State evaluation method, system and equipment of complex electromechanical equipment, medium and product

Abstract

The invention discloses a state evaluation method, system and device for complex electromechanical equipment, a medium and a product, and relates to the field of computer simulation, and the method comprises the steps: obtaining fault time data in the operation process of the complex electromechanical equipment; the fault time data comprises accumulated fault numbers and time corresponding to each fault number; based on the fault time data, obtaining a cumulative fault function; deriving the accumulative fault function to obtain a fault probability density function; obtaining a fault probability density curve based on the fault probability density function; the abscissa of the fault probability density curve is time, and the ordinate of the fault probability density curve is fault probability density; and analyzing the trend of the fault probability density curve to evaluate the current state of the complex electromechanical equipment. According to the method, the fault time data in the operation process of the complex electromechanical equipment is analyzed, the current state of the complex electromechanical equipment is obtained, subsequent maintenance of the complex electromechanical equipment is facilitated, and the method has higher generalization ability.

Description

Technical Field

[0001] The present application relates to the field of computer simulation, and particularly to a method, system, device, medium and product for evaluating the state of complex electromechanical equipment. Background Art

[0002] Complex electromechanical equipment, such as nuclear power equipment, aerospace equipment, numerically controlled electromechanical equipment, etc., usually has the characteristics of complex overall structure, rich equipment functions, and close correlation between various components. The equipment is subjected to various forms of loads during its service life, and its working environment is very complex, making equipment failures generally have coupling and uniqueness. Therefore, it is difficult to fully analyze the failure mechanism of the equipment when analyzing the failure characteristics and degradation trends of complex electromechanical equipment, thus limiting the availability of relevant physical models of the equipment. Complex electromechanical equipment is also developing towards more automation, intelligence, high reliability, and high precision. The speed of replacement of electromechanical equipment is accelerating continuously, reducing the degree of reference of historical experience and knowledge. Therefore, there is an urgent need for a state evaluation method for electromechanical equipment with strong generalization ability to judge the fault stage of electromechanical equipment. Summary of the Invention

[0003] The purpose of the present application is to provide a method, system, device, medium and product for evaluating the state of complex electromechanical equipment. The present application analyzes the fault time data during the operation of complex electromechanical equipment to obtain the current state of the complex electromechanical equipment, laying a foundation for subsequent maintenance of the complex electromechanical equipment and having stronger generalization ability.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a method for evaluating the state of complex electromechanical equipment, including:

[0006] Obtaining the fault time data during the operation of complex electromechanical equipment; the fault time data includes the accumulated number of faults and the time corresponding to each number of faults;

[0007] Based on the fault time data, obtaining the cumulative fault function;

[0008] Taking the derivative of the cumulative fault function to obtain the fault probability density function;

[0009] Based on the fault probability density function, obtaining the fault probability density curve; the abscissa of the fault probability density curve is time, and the ordinate is the fault probability density;

[0010] Analyzing the trend of the fault probability density curve to evaluate the current state of complex electromechanical equipment.

[0011] Second aspect, the present application provides a state evaluation system for complex electromechanical equipment, including:

[0012] A fault time data module for obtaining fault time data during the operation of complex electromechanical equipment; the fault time data includes the accumulated number of faults and the time corresponding to each number of faults;

[0013] An accumulated fault function acquisition module, connected to the fault time data module, for obtaining an accumulated fault function based on the fault time data;

[0014] A fault probability density function acquisition module, connected to the accumulated fault function acquisition module, for taking the derivative of the accumulated fault function to obtain a fault probability density function;

[0015] A fault probability density curve acquisition module, connected to the fault probability density function acquisition module, for obtaining a fault probability density curve based on the fault probability density function; the abscissa of the fault probability density curve is time, and the ordinate is the fault probability density;

[0016] A complex electromechanical equipment evaluation module, connected to the fault probability density curve acquisition module, for analyzing the trend of the fault probability density curve to evaluate the current state of the complex electromechanical equipment.

[0017] Third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the above-mentioned state evaluation method for complex electromechanical equipment.

[0018] Fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned state evaluation method for complex electromechanical equipment is implemented.

[0019] Fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above-mentioned state evaluation method for complex electromechanical equipment is implemented.

