includes a data acquisition module 11, a model detection module 13, a feature extraction module 15, a feature retrieval module 17, and a result output module 19. The data acquisition module 11 is used for acquiring electric monitoring data and environment monitoring data of the tested motor when receiving an input direct-current resistance detection instruction; the electrical monitoring data comprises winding current and terminal voltage in the current operation period of the tested motor, and the environment monitoring data comprises winding temperature, winding humidity, vibration data and magnetic field data. The model detection module 13 is used for respectively inputting the electric monitoring data and the environment monitoring data into the FPGA detection module based on the software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type. The feature extraction module 15 is used for reading the direct current resistance of each motor and extracting features to obtain a resistance feature curve of the motor to be tested in the current operation period. The feature retrieval module 17 is configured to perform feature retrieval in the resistance feature library according to the resistance feature curve, so as to obtain a standard curve that is the best match with the resistance feature curve. The result output module 19 is used for reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the result as a direct current resistance detection result of the motor to be detected.
It will be understood that, with respect to the explanation of each feature in the present embodiment, the explanation of the corresponding feature of the automatic detection method for the dc resistance of the motor may be understood in the same way, and will not be repeated here.
According to the motor direct current resistance automatic detection system 100, when an input direct current resistance detection instruction is received, electric monitoring data and environment monitoring data of a motor to be detected in a current operation period are collected, then the electric monitoring data and the environment monitoring data are respectively input into the FPGA detection module based on software definition to carry out direct current resistance detection, and after each motor direct current resistance corresponding to each sampling point in the current operation period is obtained by utilizing a deep learning means, the direct current resistance monitoring model is obtained by utilizing training data corresponding to the motor of the same type of motor to be detected through semi-supervised learning training, and the training data comprise the electric monitoring data and the environment monitoring data of the motor of the same type in different time periods, so that the latest machine detection direct current resistance can be automatically and rapidly given through the actually collected data.
Compared with the prior art, the machine-check direct-current resistor benefits from the learning and calculation output of a model, and depth detection information of a context is included to be more accurate, but in order to fully meet the requirements of high precision, digitization and intellectualization of motor application scenes, the machine-check direct-current resistor continuously reads the direct-current resistors of each motor and extracts the resistance characteristic curve of a tested motor in the current operation period, so that the resistance characteristic of higher dimension in the period is mined, the characteristic search is carried out in a resistance characteristic library according to the resistance characteristic, the best matching standard curve is found, the real-time direct-current resistor and the static direct-current resistor corresponding to the standard curve are finally read directly and output as the direct-current resistor detection result of the tested motor, the purpose of ultra-high precision motor direct-current resistor on-line detection integrating high-dimensional characteristics and knowledge learning is achieved, and the digitization and intelligent high-efficiency automatic detection effect in the field is realized.
In one embodiment, the automatic motor dc resistance detection system 100 may also be used to implement the step functions of other embodiments of the automatic motor dc resistance detection method.
For specific limitations of the motor dc resistance automatic detection system 100, reference may be made to the corresponding limitations of the motor dc resistance automatic detection method described above, and no further description is given here. The above-mentioned modules in the motor direct current resistance automatic detection system 100 may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in hardware or independently of equipment with the experimental component management function, or can be stored in a memory of the equipment in a software mode so that a processor can call and execute operations corresponding to the modules, and the equipment can be, but is not limited to, various computing terminal equipment existing in the field.
In one embodiment, there is also provided a computer device including a memory and a processor, the memory storing a computer program, the processor implementing the following processing steps when executing the computer program: when an input direct-current resistance detection instruction is received, collecting electric monitoring data and environment monitoring data of a motor to be detected; the electrical monitoring data comprise winding current and terminal voltage in the current running period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data; respectively inputting the electric monitoring data and the environment monitoring data into an FPGA detection module based on software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type; reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period; performing feature retrieval in a resistance feature library according to the resistance feature curve to obtain a standard curve which is the best matched with the resistance feature curve; and reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
It will be appreciated that the above-mentioned computer device includes, in addition to the above-mentioned memory and processor, other software and hardware components not listed in this specification, and may be specifically determined according to the model of the specific monitoring computer device in different application scenarios, which will not be listed in detail in this specification.
In one embodiment, the processor may further implement the steps or sub-steps added in the embodiments of the method for automatically detecting the direct current resistance of the motor when executing the computer program.
