CN115730927A - Manufacturing equipment fault predictive maintenance method and system based on Internet of things platform - Google Patents
Manufacturing equipment fault predictive maintenance method and system based on Internet of things platform Download PDFInfo
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- CN115730927A CN115730927A CN202211591373.1A CN202211591373A CN115730927A CN 115730927 A CN115730927 A CN 115730927A CN 202211591373 A CN202211591373 A CN 202211591373A CN 115730927 A CN115730927 A CN 115730927A
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Abstract
The invention belongs to the technical field of industrial equipment fault monitoring, and particularly relates to a manufacturing equipment fault predictive maintenance method and system based on an Internet of things platform, wherein the method comprises the following steps: s1, creating an object model of equipment based on the characteristics of the equipment; s2, setting an independent forest algorithm and learning abnormal values based on historical data of parameters of the equipment model; s3, acquiring real-time state data corresponding to the equipment based on parameters of the object model of the equipment; s4, monitoring abnormal values of the real-time state data of the equipment through an independent forest algorithm; s5, when an abnormal value is monitored, sending abnormal early warning information to a maintenance end through a physical network platform; s6, receiving abnormal feedback information of a maintenance end through an Internet of things platform and optimizing an independent forest algorithm; and S7, monitoring abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm. The invention can accurately discover the fault in advance before the equipment possibly fails on the premise of ensuring the applicability.
Description
Technical Field
The invention belongs to the technical field of industrial equipment fault monitoring, and particularly relates to a manufacturing equipment fault predictive maintenance method and system based on an Internet of things platform.
Background
In the traditional manufacturing industry, the management of factory equipment, inspection of maintenance points and other work are mostly performed by manual leading, and the maintenance of the equipment is also mostly performed in a post-repair mode. Due to the maintenance mode, the influence of equipment damage is large, the repair cycle is long, and the industrial production progress can be seriously influenced by sudden equipment failure.
Therefore, there is a need for a method for manufacturing equipment that can monitor equipment failure and detect the equipment failure in advance before the equipment may fail, so as to prompt maintenance personnel to perform predictive maintenance on the equipment in time. Because of predictive maintenance, the method is very beneficial to improving the maintenance value of the industrial equipment and improving the running stability of the manufacturing equipment. However, the working conditions of industrial equipment are often complicated, the model to be monitored is created by means of the experience of the maintenance personnel, the time and the energy consumption are high, and the stability and the effectiveness are difficult to ensure; moreover, even if the industrial devices are of the same model, the precursors (i.e., early warning conditions) of the failures of the industrial devices are different under different use environments, which further increases the difficulty of prediction. Put another way, the existing method for early warning type maintenance is time-consuming and labor-consuming, and has poor accuracy, stability and applicability. Therefore, at present, the equipment maintenance in the manufacturing industry is still generally performed by adopting a manual regular maintenance detection mode.
In conclusion, how to accurately find the equipment in advance before the equipment possibly fails on the premise of ensuring the applicability and remind maintenance personnel to perform predictive maintenance in time is a problem to be solved urgently at present, so that the maintenance value of industrial equipment is improved, and the operation stability of manufacturing equipment is improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the manufacturing equipment fault predictive maintenance method and system based on the Internet of things platform, which can accurately discover equipment in advance before possible faults and remind maintenance personnel to perform predictive maintenance in time on the premise of ensuring the applicability, thereby improving the maintenance value of industrial equipment and improving the running stability of manufacturing equipment.
In order to solve the technical problem, the invention adopts the following technical scheme:
the manufacturing equipment fault predictive maintenance method based on the Internet of things platform comprises the following steps:
s1, accessing equipment to an Internet of things platform, and creating an object model of the equipment based on the characteristics of the equipment;
s2, setting an independent forest algorithm on the Internet of things platform, and learning abnormal values of the independent forest algorithm based on historical data of parameters of the equipment object model;
s3, acquiring real-time state data corresponding to the equipment based on the parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things;
s4, monitoring abnormal values of the real-time state data of the equipment through an independent forest algorithm;
s5, when an abnormal value is monitored, sending abnormal early warning information to a maintenance end through a physical network platform;
s6, receiving abnormal feedback information of the maintenance end through the Internet of things platform, and optimizing the independent forest algorithm based on the abnormal feedback information;
and S7, monitoring abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm, and returning to S5 when the abnormal values are monitored.
Preferably, in S1, when accessing the device to the platform of the internet of things, if the device supports direct connection, the device is connected to the platform of the internet of things through the MQTT, and if the device does not support direct connection, the device is accessed to the platform of the internet of things through the gateway.
