JP7600048B2 - Equipment diagnostic system and equipment diagnostic method - Google Patents

Description

An embodiment of the present invention relates to device diagnostic technology.

Conventionally, there are technologies that utilize data related to past abnormalities or lifespan to determine the presence or absence of abnormalities in an electric motor or evaluate its remaining lifespan from measurement data detected by a sensor. For example, a technology is known that uses machine learning to learn by associating measurement data with actual lifespan and predicts the remaining lifespan.

Patent No. 6140229 Patent No. 6625280

When using data related to past abnormalities or life spans to judge the presence or absence of abnormalities or to evaluate remaining life spans, abnormality data acquired when an abnormality exists is required. However, it is difficult to acquire abnormality data for equipment with few cases of abnormalities, and it is difficult to correlate measurement data with the cause of the abnormality and actual life span. For example, important equipment that can lead to long-term shutdowns in large plants, or equipment important to safety in nuclear plants, is regularly disassembled and inspected, and repaired or replaced before an abnormality occurs. As a result, there are very few cases of abnormalities occurring, and little abnormality data is acquired. In addition, various abnormality modes can occur for a single piece of equipment, and it is necessary to acquire abnormality data corresponding to all such abnormality modes.

In particular, there are limitations to the number of sensors and the locations where they can be installed, and it may not be possible to directly obtain the measurement data needed to evaluate loads, etc., making it impossible to make a diagnosis. In addition, technology is often used to monitor the vibration of electric motors and determine whether there are signs of abnormality from changes in level and frequency characteristics. However, because changes in vibration depend on the frequency characteristics of the equipment or the installation status of the sensor, it can be difficult to determine whether there is an abnormality or to estimate the cause. Furthermore, diagnosing only specific parts of the motor may result in abnormalities being overlooked in other parts.

The embodiment of the present invention has been made in consideration of these circumstances, and aims to provide equipment diagnostic technology that can evaluate equipment for abnormalities from actual measurement data, even for equipment that has never experienced an abnormality in the past.

The device diagnostic system according to an embodiment of the present invention includes a model construction unit that constructs a physical model that reproduces the operation of a device composed of multiple devices including at least a rotating electric machine and reproduces the state of the device that occurs in relation to the operation, an analysis unit that performs calculations by reflecting parameters in an equation formulated based on the physical model, a learning unit that learns each of the parameters corresponding to the calculation results of the analysis unit as teacher data, a physical quantity measurement unit that acquires physical quantities actually measured in each of the devices as actual measurement data, a parameter identification unit that identifies actual measurement parameters, which are the parameters when the calculation results of the analysis unit reproduce the actual measurement data, based on the teacher data learned by the learning unit, and a state determination unit that determines whether or not there is an abnormality in the device based on each of the actual measurement parameters identified by the parameter identification unit.

Embodiments of the present invention provide equipment diagnostic technology that can evaluate equipment for abnormalities from actual measurement data, even for equipment that has never experienced an abnormality in the past.

FIG. 1 is a block diagram showing a system configuration of an apparatus diagnostic system. FIG. 2 is a configuration diagram showing an apparatus diagnostic system connected to an apparatus. FIG. 4 is an explanatory diagram showing a mode in which a physical quantity obtained from a sensor is converted into analysis data. FIG. 11 is an explanatory diagram showing a first example of improper installation of a coupling. FIG. 11 is an explanatory diagram showing a second example of improper installation of a coupling. FIG. 4 is an explanatory diagram showing an aspect of learning based on parameters and calculation results. A graph showing fatigue versus time. 1 is a graph showing the relationship between stress amplitude and number of repeated cycles to break. 1 is a graph showing an operation mode of remaining life evaluation. 4 is a flowchart showing an apparatus diagnosis method.

Below, we will explain in detail the embodiments of the device diagnostic system and device diagnostic method with reference to the drawings.

The reference numeral 1 in FIG. 1 denotes an equipment diagnostic system according to this embodiment. This equipment diagnostic system 1 optimizes the inspection plan for a given equipment 20 (FIG. 2), and when an abnormality occurs, it supports the maintenance activities for the equipment 20 by presenting the cause and the location of the abnormality. In particular, for equipment 20 that has never experienced an abnormality in the past, it is used to evaluate the presence or absence of abnormal symptoms, the degree of abnormality, the type of abnormality, and the remaining life of each piece of equipment 21 from actual measurement data.

