CN115375039A

CN115375039A - Industrial equipment fault prediction method and device, electronic equipment and storage medium

Info

Publication number: CN115375039A
Application number: CN202211127600.5A
Authority: CN
Inventors: 佘靖雯; 何秀; 陈智超; 钱文洁; 陈灵珊; 郭会强; 吴春龙; 崔宁; 李卅
Original assignee: Shanghai Aircraft Manufacturing Co Ltd
Current assignee: Shanghai Aircraft Manufacturing Co Ltd
Priority date: 2022-09-16
Filing date: 2022-09-16
Publication date: 2022-11-22

Abstract

The invention discloses a method and a device for predicting industrial equipment faults, electronic equipment and a storage medium. The method comprises the following steps: acquiring fault data and steady-state data of industrial equipment in the operation process; processing the fault data and the steady-state data by using a preset naive Bayes model to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient; and determining the fault prior probability and the fault condition probability associated with the precision coefficient, and calculating to obtain the posterior probability of the fault of the industrial equipment according to the fault prior probability and the fault condition probability. According to the technical scheme, the potential fault risk of the equipment can be found in time, so that the effect of solving the fault in advance is achieved, the maintenance cost is reduced, and the economic loss is avoided.

Description

Industrial equipment fault prediction method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of industrial equipment failure prediction technologies, and in particular, to an industrial equipment failure prediction method, an apparatus, an electronic device, and a storage medium.

Background

In the manufacturing industry, industrial equipment is one of the guarantees that various working procedures are smoothly completed in the manufacturing process. In the field of civil aviation, various procedures of aircraft manufacturing all relate to various industrial equipment, and the guarantee of stable operation of the equipment is the basis for guaranteeing normal development of production, so that the supervision of the equipment is particularly important.

However, in actual production, the equipment is usually overhauled after the alarm of the equipment is received.

This not only results in expensive maintenance and repair costs, but also affects the execution of the production plan, which in turn affects the operation of the entire production system, causing severe economic losses.

Disclosure of Invention

The invention provides a method and a device for predicting the fault of industrial equipment, electronic equipment and a storage medium, which can find the potential fault risk of the equipment in time, thereby achieving the effect of solving the fault in advance, reducing the maintenance cost and avoiding the economic loss.

According to an aspect of the present invention, there is provided an industrial equipment failure prediction method, the method including:

acquiring fault data and steady-state data of industrial equipment in the operation process;

processing the fault data and the steady-state data by using a preset naive Bayesian model to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient;

and determining the fault prior probability and the fault condition probability associated with the precision coefficient, and calculating to obtain the posterior probability of the fault of the industrial equipment according to the fault prior probability and the fault condition probability.

According to another aspect of the present invention, there is provided an industrial equipment failure prediction apparatus, including:

the data acquisition module is used for acquiring fault data and steady-state data in the operation process of the industrial equipment;

the precision coefficient obtaining module is used for processing the fault data and the steady-state data by utilizing a preset naive Bayes model to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient;

and the posterior probability calculation module is used for determining the fault prior probability and the fault condition probability associated with the precision coefficient and calculating the posterior probability of the fault of the industrial equipment according to the fault prior probability and the fault condition probability.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of industrial equipment fault prediction according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a method for predicting a fault of an industrial device according to any one of the embodiments of the present invention when the computer instructions are executed.

