Method and platform for predicting service life of equipment and electronic equipment

Abstract

The invention provides a service life prediction method and platform of equipment and electronic equipment, and belongs to the field of industrial Internet of Things and intelligent maintenance. The method comprises the following steps: acquiring operation data of equipment; the operation data of the equipment are input into a pre-constructed linear degradation model for residual life prediction, a life prediction result is obtained, and the linear degradation model is constructed based on the linear change relation between the equipment health index observed quantity and time in the performance degradation process of the equipment. The residual service life of the equipment is efficiently predicted by constructing the linear degradation model and combining the equipment operation data collected in real time, so that predictive maintenance is realized, the non-planned downtime is shortened, the maintenance cost is reduced, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial Internet of Things and intelligent maintenance, and specifically relates to a life prediction method, platform and electronic equipment for equipment. Background Art

[0002] With the rapid development of industrial automation and information technology, the traditional industrial equipment maintenance model is undergoing profound changes. The core of this change is to move from traditional regular maintenance and post-failure repair to more intelligent and forward-looking predictive maintenance. Predictive maintenance can not only significantly improve the operating efficiency of equipment, but also significantly reduce production losses caused by unexpected downtime.

[0003] However, despite the obvious transformation trend and broad prospects, the existing industrial equipment life prediction system faces many challenges. The first problem is the comprehensiveness of data collection. Many key operating data are missing due to unreasonable sensor layout or data acquisition technology limitations, which directly affects the accuracy and reliability of subsequent analysis. In addition, the adaptability of the model is also a major pain point. Different industrial equipment and different working environments often have very different life decay laws. Existing prediction models are often difficult to flexibly adapt to these complex and changing situations, resulting in the general applicability of the prediction results is not strong. Finally, the low prediction accuracy is also a problem that needs to be solved urgently. Affected by multiple factors such as data quality and model algorithms, the current prediction system often finds it difficult to give convincing high-precision prediction results, which not only affects the scientific nature of maintenance decisions, but also restricts the widespread application of predictive maintenance technology in the industrial field. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, platform and electronic device for predicting the life of an equipment, which are used to fully or at least partially solve the technical problems existing in the above-mentioned prior art that the systems for predicting the life of industrial equipment often have incomplete data collection, poor model adaptability and low prediction accuracy.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting the life of a device, comprising: Collect equipment operation data; The operation data of the equipment is input into a pre-built linear degradation model to predict the remaining life and obtain a life prediction result, wherein the linear degradation model is constructed based on the linear change relationship between the observed value of the equipment health indicator and time during the performance degradation process of the equipment.

[0006] Optionally, after collecting the operation data of the equipment, the equipment life prediction method further includes: Clean the collected equipment operation data to remove noise and outliers; Perform feature screening on the equipment operation data after data cleaning to obtain target data.

[0007] Optionally, the construction process of the linear degradation model includes: Assuming that the relationship between a certain observed quantity and time during the performance degradation process of the device satisfies a linear change, an initial linear degradation model is constructed based on the linear change; For devices with complete life cycle historical data, the slope and intercept of each device are obtained through linear regression, and the noise variance is calculated; Summarize the model parameters of all devices and obtain the prior model parameters by weighted average method, wherein the model parameters of all devices at least include the operation time of the devices and the weight of the number of data points; Calculate the posterior model parameters according to the accumulated state variables, the noise variance and the prior model parameters; A linear relationship test is performed on the input equipment operation data. If the linear relationship is not satisfied, the model parameters are reinitialized and the test process is restarted until the linear relationship is satisfied to obtain the target linear degradation model.

[0008] Optionally, the initial linear degradation model formula is as follows: S(t)=θt+φ+ε; Where S(t) represents the observed value of the health indicator or specific performance parameter of the equipment at time t, θt is the degradation rate, φ is the initial state, and ε is the random error term.

[0009] Optionally, calculate the noise variance according to the following formula: ; In the formula, is the predicted value at the jth time point, S ij is the true observation at the jth time point.

[0010] Optionally, the formula for obtaining the prior model parameters by weighted average method is as follows: ; ; In the formula, , represents the prior model parameters, N is the total number of devices, is the weight of the i-th device.