The device diagnosis system 1 of this embodiment is configured as a computer having hardware resources such as a CPU, ROM, RAM, and HDD, and in which software-based information processing is realized using the hardware resources as the CPU executes various programs. Furthermore, the device diagnosis method of this embodiment is realized by having the computer execute the various programs.

First, the system configuration of the device diagnostic system 1 will be described with reference to the block diagram shown in Figure 1.

The device diagnostic system 1 includes a plurality of sensors 2 and an analysis computer 3. The analysis computer 3 includes an input unit 4, an output unit 5, a communication unit 6, a memory unit 7, and a main control unit 8.

Predetermined information is input to the input unit 4 in response to operations by a user who uses the system. This input unit 4 includes input devices such as a mouse or a keyboard. In other words, predetermined information is input to the input unit 4 in response to operations of these input devices.

The output unit 5 outputs predetermined information. The device diagnosis system 1 of this embodiment includes a device that displays images, such as a display that outputs analysis results. In other words, the output unit 5 controls the images displayed on the display. The display may be separate from the computer main body, or may be integrated with it.

The device diagnosis system 1 of this embodiment may control images displayed on a display of another computer connected via a network. In this case, the output unit 5 of the other computer may control the output of the analysis results of this embodiment.

In this embodiment, a display is used as an example of an image display device, but other configurations are also possible. For example, information may be displayed using a head-mounted display or a projector. Furthermore, a printer that prints information on paper media may be used instead of a display. In other words, the objects controlled by the output unit 5 may include a head-mounted display, a projector, or a printer.

The communication unit 6 communicates with other computers via a communication line such as the Internet. In this embodiment, the device diagnosis system 1 and the other computers are connected to each other via the Internet, but other configurations are also possible. For example, they may be connected to each other via a LAN (Local Area Network), a WAN (Wide Area Network), or a mobile communication network.

The storage unit 7 stores various information required for device diagnosis. The storage unit 7 also includes a specified database. These are collections of information stored in memory, a HDD, or the cloud, and organized so that they can be searched or accumulated.

The main control unit 8 includes a model construction unit 9, an analysis unit 10, a learning unit 11, a physical quantity measurement unit 12, an actual measurement data processing unit 13, a parameter identification unit 14, a state determination unit 15, an anomaly identification unit 16, a remaining life evaluation unit 17, and an inspection estimation unit 18. These are realized by the CPU executing a program stored in the memory or HDD.

Note that each component of the analysis computer 3 does not necessarily have to be provided on a single computer. For example, a single system may be realized using multiple computers connected to each other via a network. For example, the learning unit 11 may be provided on a computer separate from the analysis computer 3.

As shown in FIG. 2, the device 20 to be diagnosed in this embodiment is, for example, a device installed in a nuclear power plant. The device 20 is composed of multiple devices 21. For example, the device 20 is composed of a control panel 21A, an electric motor 21B, a rotating body 21C, and a group of devices 21D on the load side.

Here, the control panel 21A corresponds to a component (control unit) involved in controlling the behavior of the electric motor 21B. For example, it controls the rotation speed or torque of the electric motor 21B.

The rotating body 21C is a component that transmits the energy output from the electric motor 21B to the load side equipment group 21D.

The load-side equipment group 21D corresponds to components that consume the energy transmitted from the rotating body 21C. The load-side equipment group 21D is composed of, for example, a pump, a fan, gears, etc.

Here, an electric motor 21B is exemplified as a rotating electric machine in this embodiment. In other words, the device 20 is composed of a plurality of devices 21 including at least a rotating electric machine. The rotating electric machine may be a generator. This embodiment can be applied whether the rotating electric machine is an electric motor 21B or a generator.

For example, suppose there is a parameter for a specific frequency related to a rotating electric machine. This frequency can be either the frequency of the power output from the rotating electric machine, or the frequency of the power supplied to the rotating electric machine. If the rotating electric machine is an electric motor 21B, this frequency is the frequency of the AC supplied to this electric motor 21B from the outside. Also, if the rotating electric machine is a generator, this frequency is the frequency of the AC output from this generator to the outside.

Each sensor 2 of the device diagnostic system 1 is installed in the device 20. The sensor 2 detects at least one of the following physical quantities: vibration, current, voltage, magnetic flux, temperature, pressure, and rotational torque. In this way, the sensor 2 can obtain physical quantities for any type of device 21.

For example, sensors 2 are provided on the control panel 21A, the electric motor 21B, the rotating body 21C, and the load side equipment group 21D. Predetermined information acquired by these sensors 2 is input to the analysis computer 3. The sensors 2 may not only be pre-installed on the device 20, but may also be added each time an inspection is performed. Also, the location of the sensors 2 that have already been installed may be changed.