According to the technical scheme of the embodiment of the invention, the precision coefficient is obtained by acquiring the fault data and the steady-state data of the industrial equipment in the operation process and processing the fault data and the steady-state data by using a preset naive Bayes model; the precision coefficient comprises overall classification precision and a Kappa coefficient, then the fault prior probability and the fault condition probability associated with the precision coefficient are determined, and the posterior probability of the fault of the industrial equipment is obtained through calculation according to the fault prior probability and the fault condition probability. According to the technical scheme, the potential fault risk of the equipment can be found in time, so that the effect of solving the fault in advance is achieved, the maintenance cost is reduced, and the economic loss is avoided.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a method for predicting a fault of an industrial device according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a process for predicting a failure of an industrial device according to an embodiment of the present disclosure;

FIG. 3 is a flow chart of a process for predicting a failure of an industrial device according to an embodiment of the present disclosure;

FIG. 4 is a schematic structural diagram of a failure device for industrial equipment according to a second embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device implementing the method for predicting the fault of the industrial device according to the embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It is to be understood that the terms "target" and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a method for predicting a fault of an industrial device according to an embodiment of the present invention, where this embodiment is applicable to predicting a fault of each industrial device, and the method may be implemented by an industrial device fault predicting apparatus, where the industrial device fault predicting apparatus may be implemented in a form of hardware and/or software, and the industrial device fault predicting apparatus may be configured on any electronic device with a network communication function, where the electronic device includes, but is not limited to: computers, personal digital assistants, and the like. As shown in fig. 1, the method includes:

and S110, acquiring fault data and steady-state data in the operation process of the industrial equipment.

In this embodiment, as the scale of data generated by the industrial device increases continuously, the equipment operation environment becomes more complex, the coupling association between system elements causes the state information of each element to be related to each other, each data in the operation process of the industrial device has a certain influence on the fault of the industrial device, and the influence of each data on the fault of the industrial device needs to be considered comprehensively, so as to mine the potential relationship between each data and the fault of the industrial device, thereby effectively predicting the possibility of the fault of the device at a certain time in the future.

The industrial equipment can be various industrial equipment in various procedures of aircraft manufacturing. For example, the industrial equipment may refer to painting robots, machine tools, and the like.

In this embodiment, the status of the industrial equipment is classified into two types, fault and steady state, and both the fault data and the steady state data include voltage, current, vibration, torque, speed, temperature, humidity, and other indicators. Real-time status data of the device can be continuously obtained based on sensors installed on the industrial device. Namely, fault data and steady-state data in the operation process of the industrial equipment are obtained in real time.

In this technical solution, optionally, the obtaining of the steady-state data in the operation process of the industrial equipment includes:

acquiring steady state data to be processed in the operation process of industrial equipment;

processing the steady-state data to be processed by utilizing a preset random undersampling technology to obtain steady-state data; wherein the number of the steady state data and the number of the fault data are the same.

In this embodiment, for the real-time monitoring equipment, time series data is generated, and is a data sequence recorded by the same index in time sequence, and the time series data related to the operation of the equipment is a series of observation data acquired over time at the operation stage of the equipment. The data volume of time sequence data is huge, the equipment is in a stable running state in most of time, and only a small part of time can cause alarm failure. Therefore, much more steady state data is collected than fault data.

Specifically, the collected steady-state data to be processed may be processed by using a random undersampling technique, so as to obtain the same number of steady-state data as the number of fault data.

By processing the steady-state data to be processed by utilizing the random undersampling technology, the problem of data imbalance of the industrial equipment can be solved, and the accuracy of fault prediction is improved.

S120, processing the fault data and the steady-state data by using a preset naive Bayesian model to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient.

The Overall classification Accuracy (OA) is used to represent the proportion of the number of samples correctly classified to all the samples. The Kappa coefficient is an index for consistency check and can also be used to measure the effect of classification.

In this embodiment, the naive bayesian model is a method based on bayesian theorem and assuming mutual independence between feature conditions, which first learns the joint probability distribution from input to output by using independence between feature words as a premise assumption through a given training set, and then inputs a feature attribute set of sample data based on the learned model to find the output that maximizes the posterior probability.

Specifically, the set naive bayes model can be used for classifying the fault data and the steady-state data to obtain the classification result of the fault data and the steady-state data, and the precision coefficient is calculated based on the classification result.