In one embodiment, there is also provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the following processing steps: when an input direct-current resistance detection instruction is received, collecting electric monitoring data and environment monitoring data of a motor to be detected; the electrical monitoring data comprise winding current and terminal voltage in the current running period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data; respectively inputting the electric monitoring data and the environment monitoring data into an FPGA detection module based on software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprise electric monitoring data and environment monitoring data of the motors of the same type; reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period; performing feature retrieval in a resistance feature library according to the resistance feature curve to obtain a standard curve which is the best matched with the resistance feature curve; and reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
In one embodiment, the computer program may be executed by the processor to implement the steps or sub-steps added in the embodiments of the method for automatically detecting the direct current resistance of the motor.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, RDRAM for short), and interface dynamic random access memory (DRDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it is possible for those skilled in the art to make several variations and modifications without departing from the spirit of the present application, which fall within the protection scope of the present application. The scope of the application is therefore intended to be covered by the appended claims.
Claims (9)
1. The automatic detection method for the direct current resistance of the motor is characterized by comprising the following steps of:
When an input direct-current resistance detection instruction is received, collecting electric monitoring data and environment monitoring data of a motor to be detected; the electrical monitoring data comprise winding current and terminal voltage in the current operation period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data;
Respectively inputting the electric monitoring data and the environment monitoring data into an FPGA detection module based on software definition to detect direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprises electric monitoring data and environment monitoring data of the motors of the same type;
reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period;
Performing feature retrieval in a resistance feature library according to the resistance feature curve to obtain a standard curve which is most matched with the resistance feature curve;
And reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
2. The automatic detection method of direct current resistance of a motor according to claim 1, further comprising the steps of:
carrying out peak value search on the resistance characteristic curve, and determining influence factors corresponding to the direct current resistance of each motor; the influencing factors include at least one of current, temperature, humidity, vibration, and magnetic field.
3. The automatic detection method of the direct current resistance of the motor according to claim 2, further comprising the steps of:
And marking each influence factor to a corresponding position on the resistance characteristic curve and carrying out visual display.
4. The automatic detection method of the direct current resistance of the motor according to claim 2, further comprising the steps of:
scoring and counting the corresponding influence factors according to the peak value size sequence of the resistance characteristic curve, and determining the most obvious influence factor with the highest score;
marking the scored influence factors to the corresponding positions on the resistance characteristic curves respectively and carrying out visual display.
5. The automatic motor direct current resistance detection method according to any one of claims 1 to 4, wherein the direct current resistance monitoring model based on deep learning includes a recurrent neural network, or an automatic encoder.
6. The method of claim 5, wherein the current operating period comprises a plurality of discrete data sampling and detection time slots.
7. The automatic detection method of the direct current resistance of the motor according to claim 5, further comprising the step of, after outputting the detection result of the direct current resistance of the motor to be detected:
comparing the direct current resistance detection result with a pre-stored resistance alarm threshold value;
And if the direct current resistance detection result deviates from the tolerance corresponding to the resistance alarm threshold, generating a direct current resistance abnormal alarm signal and carrying out visual display after correlating with the most obvious influence factors.
8. An automatic detection system for a direct current resistance of a motor, comprising:
The data acquisition module is used for acquiring electric monitoring data and environment monitoring data of the tested motor when receiving an input direct-current resistance detection instruction; the electrical monitoring data comprise winding current and terminal voltage in the current operation period of the tested motor, and the environment monitoring data comprise winding temperature, winding humidity, vibration data and magnetic field data;
The model detection module is used for respectively inputting the electric monitoring data and the environment monitoring data into the FPGA detection module based on software definition to detect the direct current resistance, so as to obtain the direct current resistance of each motor corresponding to each sampling point in the current operation period; the FPGA detection module is internally provided with a direct current resistance monitoring model based on deep learning, the direct current resistance monitoring model is obtained by semi-supervised learning training through training data corresponding to the motors of the same type of the motor to be detected, and the training data comprises electric monitoring data and environment monitoring data of the motors of the same type;
The characteristic extraction module is used for reading the direct current resistance of each motor and extracting the characteristics to obtain a resistance characteristic curve of the motor to be tested in the current operation period;
the characteristic retrieval module is used for carrying out characteristic retrieval in a resistance characteristic library according to the resistance characteristic curve to obtain a standard curve which is most matched with the resistance characteristic curve;
And the result output module is used for reading the real-time direct current resistance and the static direct current resistance corresponding to the standard curve and outputting the real-time direct current resistance and the static direct current resistance as a direct current resistance detection result of the motor to be detected.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method for automatically detecting the direct current resistance of a motor according to any one of claims 1 to 7.
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| CN114636846A (en) * | 2022-03-16 | 2022-06-17 | 福州昆硕宸信息科技有限公司 | Neural network improvement-based resistance cold end temperature compensation algorithm and cable resistance online detection equipment thereof |
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| CN105403772A (en) * | 2015-10-30 | 2016-03-16 | 芜湖市振华戎科智能科技有限公司 | Transformer DC resistance testing device |
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| CN114636846A (en) * | 2022-03-16 | 2022-06-17 | 福州昆硕宸信息科技有限公司 | Neural network improvement-based resistance cold end temperature compensation algorithm and cable resistance online detection equipment thereof |
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