These sensors 2 detect physical quantities that change depending on the state of the device 20. Specific examples of physical quantities include the current and voltage in the control panel 21A and the electric motor 21B, the temperature of the electric motor 21B, the vibration of the electric motor 21B and the rotating body 21C, the magnetic flux, load, and torque of the electric motor 21B, etc.

In this embodiment, the model construction unit 9 (FIG. 1) constructs a physical model that reproduces the operation of the device 20 and reproduces the state of the equipment 21 that occurs in relation to the operation. Note that the physical model is a virtual model that can simulate physical quantities related to the device 20. By using the physical model, it is possible to not only simulate the equipment 21 that is operating normally, but also to simulate the equipment 21 that is operating abnormally.

The physical model reproduces the behavior of the device 20 by a combination of parameters. For example, the physical model includes at least one of a mechanical model, a control model, an electrical model, a magnetic model, a thermal model, a fluid model, a physical property model, and a structural model.

Here, the mechanical model represents the behavior of the mechanical system related to at least one of the rotational motion and translational motion of each device 21, such as the electric motor 21B, the rotating body 21C, and the load side device group 21D. The control model is for the control panel 21A, which serves as a control unit constituting at least one device 21, to control the behavior of the entire device 20. The electrical model represents the behavior of the electrical system of the device 20. The magnetic model represents the behavior of the magnetic system of the device 20. The thermal model represents the thermal behavior of the device 20. The fluid model represents the behavior of at least one of the flow rate and pressure of the fluid flowing through the piping connected to the pump constituting at least one device 21. The physical property model represents the material properties of each device 21. The structural model has information on at least one of the dimensions and shape of each device 21. In this way, the behavior of the device 20 can be precisely reproduced according to various models.

In other words, the physical model is a model that models and formulates the structure or function of the device 20. A set of equations corresponding to these are then formulated.

The physical model also includes an abnormality model that indicates an abnormality in each of the devices 21 that constitute the apparatus 20. For example, as shown in Figures 4 and 5, consider a coupling as a rotating body 21C. The effect of eccentricity e _c or deflection angle d _c from the center of rotation caused by poor installation of the coupling is formulated as a model. Furthermore, parameters that indicate the degree of abnormality, such as the magnitude of eccentricity e _c or deflection angle d _c, are defined.

Furthermore, abnormality models for the rotor 21C include misalignment of the bearings, breakage of the rotor bar or short-circuit ring of the motor 21B, protrusion of the rotor bar, protrusion of the coil inside the stator or slot, bending or cracking of the rotor 21C, missing parts, short circuit or burnout of the stator winding, unbalanced power supply voltage, irregular waves in the inverter, and defects in the rolling bearings (inner ring, outer ring, rolling elements). Parameters for each abnormality are defined.

The analysis unit 10 (Figure 1) performs calculations by reflecting parameters in an equation (group of equations) formulated based on a physical model. This calculation includes structural analysis using the finite element method (FEM) and the like.

Here, the physical quantity measuring unit 12 (Fig. 1) acquires the physical quantities actually measured by each device 21 as actual measurement data. This physical quantity measuring unit 12 acquires the physical quantities detected by the sensor 2 (Fig. 1) as time-series data. For example, as shown in Fig. 3, time-series data including physical quantities indicating current, magnetic flux, acceleration, angular velocity, displacement, pressure, etc. is acquired as actual measurement data from each device 21.

In this embodiment, the physical quantity measuring unit 12 uses the sensor 2 to acquire physical quantities in real time while the device 20 is in operation, but other configurations are also possible. For example, during regular inspection, the sensor 2 may be attached to the device 21 to acquire and record physical quantities, and the physical quantity measuring unit 12 may acquire the records at a later date.

In other words, the physical quantity measuring unit 12 acquires, as actual measurement data, at least one of the physical quantities detected by each sensor 2 and the physical quantities contained in the test data or inspection data obtained when a test or inspection was previously performed on the device 21. In this way, by acquiring at least one of the current physical quantity and the past physical quantity, it is possible to determine whether or not there is an abnormality in the device 20.

The test data or inspection data also includes information indicating whether the device 21 has failed. In other words, the physical quantity in this embodiment includes information indicating whether the device 21 has failed in the past. The physical quantity also includes information indicating the date, time, location, and condition of the past failure.