In this technical solution, optionally, the processing the fault data and the steady-state data by using a preset naive bayes model to obtain a precision coefficient includes:

determining a training sample and a testing sample according to the fault data and the steady-state data;

processing the training sample and the test sample by using a preset naive Bayes model to obtain a confusion matrix;

and calculating the sample classification data in the confusion matrix according to a preset calculation formula to obtain a precision coefficient.

In this scheme, a random sample table may be constructed based on the fault data and the steady-state data, and training samples and test samples may be set according to the random sample table. For example, 80% of the samples in the random sample table may be selected as training samples and 20% of the samples in the random sample table may be selected as testing samples.

Specifically, a preset naive Bayes model is used for classifying training samples and test samples, the posterior probability of the test samples is obtained through prediction, and then the posterior probability of the test samples obtained through prediction is compared with the real posterior probability to construct a confusion matrix.

The confusion matrix is also called an error matrix, is a standard format for identifying precision evaluation, and is represented by a matrix with n rows and n columns. The confusion matrix comprises steady-state samples TP correctly classified by the model, steady-state samples FN incorrectly classified by the model, fault samples FP incorrectly classified by the model and fault samples TN correctly classified by the model.

Wherein, the total classification precision is calculated by dividing the sum of correctly classified pixels by the total number of pixels. The number of correctly classified pixels is distributed along the diagonal of the confusion matrix, and the total number of pixels is equal to the total number of pixels of all real references.

Specifically, the overall classification accuracy is calculated by using the following formula:

or the like, or, alternatively,

wherein x is _ii Indicating correctly classified pixels and N indicates the total number of pixels of all real references.

In this embodiment, the Kappa coefficient may be determined by multiplying the total number of pixels N of all real references by the diagonal x of the confusion matrix _ii And then subtracting the number x of real reference pixels in each class _i+ And the total number x of classified pixels in the class _+i After the product, it is divided by the square of the total number of pixels N minus the total number of true reference pixels x in each class _i+ And the total number x of classified pixels in the class _+i The product of this is the result of summing all the classes.

Specifically, the Kappa coefficient is calculated using the following formula:

by calculating the precision coefficient, more optimal sample data can be selected based on the precision coefficient to carry out fault prediction, and the precision of fault prediction is improved.

In this technical solution, optionally, the training sample and the testing sample are processed by using a preset naive bayes model to obtain a confusion matrix, including:

processing the training sample by using a preset naive Bayes model to obtain a prior probability and a conditional probability of the training sample;

calculating to obtain the posterior probability of the test sample based on the prior probability and the conditional probability of the test sample and the training sample;

and comparing the posterior probability of the test sample with the predetermined real posterior probability to obtain sample classification data, and constructing a confusion matrix based on the sample classification data.

Specifically, the prior probability and the conditional probability of the corresponding training sample can be selected according to the data in the test sample, and the posterior probabilities of different data in different test samples are obtained through calculation based on the prior probability and the conditional probability of the training sample.

In this embodiment, if the posterior probability of the test sample is the same as the predetermined true posterior probability, it indicates that the sample is classified correctly, and it can be represented by a number.

S130, determining the fault prior probability and the fault condition probability associated with the precision coefficient, and calculating to obtain the posterior probability of the fault of the industrial equipment according to the fault prior probability and the fault condition probability.

The fault prior probability and the fault condition probability are obtained by processing a training sample and a testing sample in advance based on a naive Bayes model.

In this embodiment, the fault prior probability and the fault condition probability associated with the precision coefficient may be obtained by searching from the sample table based on the precision coefficient.

In the scheme, the posterior probability of faults in different future states is further calculated based on the fault prior probability and the fault condition probability, and the calculation formula is as follows:

wherein Y = { Y = ₀ ,y ₁ },y ₀ Represents steady state, y ₁ Indicating a failure. a is ₁₁ ,b ₁₁ ,....,f ₁₁ ,g ₁₁ The states of the respective indices are shown. Based on the probability, the probability calculation method of the fault occurrence is as follows:

according to the scheme, after the fault posterior probability is obtained, the industrial equipment can be checked in advance according to the fault posterior probability, the fault maintenance cost is reduced, and certain economic loss caused when the industrial equipment is used is avoided. For example, when the posterior probability of the failure of the industrial equipment is predicted to be 80%, the industrial equipment is subjected to failure check.