[0011] Optionally, the formula for calculating the posterior model parameters based on the accumulated state variables, the noise variance, and the prior model parameters is as follows: ; ; In the formula, , represents the posterior model parameters, σ 2 represents the noise variance, , represents the prior model parameters, k is the number of observation points before the current time point, and t i represents a time series, is the variance of the prior model parameters.

[0012] Optionally, the remaining life of the equipment is predicted according to the following formula: ; In the formula, , represents the posterior model parameters, S f is the failure threshold of the device.

[0013] In a second aspect, the present application also provides a life prediction platform for equipment, including: Data collection layer, used to collect equipment operation data; The data transmission layer is used to transmit the collected operation data of the equipment to the data storage layer through the industrial gateway; The data storage layer is used to store the operation data of the device using distributed database or cloud storage technology; The data processing and analysis layer is used to clean the equipment's operating data, extract features, build and train linear change models, and analyze life prediction results; The user interface layer is used to provide an operation interface so that users can view device status, receive warning information, and view prediction results.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for predicting the life of the device when executing the program.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: In the equipment life prediction method, platform and electronic equipment provided in the present application, by constructing a linear degradation model and combining the equipment operation data collected in real time, the remaining service life of the equipment is efficiently predicted, thereby realizing predictive maintenance, reducing unplanned downtime, reducing maintenance costs and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 A flowchart of an implementation method for predicting the life of a device provided by an embodiment of the present invention; Figure 2 A flowchart of a construction process of a linear degradation model provided by an embodiment of the present invention; Figure 3 A detailed implementation flow chart of a method for predicting the life of a device provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a life prediction platform for equipment provided by an embodiment of the present invention; Figure 5 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the detailed description below, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0019] Hereinafter, the terms "include" or "may include" that may be used in various embodiments of the present disclosure indicate the presence of the disclosed functions or operations, and do not limit the addition of one or more functions or operations. In addition, as used in various embodiments of the present disclosure, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, or combinations of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing items.

[0020] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the words listed at the same time. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 The flowchart of a method for predicting the life of equipment in a specific embodiment is shown, and includes the following execution steps: Step 100: Collecting operation data of the equipment.

[0023] Specifically, various sensors installed on industrial equipment collect equipment operation data and historical operation and maintenance data in real time.

[0024] Exemplary, various types of sensors, such as vibration sensors, temperature sensors, pressure sensors, etc.

[0025] In some embodiments, after collecting the operating data of the equipment, the equipment life prediction method further includes: performing data cleaning on the collected operating data of the equipment to remove noise and outliers; and performing feature screening on the operating data of the equipment after data cleaning to obtain target data.

[0026] Preferably, a method combining statistical analysis and expert experience is used to identify sensor data that has a significant change trend with the equipment operation cycle as modeling feature data, such as vibration intensity data, bearing temperature data, hydraulic system pressure data, and motor current data.

[0027] Through data cleaning and feature screening, noise and outliers can be removed, and feature data closely related to the target variable (equipment life) can be selected, thereby improving the accuracy and reliability of the prediction model. Feature screening can reduce the number of input features, reduce the complexity of modeling, and improve the operating efficiency and interpretability of the model.

[0028] Step 101: Input the operation data of the equipment into a pre-built linear degradation model to predict the remaining life and obtain a life prediction result, wherein the linear degradation model is constructed based on the linear change relationship between the observed value of the equipment health indicator and time during the performance degradation process of the equipment.

[0029] In this embodiment, by constructing a linear degradation model and combining historical data, the remaining service life of industrial equipment is efficiently predicted, thereby achieving predictive maintenance, reducing unplanned downtime, reducing maintenance costs, and improving production efficiency.

[0030] In one embodiment of the present invention, based on the construction of the linear degradation model in step 101, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner. Figure 2 shown.

[0031] S1: Assume that the relationship between a certain observed quantity and time during the performance degradation process of the device satisfies a linear change, and build an initial linear degradation model based on this linear change.

[0032] Specifically, the initial linear degradation model formula is as follows: S(t)=θt+φ+ε; Where S(t) represents the observed value of the health indicator or specific performance parameter of the equipment at time t, θt is the degradation rate (slope), φ is the initial state (intercept), and ε is the random error term.