Preferably, in S1, creating an object model of the device based on the characteristics of the device includes: and abstracting the attribute and the characteristic of the equipment and then creating a corresponding object model.
Preferably, in S5, the content of the abnormality warning information includes information of a device in which the abnormality occurs and specific content of the abnormality.
Preferably, in S6, the abnormal feedback information includes an actual representation of the equipment, a reason for causing an abnormal value early warning of the equipment, and a preventive maintenance measure taken.
The invention also provides a manufacturing equipment fault predictive maintenance system based on the Internet of things platform, which is used for the manufacturing equipment fault predictive maintenance method based on the Internet of things platform, and comprises the Internet of things platform, and an acquisition end and a maintenance end which are respectively communicated with the Internet of things platform;
the Internet of things platform is used for accessing equipment, abstracting the attribute and the characteristic of the equipment and then establishing a corresponding object model; the Internet of things platform is also used for setting an independent forest algorithm and learning abnormal values of the independent forest algorithm based on historical data of parameters of the equipment model;
the acquisition end is used for acquiring real-time state data corresponding to the equipment based on parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things; the Internet of things platform is also used for monitoring abnormal values of real-time state data of the equipment by using an independent forest algorithm and sending abnormal early warning information to the maintenance end through the physical network platform when the abnormal values are monitored;
the maintenance end is used for sending out an early warning signal after receiving the abnormal early warning information; the maintenance end is also used for inputting corresponding abnormal feedback information and feeding the abnormal feedback information back to the Internet of things platform; the Internet of things platform is further used for optimizing the independent forest algorithm based on the abnormal feedback information and monitoring abnormal values of real-time state data of the equipment through the optimized independent forest algorithm.
Preferably, when the internet of things platform is used for accessing the device, if the device supports direct connection, the device is connected to the internet of things platform through MQTT, and if the device does not support direct connection, the device is accessed to the internet of things platform through the gateway.
Compared with the prior art, the invention has the following beneficial effects:
1. firstly, recording equipment to be monitored into an Internet of things platform and establishing a corresponding object model; and then, setting an independent forest algorithm on the platform of the Internet of things, and performing abnormal value learning on the independent forest algorithm based on the historical data of the parameters of the equipment model. The initial independent Sensors algorithm obtained in the way can perform predictive identification on most faults of the corresponding equipment of the object model.
Then, acquiring real-time state data corresponding to the equipment based on the parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things; and monitoring abnormal values through an initial independent forest algorithm. Due to the characteristics of industrial equipment, the initial independent forest algorithm may still have the situations of missed judgment and misjudgment for certain equipment. Based on the situation, the maintainable end is arranged, after the maintainer passes the abnormal early warning information, the actual situation of the equipment can be input into the maintenance end in the abnormal feedback information mode and sent to the Internet of things platform, and besides, for the condition of missed judgment (the condition of missed judgment rarely occurs because the judgment logic of the independent forest algorithm is abnormal value judgment), the maintainer can also input the specific situation into the maintenance end in the abnormal feedback information mode and send the specific situation to the Internet of things platform.
And then, the Internet of things platform optimizes the independent forest algorithm based on the abnormal feedback information, and monitors abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm. Through the mode, for each monitored device, the Internet of things platform predicts the abnormality more and more accurately, the actual abnormality feedback information of each device is based on the optimization of the independent forest algorithm, the optimization has the characteristic of customization, and the prediction of the device is more accurate as the service time is longer. Meanwhile, based on the characteristics of the method, even if the initial independent forest algorithm is not perfect, the subsequent effectiveness of the initial independent forest algorithm is not influenced. This has two additional effects: firstly, the universality of an initial independent forest algorithm is stronger, equipment with similar working conditions can even be directly used, and the problem of poor applicability of the prior art can be effectively solved; secondly, due to the support of subsequent optimization, the accuracy of the initial independent forest algorithm is not very strict, and the direct advantage brought by the method is that workers can complete the learning of the initial independent forest algorithm quickly and the accuracy of long-term detection due to expiration is not influenced, so that the problems of time consumption and labor consumption in the prior art can be solved.
In conclusion, the invention can accurately find the equipment in advance before the equipment possibly fails on the premise of ensuring the applicability, and remind the maintenance personnel to perform predictive maintenance in time, thereby improving the maintenance value of the industrial equipment and the running stability of the manufacturing equipment.