It is preferable that the data acquired by the physical quantity measuring unit 12 acquires multiple types of physical quantities. For example, since vibration is affected by the support structure characteristics of the device 21 or the installation state of the sensor, it is difficult to determine from vibration data alone whether the change in physical quantity is due to an abnormality. In such cases, a comprehensive judgment can be made from physical quantities such as current in addition to vibration.

The actual measurement data processing unit 13 (FIG. 1) converts the actual measurement data acquired by the physical quantity measuring unit 12 into analysis data in a format compatible with the calculations of the analysis unit 10. Here, the calculations performed by the analysis unit 10 include calculations for outputting the analysis data in the same format as the data output from the actual measurement data processing unit 13.

For example, as shown in FIG. 3, when the actual measurement data processing unit 13 outputs vibration spectrum data, the corresponding calculation results of the analysis unit 10 are also subjected to a Fourier transform (Fast Fourier Transform: FFT) calculation to output the vibration spectrum data. Here, the time-series data as the actual measurement data is converted into data for analysis. In this way, the actual measurement data can be converted into a format optimal for calculation, allowing the analysis unit 10 to perform calculations at high speed.

The measured data processing unit 13 converts the measured data, such as vibration, current, voltage, and magnetic flux, acquired by the physical quantity measuring unit 12 into spectrum data by using a Fourier transform. It also performs filtering as necessary. It also performs processing to convert time series data, such as temperature and pressure, into maximum values, minimum values, average values, effective values, and so on, as necessary. Through these processing steps, it is possible to extract the features of the measured data and remove unnecessary noise, etc. It also performs processing for errors and variations that occur due to measurement. For example, since there are measurement errors or statistical errors from measuring instruments, processing is performed for these.

The parameter identification unit 14 (Figure 1) identifies actual measurement parameters, which are parameters when the calculation results of the analysis unit 10 reproduce the actual measurement data, based on the teacher data learned by the learning unit 11. In this embodiment, analysis data converted from the actual measurement data is used. In other words, the parameters are identified based on the teacher data learned by the learning unit 11 so that the calculation results of the analysis unit 10 reproduce the analysis data. In other words, the analysis unit 10 performs analysis using a physical model so that the analysis data derived as a result of the reproduction matches the actual measurement data.

For example, if the operation of device 20 in an abnormal state is reproduced in a physical model, the parameters at the time of the abnormality can be obtained. In this way, the parameters at the time of the abnormality can be used to determine whether or not there is an abnormality in the actual device 20.

The analysis unit 10 also performs calculations reflecting the parameters identified by the parameter identification unit 14. For example, it obtains calculation results related to the load acting on each of the devices 21 constituting the device 20 or the time change in the position of the rotating body 21C. It also evaluates the numerical error that occurs in the calculations.

The parameter identification unit 14 also infers the optimal combination of parameters that will allow the physical model of the device 20 to reproduce the analysis data output from the actual measurement data processing unit 13, and outputs these as actual measurement parameters. At this time, the estimation is performed taking into account errors and variability.

In the device diagnostic system 1 of this embodiment, the parameter identification unit 14 uses, for example, a trained model generated by a neural network to determine actual measurement parameters that reproduce the analysis data output from the actual measurement data processing unit 13.

In the present embodiment, the parameters include, for example, in the case of the electric motor 21B, parameters related to the dimensions and shape of the electric motor 21B, the number of coil turns or the number of slots, etc. The parameters also include the characteristics of the components, such as electrical resistance. The parameters also include the operating conditions or the surrounding environment of the device 20. These parameters may be obtained based on the specifications of each device 21 constituting the device 20, may be obtained by actual testing, or may be obtained by simulation.

The learning unit 11 (Figure 1) learns each parameter corresponding to the calculation result of the analysis unit 10 as teacher data. This learning unit 11 learns each parameter and the calculation result corresponding to these parameters when the analysis unit 10 previously performs a calculation corresponding to a combination of multiple parameters as teacher data. In this way, the accuracy of learning can be improved.

As shown in FIG. 6, the learning unit 11 generates a learning model by machine learning using the results of calculations performed in advance under various parameter conditions in the analysis unit 10 as training data. For example, there is a learning model generated by machine learning using a neural network. Here, the learning unit 11 creates a learning model using the parameters used in the calculations performed by the analysis unit 10 and the calculation results as teacher data.

The device diagnosis system 1 of this embodiment includes a computer equipped with an algorithm that performs machine learning. In other words, it includes a computer equipped with artificial intelligence (AI) that performs machine learning. For example, the system may be configured with one computer equipped with a neural network, or may be configured with multiple computers equipped with neural networks.