[0033] S2: For devices with complete life cycle historical data, the slope and intercept of each device are obtained through linear regression, and the noise variance is calculated.

[0034] For equipment with complete life cycle historical data, the slope and intercept of each device are obtained through linear regression, and the noise variance is calculated, which can achieve the technical effects of equipment performance prediction, fault diagnosis and early warning, and equipment optimization and improvement. It has important practical significance for improving equipment reliability and stability, reducing maintenance costs, and extending equipment service life.

[0035] In some embodiments, the linear regression equation is solved using the method of least squares: ; in, It is a time series The mean of Health indicator data The mean of .

[0036] Specifically, the noise variance is calculated according to the following formula: ; In the formula, is the predicted value at the jth time point, S ij is the true observation at the jth time point.

[0037] S3: Summarize the model parameters of all devices and obtain the prior model parameters by weighted average method, wherein the model parameters of all devices at least include the operation time of the devices and the weight of the number of data points.

[0038] By summarizing the model parameters of all equipment and obtaining the prior model parameters through the weighted average method, the generalization ability of model parameters can be significantly improved, the robustness of model prediction can be enhanced, the model training efficiency can be optimized, and equipment maintenance and management decisions can be promoted. This is of great significance for improving the level of intelligent equipment management, reducing production costs and improving overall production efficiency.

[0039] Specifically, the formula for obtaining the prior model parameters by the weighted average method is as follows: ; ; In the formula, , represents the prior model parameters, N is the total number of devices, is the weight of the i-th device.

[0040] S4: Calculate the posterior model parameters based on the accumulated state variables, the noise variance and the prior model parameters.

[0041] For each device, the cumulative state variable is calculated according to the following formula: ; Where k is the number of observation points before the current time point.

[0042] Specifically, according to the accumulated state variables, noise variance and prior model parameters, the formula for calculating the posterior model parameters is as follows: ; ; In the formula, , represents the posterior model parameters, σ 2 represents the noise variance, , represents the prior model parameters, k is the number of observation points before the current time point, and t i represents a time series, is the variance of the prior model parameters.

[0043] S5: Perform a linear relationship test on the input equipment operation data. If the linear relationship is not satisfied, reinitialize the model parameters and restart the test process until the linear relationship is satisfied to obtain the target linear degradation model.

[0044] For example, a T test is used to calculate the T statistic: ; If twice the probability of the calculated T statistic is less than the significance level α, it is considered that the linear relationship is not satisfied; otherwise, continue to use the current model parameters.

[0045] By performing a linear relationship test on the input equipment operation data, and reinitializing the model parameters and restarting the test process when the linear relationship is not satisfied, until the target linear degradation model that satisfies the linear relationship is obtained, the applicability of the model can be ensured, the prediction accuracy can be improved, the model stability can be enhanced, and resource utilization can be optimized. This is of great significance for realizing the intelligent maintenance of industrial equipment and improving equipment operation efficiency and production benefits.

[0046] In some embodiments, the remaining life of the device is predicted according to the following formula: ; In the formula, , represents the posterior model parameters, S f is the failure threshold of the device.

[0047] In some implementations, a maintenance plan is automatically generated based on the prediction results, including maintenance time, maintenance content, required spare parts, etc. By combining the production plan with the inventory management system, spare parts procurement and inventory management are optimized, unnecessary inventory backlogs and capital occupation are reduced, production scheduling optimization suggestions are provided, and production arrangements are adjusted according to the equipment life prediction results to ensure efficient and stable operation of the production line.

[0048] The technical effects achieved by this application are as follows: (1) Improving prediction accuracy: By constructing a linear degradation model based on historical data and optimizing it with a machine learning algorithm, the accuracy of industrial equipment life prediction is significantly improved. (2) Realizing predictive maintenance: Predicting the remaining service life of industrial equipment in advance makes it possible for enterprises to implement predictive maintenance, reduce unplanned downtime, and reduce maintenance costs. (3) Optimizing resource allocation: Intelligently generating maintenance plans and spare parts procurement recommendations based on prediction results optimizes the enterprise's resource allocation and improves production efficiency and economic benefits. (4) Enhancing decision support: Providing intuitive and accurate data support to help enterprise management make more scientific and reasonable production scheduling and maintenance decisions.