2. Based on the object model, the invention can realize the real-time data of the visual equipment, and is convenient for analysis and judgment by combining with the actual situation when the staff carries out maintenance.
3. In the invention, the maintenance end can send out the early warning signal after receiving the abnormal early warning information, thereby ensuring that maintenance personnel can know the situation in time and carry out corresponding processing in time, and further ensuring the timeliness of predictive maintenance.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a flow diagram of a method for predictive maintenance of failure of manufacturing equipment based on an Internet of things platform in an embodiment;
FIG. 2 is a logical block diagram of an Internet of things platform based manufacturing equipment failure predictive maintenance system in an embodiment;
fig. 3 is a flowchart of a specific example in the embodiment.
Detailed Description
The following is further detailed by way of specific embodiments:
example (b):
as shown in fig. 1, the embodiment discloses a manufacturing equipment failure predictive maintenance method based on an internet of things platform, which includes the following steps:
s1, accessing the equipment to an Internet of things platform, and creating an object model of the equipment based on the characteristics of the equipment. During specific implementation, when the equipment is accessed to the Internet of things platform, if the equipment supports direct connection, the equipment is connected to the Internet of things platform through the MQTT, and if the equipment does not support direct connection, the equipment is accessed to the Internet of things platform through the gateway. Creating an object model of the device based on the characteristics of the device includes: and creating products and equipment on the platform of the Internet of things, abstracting the attributes and characteristics of the equipment, and creating a corresponding object model on the platform of the Internet of things.
The object model is a digital representation of an entity (such as a sensor, a vehicle-mounted device, a building, a factory and the like) in a physical space in a cloud end, and describes what the entity is, what can be done and what information can be provided externally according to three dimensions of attributes, services and events. The three dimensions of the object model are defined, namely the definition of the product function is completed.
S2, an independent forest algorithm (iForest algorithm) is set on the Internet of things platform, and abnormal value learning is conducted on the independent forest algorithm based on historical data of parameters of the equipment model.
The independent forest algorithm is an Ensemble-based rapid anomaly detection method, has linear time complexity and high accuracy, and is a state-of-the-art algorithm which meets the requirement of big data processing. The iForest is applied to anomaly detection of continuous data, and an anomaly is defined as "outlier that is easily isolated", which can be understood as a point that is sparsely distributed and is far from a population with high density. Statistically, in the data space, the sparsely distributed regions indicate that the probability of data occurring in the regions is low, and thus the data falling in these regions can be considered abnormal. The iForest does not define a mathematical model nor requires labeled training. For how to find which points are easily isolated, iForest uses a very efficient set of strategies. Assuming we cut the data space with one random hyperplane, two subspaces can be generated by cutting once. We then continue to cut each subspace with a random hyperplane, looping on until there is only one data point inside each subspace.
And S3, acquiring real-time state data corresponding to the equipment based on the parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things.
S4, monitoring abnormal values of the real-time state data of the equipment through an independent forest algorithm;
and S5, when the abnormal value is monitored, sending abnormal early warning information to the maintenance end through the physical network platform. The content of the abnormal early warning information comprises information of equipment with the abnormality and specific content of the abnormality.
S6, receiving abnormal feedback information of the maintenance end through the Internet of things platform, and optimizing the independent forest algorithm based on the abnormal feedback information; wherein the abnormal feedback information comprises the actual representation of the equipment, the reason for causing the early warning of the abnormal value of the equipment and preventive maintenance measures taken.
And S7, monitoring abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm, and returning to S5 when the abnormal values are monitored.
As shown in fig. 2, the invention further provides a manufacturing equipment failure predictive maintenance system based on the internet of things platform, which is used in the manufacturing equipment failure predictive maintenance method based on the internet of things platform, and the manufacturing equipment failure predictive maintenance system comprises the internet of things platform, and a collection end and a maintenance end which are respectively communicated with the internet of things platform. In this embodiment, the maintenance end is a smartphone that loads a corresponding APP.
The Internet of things platform is used for accessing equipment, abstracting the attribute and the characteristic of the equipment and then establishing a corresponding object model; the Internet of things platform is also used for setting an independent forest algorithm and learning abnormal values of the independent forest algorithm based on historical data of parameters of the equipment model;
the acquisition end is used for acquiring real-time state data corresponding to equipment based on parameters of an object model of the equipment and transmitting the real-time state data to the Internet of things; the Internet of things platform is also used for monitoring abnormal values of real-time state data of the equipment by using an independent forest algorithm and sending abnormal early warning information to the maintenance end through the physical network platform when the abnormal values are monitored;
the maintenance end is used for sending out an early warning signal after receiving the abnormal early warning information; the maintenance end is also used for inputting corresponding abnormal feedback information and feeding the abnormal feedback information back to the Internet of things platform; the Internet of things platform is further used for optimizing the independent forest algorithm based on the abnormal feedback information and monitoring abnormal values of real-time state data of the equipment through the optimized independent forest algorithm.