The device diagnostic system 1 may also include a deep learning unit that extracts a specific pattern from multiple patterns based on deep learning.

Analysis techniques based on artificial intelligence learning can be used for the computer-based analysis of this embodiment. For example, learning models generated by machine learning using neural networks, learning models generated by other machine learning, deep learning algorithms, regression analysis, and other mathematical algorithms can be used. In addition, forms of machine learning include clustering, deep learning, and other forms.

Here, a neural network is a mathematical model that represents the characteristics of brain functions through computer simulation. For example, this model shows that artificial neurons (nodes) that form a network through synaptic connections change the strength of synaptic connections through learning, and acquire problem-solving abilities. Furthermore, neural networks acquire problem-solving abilities through deep learning.

For example, a neural network is provided with an intermediate layer having six layers. Each intermediate layer is made up of 300 units. Furthermore, by having the multi-layered neural network learn in advance using training data, it is possible to automatically extract features from patterns of changes in the state of a circuit or system. Note that for the multi-layered neural network, the number of intermediate layers, number of units, learning rate, number of times of training, and activation function can be set on the user interface.

In addition, deep reinforcement learning may be used in the neural network, in which a reward function is set for each information item to be learned, and the information item with the highest value is extracted based on the reward function.

For example, we use a CNN (Convolution Neural Network), which has a proven track record in image recognition. In this CNN, the intermediate layer is composed of a convolution layer and a pooling layer. The convolution layer obtains a feature map by applying filtering to nearby nodes in the previous layer. The pooling layer further reduces the feature map output from the convolution layer to create a new feature map. At this time, by obtaining the maximum value of the pixels contained in the area of interest in the feature map, it is possible to absorb slight deviations in the position of the feature amount.

The convolutional layer extracts local features from an image, and the pooling layer consolidates local features. These processes reduce the size of the input image while maintaining its features. In other words, CNN can significantly compress (abstract) the amount of information contained in an image. Then, the input image can be recognized and classified using the abstracted image image stored in the neural network.

Note that there are various methods for deep learning, such as autoencoder, recurrent neural network (RNN), long short-term memory (LSTM), and generative adversarial network (GAN). These methods may be applied to the deep learning of this embodiment.

The remaining life evaluation unit 17 (FIG. 1) calculates the remaining life of each device 21 based on the calculation results of the analysis unit 10 when the parameters identified by the actual measurement data by the parameter identification unit 14 are reflected. In this way, the remaining life of each device 21 can be calculated based on the actual measurement data.

The remaining life evaluation unit 17 evaluates the remaining life from the results of determining at least one of the load, stress, voltage, current, and temperature related to each device 21 obtained by the analysis unit 10 when reflecting the actual measurement parameters identified by the parameter identification unit 14. In this way, it is possible to evaluate the remaining life taking into account the load or stress applied to the device 21.

For example, the load and stress of each piece of equipment 21 that makes up the device 20 are calculated to determine the remaining life from the life evaluation formula and material properties. In this embodiment, it is also possible to determine the remaining life from not only the load and stress, but also the deterioration of insulating materials due to changes in voltage or current over time, and material deterioration due to thermal cycles (changes in temperature over time).

The life evaluation formula is, for example, the basic rated life L10 when a radial load P acts on the rolling bearing supporting the rotor 21C. This radial load P is calculated by the analysis unit 10 using the following formula based on a physical model.

L10=(C/P) ^p

Here, C is the basic dynamic load rating, which indicates the dynamic load capacity of a rolling bearing. For example, it is a constant load that gives a basic rated life of L10 of 1 million revolutions. Also, p is an index that changes depending on the type of rolling bearing. For example, for ball bearings, p = 3. For roller bearings, p = 10/3.

The remaining life of the fluctuating stresses obtained by the analysis unit 10 may be evaluated using, for example, Miner's rule (modified Miner's rule), which determines the remaining life from material properties. In this case, the stress frequency distribution of the actual stress can be obtained using the rainflow method or the like.

The remaining life assessment method using Miner's rule will be explained with reference to the graphs in Figures 7 and 8. Note that the solid line graph G1 in Figure 8 is the fatigue limit curve (material properties). The dashed line graph G2 in Figure 8 is the case where the fatigue limit curve is extended using the modified Miner's rule.