[0049] In one embodiment, Figure 3 This is a detailed flow chart of a method for predicting the life of a device according to an embodiment of the present invention. This embodiment is further optimized and expanded on the basis of the above embodiments.

[0050] S300: Collecting operation data of the equipment.

[0051] S301: Clean the collected operation data of the equipment to remove noise and abnormal values.

[0052] S302: Perform feature screening on the operation data of the equipment after data cleaning to obtain target data.

[0053] S303: Assuming that the relationship between a certain observed quantity and time during the performance degradation process of the device satisfies a linear change, an initial linear degradation model is constructed based on the linear change.

[0054] S304: For devices with complete life cycle historical data, obtain the slope and intercept of each device through linear regression, and calculate the noise variance.

[0055] S305: Summarize the model parameters of all devices and obtain a priori model parameters by weighted average method, wherein the model parameters of all devices at least include the operation time of the devices and the weight of the number of data points.

[0056] S306: Calculate the posterior model parameters according to the accumulated state variables, the noise variance and the prior model parameters.

[0057] S307: Perform a linear relationship test on the input equipment operation data. If the linear relationship is not satisfied, reinitialize the model parameters and restart the test process until the linear relationship is satisfied to obtain the target linear degradation model.

[0058] S308: Inputting the operation data of the equipment into a pre-built linear degradation model to perform remaining life prediction and obtain a life prediction result.

[0059] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0060] like Figure 4 As shown, the following is an embodiment of the life prediction platform of the equipment provided by the embodiment of the present disclosure, which belongs to the same inventive concept as the life prediction method of the equipment in the above-mentioned embodiments. For details not described in detail in the embodiment of the life prediction platform of the equipment, please refer to the embodiment of the life prediction method of the above-mentioned equipment.

[0061] The data collection layer 40 is used to collect the operation data of the equipment; The data storage layer 41 is used to transmit the collected operation data of the equipment to the data storage layer through the industrial gateway; The data storage layer 42 is used to store the operation data of the device using a distributed database or cloud storage technology; The data processing and analysis layer 43 is used for cleaning the equipment's operating data, extracting features, building and training a linear change model, and analyzing life prediction results; The user interface layer 44 is used to provide an operation interface, allowing users to view device status, receive warning information, and view prediction results.

[0062] In this embodiment, the data acquisition layer can capture the operation data of the equipment in a comprehensive and real-time manner, while the data transmission layer uses the powerful data transmission capability of the industrial gateway to ensure that the data can be quickly and stably transmitted to the data storage layer. The optimization of this process provides a solid foundation for subsequent data processing and analysis. The data storage layer adopts distributed database or cloud storage technology, which can not only effectively meet the needs of large-scale data storage, but also ensure the security and reliability of the data. At the same time, this storage method also has good scalability and can flexibly adjust storage resources as the amount of data grows. The data processing and analysis layer integrates advanced data processing technologies such as data cleaning, feature extraction, linear change model construction and training, which can deeply mine the hidden information in the equipment operation data and build a high-precision prediction model. This level of intelligent processing significantly improves the accuracy and reliability of life prediction. The user interface layer provides an intuitive and easy-to-use operation interface, allowing users to easily view the equipment status, receive warning information, and view the prediction results. Not only does it improve the user experience, but it also enables the prediction results to be quickly converted into actual maintenance decisions, improving the operation efficiency and production benefits of the equipment.

[0063] Figure 5 It is a schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0064] The life prediction method of the equipment provided in the embodiment of the present application can be applied to electronic equipment. It can be understood by those skilled in the art that the electronic equipment structure involved in the embodiment of the present invention does not constitute a limitation on the electronic equipment, and the electronic equipment may include more or less components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, the electronic equipment includes but is not limited to laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic equipment can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0065] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0066] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0067] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0068] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0069] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0070] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0071] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0072] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0073] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0074] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0075] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0076] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0077] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0078] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0079] The above-mentioned electronic device realizes the operation data of the equipment collected in the subject name of the life prediction method of the equipment in this application; the operation data of the equipment is input into a pre-constructed linear degradation model for remaining life prediction to obtain a life prediction result, wherein the linear degradation model is a technical solution constructed based on the linear change relationship between the observed value of the equipment health indicator and time during the performance degradation process of the equipment, thereby achieving efficient prediction of the remaining service life of industrial equipment, thereby helping enterprises to achieve predictive maintenance, reduce unplanned downtime, and reduce maintenance costs.