To facilitate a better working of the invention by a person skilled in the art, a specific example is described below. It should be noted that the detected device in this example is a rotating electrical machine device, and meanwhile, in order to more clearly understand the specific process of the method, part of the work of the maintenance personnel is also described. As shown in fig. 3, the details are as follows:
the method comprises the steps that a rotary motor device monitoring calculator product and a device are established on an internet of things platform, a device connection platform mode is defined as a direct connection device, a communication protocol is MQTT, and a device networking mode is WIFI; creating a product model, defining product temperature, vibration and noise attributes, defining attribute identifiers, data types and data value ranges;
completing burning of connection parameters of the equipment end of the rotating motor monitoring calculator, enabling the equipment to be connected to the Internet of things platform and capable of carrying out data communication with the Internet of things platform; and setting an independent forest algorithm on the platform of the Internet of things, and performing abnormal value learning on the independent forest algorithm based on the historical data of the parameters of the equipment model.
Collecting data of the motor equipment based on parameters of an object model of the equipment and sending the data to an Internet of things platform, and discovering that the equipment reports abnormal values of temperature data in real time through unsupervised learning of an independent forest algorithm; forwarding the abnormal temperature value of the motor equipment discovered by the platform to equipment management application of a maintenance end through a rule forwarding engine; the equipment management application carries out early warning on abnormal temperature values of the motor equipment and informs maintenance personnel;
after receiving the early warning of the abnormal temperature value of the motor equipment, maintenance personnel check the equipment on site, judge whether the equipment fault prediction is accurate and whether corresponding equipment maintenance measures are taken, and record and submit the equipment fault prediction at an equipment management application mobile terminal; after receiving the feedback and the maintenance record filled by the maintenance personnel, the equipment management application at the maintenance end pushes the feedback to the Internet of things platform through the API, and the platform optimizes the abnormal data monitoring algorithm by referring to the feedback, so that the accuracy of the algorithm is continuously improved.
Firstly, recording equipment to be monitored into an Internet of things platform and establishing a corresponding object model; and then, setting an independent forest algorithm on the platform of the Internet of things, and performing abnormal value learning on the independent forest algorithm based on the historical data of the parameters of the equipment model. The initial independent Sensors algorithm obtained in the way can perform predictive identification on most faults of the corresponding equipment of the object model. Then, acquiring real-time state data corresponding to the equipment based on the parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things; and monitoring abnormal values through an initial independent forest algorithm. Due to the characteristics of industrial equipment, the initial independent forest algorithm may still have the situations of missed judgment and misjudgment for certain equipment. Based on the situation, the maintainable end is arranged, after the maintainer passes the abnormal early warning information, the actual situation of the equipment can be input into the maintainable end in an abnormal feedback information mode and sent to the Internet of things platform, and besides, for the condition of missed judgment (the condition of missed judgment rarely occurs because the judgment logic of the independent forest algorithm is abnormal value judgment), the maintainer can also input the specific situation into the maintainable end in an abnormal feedback information mode and send to the Internet of things platform after the maintenance.
And then, the Internet of things platform optimizes the independent forest algorithm based on the abnormal feedback information, and monitors abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm. Through the mode, for each monitored device, the Internet of things platform predicts the abnormality more and more accurately, the actual abnormality feedback information of each device is based on the optimization of the independent forest algorithm, the optimization has the characteristic of customization, and the prediction of the device is more accurate as the service time is longer. Meanwhile, based on the characteristics of the method, even if the initial independent forest algorithm is not perfect, the subsequent effectiveness of the initial independent forest algorithm is not influenced. This has two additional effects: firstly, the universality of an initial independent forest algorithm is strong, equipment with similar working conditions can even be directly used, and the problem of poor applicability of the prior art can be effectively solved; secondly, due to the support of subsequent optimization, the accuracy of the initial independent forest algorithm is not very harsh, and the direct advantage brought by the method is that the worker can complete the learning of the initial independent forest algorithm quickly, and the accuracy of long-term detection due to expiration is not influenced, so that the problems of time consumption and labor consumption in the prior art can be solved.