Here, let Ni be the number of repetitions at which a repeated stress σi of a certain stress amplitude causes fracture in the S-N curve of the target material. For example, suppose that a given object is subjected to k repeated stresses σi (i = 1 to k) of different amplitudes each time ni (i = 1 to k) times. At this time, the fatigue damage D accumulated in this object is given by the following formula. Here, the life is defined as when D reaches 1.

The remaining life assessment method will be explained with reference to the graph in Figure 9. In this graph, the vertical axis is fatigue damage D and the horizontal axis is the number of cycles.

The range indicated by dashed graph G3 in Figure 9 shows the results of evaluating the remaining lifespan at the start of operation of the device 20. As shown in this dashed graph, the load evaluation by simulation takes into account parameter variations and calculation errors, so the calculated lifespan is a statistical distribution. For this reason, the lifespan of the equipment 21 is determined from the failure probability. The reference failure probability is set for each piece of equipment 21.

The range indicated by the solid line graph G4 in Figure 9 shows the remaining life evaluation result after operating the device 20 for a specified period of time. If the state of the equipment 21 changes during operation, the remaining life evaluation result will change accordingly. Such an evaluation is preferably performed in real time during the operation period, but may also be performed periodically depending on the method of operation.

The state determination unit 15 (FIG. 1) of this embodiment determines whether or not there is an abnormality in the device 20 based on each of the actual parameters identified by the parameter identification unit 14. Here, the state determination unit 15 determines whether or not there is an abnormality in the device 20 by comparing the remaining life calculated by the remaining life evaluation unit 17 with the pre-planned operating time. In this way, it is possible to evaluate whether or not an abnormality will occur in the device 20 during the pre-planned operating period. For example, the presence or absence of an abnormality is evaluated by comparing the remaining life of each device 21 with the planned operating time.

The state determination unit 15 also determines whether or not there is an abnormality in the device 20 by comparing the actual parameters identified by the parameter identification unit 14 with an arbitrarily set allowable range for the parameters. In this way, the process of determining whether or not there is an abnormality in the device 20 can be easily performed using the arbitrarily set allowable range for the parameters.

The state determination unit 15 also determines whether or not there is an abnormality in the device 20 by comparing the actual measurement data acquired by the physical quantity measurement unit 12 with an arbitrarily set data tolerance range. In this way, the process of determining whether or not there is an abnormality in the device 20 can be easily performed using the arbitrarily set data tolerance range.

For example, the state determination unit 15 determines whether or not there is an abnormality by comparing the actual measurement data acquired by the physical quantity measurement unit 12 with the data tolerance range. Also, the state determination unit 15 determines whether or not there is an abnormality by comparing the analysis data output from the data processing unit with the tolerance range.

The parameter tolerance ranges and data tolerance ranges used for judgment by the state judgment unit 15 are set in advance. These tolerance ranges may be arbitrarily set in advance by the user. In addition, all of these tolerance ranges may be the same for all devices 20, or may be different for each individual device 20.

These tolerances may be set based on actual measurement data, based on specifications regarding the behavior of the device 20, based on actual test results, based on simulation results, or based on a combination of these.

The abnormality identification unit 16 (Figure 1) identifies the degree and cause of the abnormality when the state determination unit 15 determines that the device 20 is abnormal. In this way, the degree and cause of the abnormality can be identified based on the actual measurement data. In addition, the degree of the abnormality can be evaluated from the combination of the actual measurement parameters and the magnitude of their values.

When the status determination unit 15 determines that the device 20 is normal, the inspection estimation unit 18 (Fig. 1) estimates the progression of the abnormality from the combination and magnitude of the measured parameters, and estimates the next inspection location and inspection time from the remaining life of the equipment 21 calculated by the remaining life evaluation unit 17. In this way, the inspection location and inspection time can be automatically presented to the user.

The main control unit 8 controls the output unit 5 to output the evaluation results of the device diagnosis system 1. For example, it outputs the results derived by the anomaly identification unit 16 and the inspection estimation unit 18.

Next, the device diagnosis method (processing) executed by the device diagnosis system 1 of this embodiment will be described with reference to the flowchart in FIG. 10. The aforementioned drawings will be referred to as appropriate. The following steps are at least a part of the processing included in the device diagnosis method, and other steps may be included in the device diagnosis method.

First, in step S1, the model construction unit 9 constructs a physical model that reproduces the operation of the device 20, which is composed of multiple devices 21 including at least a rotating electric machine, and reproduces the state of the devices 21 that arise in relation to the operation.

In the next step S2, the analysis unit 10 performs calculations by incorporating the parameters into the equation formulated based on the physical model.