In this technical solution, optionally, after obtaining the accuracy coefficient, the method further includes:

comparing the precision coefficient with a preset threshold value to obtain a target precision coefficient;

correspondingly, determining the fault prior probability and the fault condition probability associated with the accuracy coefficient comprises:

determining a prior probability of failure and a conditional probability of failure associated with the target accuracy coefficient.

The preset threshold comprises an OA threshold and a Kappa threshold, and the size of the preset threshold can be set according to the industrial equipment fault prediction requirement. For example, an OA threshold of 0.8 and a kappa threshold of 0.6 may be set.

Specifically, a target accuracy coefficient whose accuracy coefficient is equal to or greater than a preset threshold may be used. Namely, a precision factor having an OA value of 0.8 or higher and a Kappa value of 0.6 or higher is set as the target precision factor.

In the scheme, if the accuracy coefficient obtained based on the steady-state data and the fault data does not accord with the constraint of the preset threshold, the steady-state data can be adjusted, circulation is continued, and the target accuracy coefficient which accords with the constraint of the preset threshold is searched. The number of cycles can be set according to the demand of predicting the fault of the industrial equipment, for example, the number of cycles can be set to 10000. That is, after the number of cycles is exceeded, no steady state data adjustment is performed.

By determining the target precision coefficient, the fault prior probability and the fault condition probability with better performance can be obtained.

In this technical solution, optionally, determining the prior probability of failure and the conditional probability of failure associated with the target accuracy coefficient includes:

determining a sample table associated with the target precision coefficient according to the target precision coefficient;

and searching in the sample table to obtain the fault prior probability and the fault condition probability associated with the target precision coefficient.

The target precision coefficient, the fault prior probability and the fault condition probability are stored in the sample table, and the fault prior probability and the fault condition probability associated with the target precision coefficient can be obtained by searching from the sample table according to the target precision coefficient.

Specifically, a sample table with the largest target precision coefficient may be selected from the sample tables as an optimal sample table, and the failure prior probability and the failure condition probability stored in the optimal sample table are extracted.

By determining the fault prior probability and the fault condition probability, the potential fault risk of the equipment can be timely discovered, so that the effect of solving the fault in advance is achieved, the maintenance cost is reduced, and the economic loss is avoided.

For example, fig. 2 is a schematic diagram of a process for predicting a fault of an industrial device according to an embodiment of the present disclosure, and as shown in fig. 2, the industrial device is divided into two types, namely a fault type and a steady state type, and the two types are trained by selecting indexes such as voltage, current, vibration, torque, speed, temperature, humidity and the like and substituting the selected indexes into a naive bayes model, and then the training result is substituted into the model to predict the fault.

Exemplarily, fig. 3 is a flowchart of a process for predicting a fault of an industrial device according to an embodiment of the present application, and as shown in fig. 3, a random undersampling process is performed first to obtain steady-state data B and fault data a; constructing a random sample table M based on the steady-state data and the fault data; setting a training sample and a testing sample; probability calculation is carried out based on a naive Bayes model; calculating precision coefficients (OA and Kappa); judging whether OA is more than or equal to 0.8 and Kappa is more than or equal to 0.6; if yes, storing a sample table corresponding to the precision coefficient; selecting an optimal sample table from the sample tables; calculating the posterior probability; and (4) measuring and calculating the equipment fault risk. If not, adjusting the steady-state data, and continuing to calculate until the cycle is finished.