And based on the object model, the invention can realize the real-time data of the visual equipment, and is convenient for analysis and judgment by combining with the actual situation when the staff maintains. In addition, in the invention, the maintenance end can send out the early warning signal after receiving the abnormal early warning information, thereby ensuring that maintenance personnel can know the situation in time and carry out corresponding processing in time, and further ensuring the timeliness of predictive maintenance.
The invention can accurately find the equipment in advance before the equipment possibly fails on the premise of ensuring the applicability, and remind maintenance personnel to perform predictive maintenance in time, thereby improving the maintenance value of industrial equipment and the running stability of the manufacturing equipment.
It should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that the technical solutions of the present invention can be modified or substituted with equivalent solutions without departing from the spirit and scope of the technical solutions, and all should be covered in the claims of the present invention.
Claims (7)
1. The manufacturing equipment fault predictive maintenance method based on the Internet of things platform is characterized by comprising the following steps:
s1, accessing equipment to an Internet of things platform, and creating an object model of the equipment based on the characteristics of the equipment;
s2, setting an independent forest algorithm on the Internet of things platform, and learning abnormal values of the independent forest algorithm based on historical data of parameters of the equipment object model;
s3, acquiring real-time state data corresponding to the equipment based on the parameters of the object model of the equipment and transmitting the real-time state data to the Internet of things;
s4, monitoring abnormal values of the real-time state data of the equipment through an independent forest algorithm;
s5, when an abnormal value is monitored, sending abnormal early warning information to a maintenance end through a physical network platform;
s6, receiving abnormal feedback information of the maintenance end through the Internet of things platform, and optimizing an independent forest algorithm based on the abnormal feedback information;
and S7, monitoring abnormal values of the real-time state data of the equipment through the optimized independent forest algorithm, and returning to S5 when the abnormal values are monitored.
2. The internet of things platform-based manufacturing equipment failure predictive maintenance method of claim 1, wherein: in S1, when the equipment is accessed to the Internet of things platform, if the equipment supports direct connection, the equipment is connected to the Internet of things platform through MQTT, and if the equipment does not support direct connection, the equipment is accessed to the Internet of things platform through a gateway.
3. The internet of things platform-based manufacturing equipment failure predictive maintenance method of claim 2, wherein: in S1, creating an object model of the device based on the characteristics of the device includes: and abstracting the attribute and the characteristic of the equipment and then creating a corresponding object model.
4. The internet of things platform-based manufacturing equipment failure predictive maintenance method of claim 3, wherein: in S5, the content of the abnormality warning information includes information of the abnormal device and specific content of the abnormality.
5. The internet of things platform-based manufacturing equipment failure predictive maintenance method of claim 4, wherein: in S6, the abnormal feedback information includes actual representation of the device, a reason for causing early warning of an abnormal value of the device, and a preventive maintenance measure taken.
6. Manufacturing equipment failure predictive maintenance system based on thing networking platform, its characterized in that: the manufacturing equipment fault predictive maintenance method based on the Internet of things platform is applied to any one of claims 1 to 5, and comprises the Internet of things platform, and a collection end and a maintenance end which are respectively communicated with the Internet of things platform;
the Internet of things platform is used for accessing the equipment, abstracting the attribute and the characteristic of the equipment and then creating a corresponding object model; the Internet of things platform is also used for setting an independent forest algorithm and learning abnormal values of the independent forest algorithm based on historical data of parameters of the equipment model;
the acquisition end is used for acquiring real-time state data corresponding to equipment based on parameters of an object model of the equipment and transmitting the real-time state data to the Internet of things; the Internet of things platform is also used for monitoring abnormal values of real-time state data of the equipment by using an independent forest algorithm and sending abnormal early warning information to the maintenance end through the physical network platform when the abnormal values are monitored;
the maintenance end is used for sending out an early warning signal after receiving the abnormal early warning information; the maintenance end is also used for inputting corresponding abnormal feedback information and feeding the abnormal feedback information back to the Internet of things platform; the Internet of things platform is further used for optimizing the independent forest algorithm based on the abnormal feedback information and monitoring abnormal values of real-time state data of the equipment through the optimized independent forest algorithm.
7. The internet of things platform-based manufacturing equipment failure predictive maintenance system of claim 6, wherein: when the Internet of things platform is used for accessing the equipment, if the equipment supports direct connection, the equipment is connected to the Internet of things platform through the MQTT, and if the equipment does not support direct connection, the equipment is accessed to the Internet of things platform through the gateway.
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