In the next step S3, the learning unit 11 learns the calculation results of the analysis unit 10 and the corresponding parameters as teacher data.

In the next step S4, the physical quantity measuring unit 12 acquires the physical quantities actually measured by each device 21 as actual measurement data.

In the next step S5, the actual measurement data processing unit 13 converts the actual measurement data acquired by the physical quantity measuring unit 12 into analysis data in a format compatible with the calculations of the analysis unit 10.

In the next step S6, the parameter identification unit 14 identifies actual measurement parameters, which are parameters when the calculation results of the analysis unit 10 reproduce the analysis data (actual measurement data), based on the teacher data learned by the learning unit 11.

In the next step S7, the state determination unit 15 determines whether or not there is currently an abnormality in the device 20 based on each of the actual measurement parameters identified by the parameter identification unit 14. If there is no abnormality (NO in step S7), the process proceeds to step S8. On the other hand, if there is an abnormality (YES in step S7), the process proceeds to step S10.

In step S8, which is reached if step S7 is negative, the remaining life assessment unit 17 calculates the remaining life of each piece of equipment 21 based on the calculation results of the analysis unit 10 when the analysis data (actual measurement data) is updated to reflect the parameters identified by the parameter identification unit 14.

In the next step S9, the inspection estimation unit 18 estimates the progression of the abnormality from the combination and magnitude of the measured parameters, and estimates the next inspection location and inspection time from the remaining life of the equipment 21 calculated by the remaining life evaluation unit 17. The main control unit 8 controls the output unit 5 to output the evaluation result of the equipment diagnosis system 1. Then, the equipment diagnosis method is terminated.

In step S10, which is reached if step S7 is YES, the abnormality identification unit 16 identifies the degree and cause of the abnormality if the state determination unit 15 determines that the device 20 is abnormal. The main control unit 8 controls the output unit 5 to output the evaluation result of the device diagnosis system 1. Then, the device diagnosis method ends.

In the flowchart of the above embodiment, the steps are executed in series, but the order of steps is not necessarily fixed, and some steps may be executed in reverse. Some steps may be executed in parallel with other steps.

The system of the above-mentioned embodiment includes a control device with a highly integrated processor such as a dedicated chip, FPGA (Field Programmable Gate Array), GPU (Graphics Processing Unit), or CPU (Central Processing Unit), a storage device such as ROM (Read Only Memory) or RAM (Random Access Memory), an external storage device such as HDD (Hard Disk Drive) or SSD (Solid State Drive), a display device such as a display, an input device such as a mouse or keyboard, and a communication interface. This system can be realized with a hardware configuration using a normal computer.

The program executed by the system of the above-mentioned embodiment is provided in advance in a ROM or the like. Alternatively, the program may be provided in the form of an installable or executable file stored on a non-transitory computer-readable storage medium such as a CD-ROM, CD-R, memory card, DVD, or flexible disk (FD).

The programs executed by this system may also be stored on a computer connected to a network such as the Internet and provided by downloading them via the network. This system may also be configured by combining separate modules that independently perform the functions of the components and connect them together via a network or dedicated lines.

According to the embodiment described above, by providing a model construction unit that reproduces the operation of a device composed of multiple devices including at least a rotating electric machine and constructs a physical model that reproduces the state of the devices that occurs in relation to the operation, it is possible to evaluate abnormalities from the actual measurement data of the device, even if the device has never experienced an abnormality in the past.

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations can be made without departing from the spirit of the invention. These embodiments or modifications thereof are within the scope of the invention and its equivalents as set forth in the claims, as well as the scope and spirit of the invention.

1...equipment diagnostic system, 2...sensor, 3...analysis computer, 4...input section, 5...output section, 6...communication section, 7...storage section, 8...main control section, 9...model construction section, 10...analysis section, 11...learning section, 12...physical quantity measurement section, 13...measurement data processing section, 14...parameter identification section, 15...state determination section, 16...abnormality identification section, 17...remaining life evaluation section, 18...inspection estimation section, 20...apparatus, 21...equipment, 21A...control panel, 21B...electric motor, 21C...rotating body, 21D...group of equipment on the load side.