According to the technical scheme of the embodiment of the invention, the precision coefficient is obtained by acquiring the fault data and the steady-state data of the industrial equipment in the operation process and processing the fault data and the steady-state data by using a preset naive Bayes model; the precision coefficient comprises overall classification precision and a Kappa coefficient, then the fault prior probability and the fault condition probability associated with the precision coefficient are determined, and the posterior probability of the fault of the industrial equipment is obtained through calculation according to the fault prior probability and the fault condition probability. By executing the technical scheme, the fault risk in the operation process of the industrial equipment can be predicted in real time, so that the effect of solving the fault in advance is achieved, the maintenance cost is reduced, and the economic loss is avoided.

Example two

Fig. 4 is a schematic structural diagram of an industrial equipment failure device according to a second embodiment of the present invention. As shown in fig. 4, the apparatus includes:

the data acquisition module 410 is used for acquiring fault data and steady-state data in the operation process of the industrial equipment;

a precision coefficient obtaining module 420, configured to process the fault data and the steady-state data by using a preset naive bayes model, so as to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient;

and the posterior probability calculating module 430 is configured to determine the prior probability of the fault and the probability of the fault condition associated with the accuracy coefficient, and calculate the posterior probability of the fault of the industrial equipment according to the prior probability of the fault and the probability of the fault condition.

In this technical solution, optionally, the data obtaining module 410 is specifically configured to:

processing the steady-state data to be processed by utilizing a preset random undersampling technology to obtain steady-state data; wherein the number of steady state data and the number of fault data are the same.

In this technical solution, optionally, the precision coefficient obtaining module 420 includes:

the sample determining unit is used for determining a training sample and a testing sample according to the fault data and the steady-state data;

a confusion matrix obtaining unit, configured to process the training sample and the test sample by using a preset naive bayes model to obtain a confusion matrix;

and the precision coefficient obtaining unit is used for calculating the sample classification data in the confusion matrix according to a preset calculation formula to obtain the precision coefficient.

In this technical solution, optionally, the confusion matrix obtaining unit is specifically configured to:

In this technical solution, optionally, the apparatus further includes:

the target precision coefficient obtaining module is used for comparing the precision coefficient with a preset threshold value to obtain a target precision coefficient;

accordingly, the posterior probability computation module 430 includes:

and the fault probability determination unit is used for determining the fault prior probability and the fault condition probability which are associated with the target precision coefficient.

In this technical solution, optionally, the failure probability determining unit is specifically configured to:

The industrial equipment fault prediction device provided by the embodiment of the invention can execute the industrial equipment fault prediction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

EXAMPLE III

FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 11 performs the various methods and processes described above, such as an industrial equipment failure prediction method.

In some embodiments, an industrial equipment failure prediction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, the computer program may perform one or more steps of an industrial equipment failure prediction method as described above. Alternatively, in other embodiments, the processor 11 may be configured to perform an industrial equipment failure prediction method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting a failure of an industrial device, comprising:

processing the fault data and the steady-state data by using a preset naive Bayes model to obtain a precision coefficient; wherein the precision coefficient comprises an overall classification precision and a Kappa coefficient;

2. The method of claim 1, wherein obtaining steady state data during operation of the industrial plant comprises:

3. The method of claim 1, wherein the processing the fault data and the steady-state data using a preset naive bayes model to obtain a precision coefficient comprises:

4. The method of claim 3, wherein processing the training samples and the testing samples using a preset naive Bayes model to obtain a confusion matrix comprises:

5. The method of claim 1, wherein after obtaining the precision factor, the method further comprises:

6. The method of claim 5, wherein determining the prior probability of failure and the conditional probability of failure associated with the target accuracy coefficient comprises:

7. An industrial equipment failure prediction apparatus, comprising:

8. The apparatus of claim 7, wherein the data acquisition module is specifically configured to:

acquiring steady-state data to be processed in the operation process of industrial equipment;

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a method of industrial equipment failure prediction as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to execute a method of predicting a failure of an industrial device according to any one of claims 1-6.