[0080] The storage medium provided in the present application stores a program product capable of implementing a method for predicting the life of a device.

[0081] The device life prediction method includes: collecting device operation data; inputting the device operation data into a pre-built linear degradation model to perform remaining life prediction, and obtaining a life prediction result, wherein the linear degradation model is constructed based on the linear change relationship between the device health indicator observation quantity and time during the performance degradation process of the device.

[0082] In some possible implementations, the life prediction method and system of the subject device of the present disclosure may be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above “Exemplary Method” section of this specification according to various exemplary implementations of the present disclosure.

[0083] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0084] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the life of equipment, characterized in that: include: Collect equipment operation data; The operation data of the equipment is input into a pre-built linear degradation model to predict the remaining life and obtain a life prediction result, wherein the linear degradation model is constructed based on the linear change relationship between the observed value of the equipment health indicator and time during the performance degradation process of the equipment.

2. The device life prediction method according to claim 1, characterized in that: After collecting the operation data of the equipment, the life prediction method of the equipment further includes: Clean the collected equipment operation data to remove noise and outliers; Perform feature screening on the equipment operation data after data cleaning to obtain target data.

3. The device life prediction method according to claim 1, characterized in that: The construction process of the linear degradation model includes: Assuming that the relationship between a certain observed quantity and time during the performance degradation process of the device satisfies a linear change, an initial linear degradation model is constructed based on the linear change; For devices with complete life cycle historical data, the slope and intercept of each device are obtained through linear regression, and the noise variance is calculated; Summarize the model parameters of all devices and obtain the prior model parameters by weighted average method, wherein the model parameters of all devices at least include the operation time of the devices and the weight of the number of data points; Calculate the posterior model parameters according to the accumulated state variables, the noise variance and the prior model parameters; A linear relationship test is performed on the input equipment operation data. If the linear relationship is not satisfied, the model parameters are reinitialized and the test process is restarted until the linear relationship is satisfied to obtain the target linear degradation model.

4. The device life prediction method according to claim 3, characterized in that: The initial linear degradation model formula is as follows: S(t)=θt+φ+ε; Where S(t) represents the observed value of the equipment's health index or set performance parameter at time t, θt is the degradation rate, φ is the initial state, and ε is the random error term.

5. The method for predicting the life of equipment according to claim 3, characterized in that: The noise variance is calculated according to the following formula: ； In the formula, is the predicted value at the jth time point, S ij is the true observation at the jth time point.

6. The method for predicting the life of equipment according to claim 3, characterized in that: The formula for obtaining the prior model parameters by weighted average method is as follows: ； ； In the formula, , represents the prior model parameters, N is the total number of devices, is the weight of the i-th device.

7. The device life prediction method according to claim 3, characterized in that: According to the accumulated state variables, noise variance and prior model parameters, the formula for calculating the posterior model parameters is as follows: ； ； In the formula, , represents the posterior model parameters, σ 2 represents the noise variance, , represents the prior model parameters, k is the number of observation points before the current time point, and t i represents a time series, is the variance of the prior model parameters.

8. The device life prediction method according to claim 7, characterized in that: The remaining life of the equipment is predicted according to the following formula: ； In the formula, , represents the posterior model parameters, S f is the failure threshold of the device.

9. A life prediction platform for equipment, characterized in that: include: Data collection layer, used to collect equipment operation data; The data transmission layer is used to transmit the collected operation data of the equipment to the data storage layer through the industrial gateway; The data storage layer is used to store the operation data of the device using distributed database or cloud storage technology; The data processing and analysis layer is used to clean the equipment's operating data, extract features, build and train linear change models, and analyze life prediction results; The user interface layer is used to provide an operation interface so that users can view device status, receive warning information, and view prediction results.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the device life prediction method according to any one of claims 1 to 7 are implemented.

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