Claims (14)

a model construction unit that reproduces the operation of an apparatus including a plurality of devices including at least a rotating electric machine and constructs a physical model that reproduces the state of the devices that occurs in relation to the operation;
an analysis unit that performs calculations by reflecting parameters in an equation formulated based on the physical model;
a learning unit configured to learn each of the parameters corresponding to the calculation results of the analysis unit as teacher data;
a physical quantity measuring unit that acquires physical quantities actually measured by each of the devices as actual measurement data;
a parameter identification unit that identifies actual measurement parameters, which are the parameters when the calculation result of the analysis unit reproduces the actual measurement data, based on the teacher data learned by the learning unit;
a state determination unit that determines whether or not there is an abnormality in the device based on each of the actual measurement parameters identified by the parameter identification unit;
Equipped with
Equipment diagnostic system. a remaining life evaluation unit that calculates a remaining life of each of the devices based on the calculation result of the analysis unit when the parameters identified by the actual measurement data by the parameter identification unit are reflected,
The device diagnostic system of claim 1 . the state determination unit determines whether or not there is an abnormality in the device by comparing the remaining life calculated by the remaining life evaluation unit with a pre-planned operation time.
The device diagnostic system of claim 2 . the remaining life evaluation unit evaluates the remaining life from a result of determining at least one of a load, a stress, a voltage, a current, and a temperature related to each of the devices obtained by the analysis unit when the actual measurement parameters identified by the parameter identification unit are reflected;
The device diagnostic system according to claim 2 or 3. and an inspection estimation unit that, when the state determination unit determines that the device is normal, estimates a degree of progress of an abnormality from a combination and magnitude of the actual measurement parameters, and estimates a next inspection location and inspection time from the remaining life of the equipment calculated by the remaining life evaluation unit.
The device diagnostic system according to any one of claims 2 to 4. and an abnormality identification unit that identifies the degree and cause of the abnormality when the state determination unit determines that the device is abnormal.
The device diagnostic system according to any one of claims 1 to 5. a measurement data processing unit that converts the measurement data acquired by the physical quantity measuring unit into analysis data in a format compatible with the calculation by the analysis unit,
The device diagnostic system according to any one of claims 1 to 6. The learning unit learns, as the teacher data, each of the parameters and the calculation results corresponding to the parameters when calculations corresponding to combinations of the parameters are performed in advance by the analysis unit.
The device diagnostic system according to any one of claims 1 to 7. The physical model is
A dynamic model representing the behavior of a dynamic system related to at least one of rotational motion and translational motion of each of the devices;
A control model for a control unit constituting at least one of the devices to control the behavior of the entire device;
an electrical model representing the behavior of an electrical system of the device;
a magnetic model representing the behavior of the magnetic system of the device;
a thermal model describing the thermal behavior of the device;
A fluid model representing at least one of the behaviors of a flow rate and a pressure of a fluid flowing through a pipe connected to a pump constituting at least one of the devices;
A material model representing material properties of each of the devices;
A structural model having information on at least one of dimensions and shape of each of the devices;
At least one of
Reproducing the behavior of the device by a combination of the parameters;
The device diagnostic system according to any one of claims 1 to 8. the state determination unit determines the presence or absence of an abnormality in the device by comparing the actual measurement parameter identified by the parameter identification unit with an arbitrarily set allowable range for the parameter.
The device diagnostic system according to any one of claims 1 to 9. the state determination unit determines the presence or absence of an abnormality in the device by comparing the actual measurement data acquired by the physical quantity measurement unit with an arbitrarily set data allowable range.
The device diagnostic system according to any one of claims 1 to 10. A sensor is provided on each of the devices,
The physical quantity measuring unit includes:
The physical quantities detected by the respective sensors; and
The physical quantity included in test data or inspection data obtained when a test or inspection was previously performed on the device; and
At least one of the above is acquired as the actual measurement data.
The device diagnostic system according to any one of claims 1 to 11. The sensor detects at least one of vibration, current, voltage, magnetic flux, temperature, pressure, and rotational torque as the physical quantity.
13. The device diagnostic system of claim 12. a model construction unit constructing a physical model that reproduces an operation of an apparatus including a plurality of devices including at least a rotating electric machine and reproduces a state of the devices that occurs in association with the operation;
an analysis unit performing a calculation by reflecting parameters in an equation formulated based on the physical model;
A learning unit learns each of the parameters corresponding to the calculation results of the analysis unit as teacher data;
A physical quantity measuring unit acquires physical quantities actually measured by each of the devices as actual measurement data;
a parameter identification unit identifying an actual measurement parameter, which is the parameter when the calculation result of the analysis unit reproduces the actual measurement data, based on the teacher data learned by the learning unit;
a state determination unit determining whether or not an abnormality exists in the device based on each of the actual measurement parameters identified by the parameter identification unit;
Including,
Device diagnostic methods.

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