[0020] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0021] The present application provides a state evaluation method, system, device, medium and product for complex electromechanical equipment. By obtaining a fault probability density curve from the fault time data during the operation process, and then evaluating the current state of the complex electromechanical equipment through the trend of the fault probability density curve, it is applicable to the fault situations of various electromechanical equipment, has stronger generalization ability, and can perform corresponding maintenance according to the current state of the complex electromechanical equipment, improving the maintenance efficiency of the complex electromechanical equipment. Brief Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is an application environment diagram of a method for evaluating the state of a complex electromechanical device in an embodiment of the present application;

[0024] Figure 2 It is a flowchart of a method for evaluating the state of a complex electromechanical device provided in an embodiment of the present application;

[0025] Figure 3 It is a cumulative failure fitting curve diagram of a method for evaluating the state of a complex electromechanical device provided in an embodiment of the present application;

[0026] Figure 4 It is a failure probability density curve diagram of a method for evaluating the state of a complex electromechanical device provided in an embodiment of the present application;

[0027] Figure 5 It is a Mann-Kendall trend analysis diagram of the failure probability density curve of a method for evaluating the state of a complex electromechanical device provided in an embodiment of the present application;

[0028] Figure 6 It is a partition diagram of the failure probability density curve of a method for evaluating the state of a complex electromechanical device provided in an embodiment of the present application;

[0029] Figure 7 It is a functional module diagram of a system for evaluating the state of a complex electromechanical device provided in another embodiment of the present application;

[0030] Figure 8 It is a structural diagram of a computer device provided in an embodiment of the present application. Detailed Description of the Embodiments

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0032] To make the above objects, features, and advantages of the present application more apparent and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] The state evaluation method for complex electromechanical equipment provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the fault time data during the operation process to the server 104. After receiving the fault time data during the operation process, for the fault time data during the operation process, the server 104 obtains a fault probability density curve based on the fault time data; analyzes the trend of the fault probability density curve to evaluate the current state of the complex electromechanical equipment. The server 104 can feedback the obtained current state of the complex electromechanical equipment to the terminal 102. In addition, in some embodiments, the state evaluation method for complex electromechanical equipment can also be implemented separately by the server 104 or the terminal 102.

[0034] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0035] In an exemplary embodiment, as Figure 2 shown, a state evaluation method for complex electromechanical equipment is provided. This method is executed by a computer device, and can be specifically executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in as an example for illustration, it includes the following steps 201 to step 208. Among them:

[0036] Step 201, obtain the fault time data during the operation of the complex electromechanical equipment; the fault time data includes the accumulated number of faults and the time corresponding to each number of faults.

[0037] Step 202, obtain an accumulated fault function based on the fault time data.

[0038] Step 203, take the derivative of the accumulated fault function to obtain a fault probability density function.

[0039] Step 204: Obtain a failure probability density curve based on the failure probability density function; the abscissa of the failure probability density curve is time, and the ordinate is the failure probability density.

[0040] Step 205: Analyze the trend of the failure probability density curve to evaluate the current state of the complex electromechanical equipment.

[0041] In another exemplary embodiment of the present application, a field test is conducted on a certain type of CNC grinding machine, and the failure time data is collected. The test uses time-truncation, and the truncation time is set to 17000 h. The failure time data is shown in Table 1.

[0042] Table 1 Grinding machine failure time data

[0043] Number of failures 1 2 3 4 5 6 7 8 9 Time 100 230 400 700 1000 1400 1700 3000 5000 Number of failures 10 11 12 13 14 15 16 17 18 Time 8000 10100 11900 12800 14100 15100 16000 16500 17000

[0044] In another exemplary embodiment of the present application, Step 202 specifically includes:

[0045] Perform linear fitting on the failure time data to obtain a cumulative failure fitting curve; the abscissa of the cumulative failure fitting curve is time, and the ordinate is the cumulative number of failures. In the present application, the polynomial curve fitting is performed using the cftool in the Matlab built-in toolbox.

[0046] Obtain a cumulative failure function according to the cumulative failure fitting curve. The cumulative failure function is:

[0047] W(t) = p1·t 6 + p2·t 5 + p3·t 4 + p4·t 3 + p5·t 2 + p6·t + p7.

[0048] Wherein, W(t) is the cumulative number of failures, p1, p2, p3, p4, p5, p6, and p7 are constants with different values, and t is time.

[0049] The cumulative failure fitting curve is as Figure 3 shown. After analysis, the fitting effect of the cumulative failure fitting curve on the failure time data is as follows:

[0050] The sum of squares for error (SSE) is 0.4266; the corresponding formula is: Wherein, M is the number of failure time data, m is the serial number of the failure time data, W m is the actual cumulative number of failures, is the cumulative number of failures in the fitting curve.

[0051] The Root Mean Square Error (RMSE) is 0.1969. The corresponding formula is: Analysis Figure 3 It can be obtained that the cumulative failure fitting curve can well fit the failure time data.

[0052] In another exemplary embodiment of the present application, the failure probability density function is:

[0053] f(t) = 6·p1·t 5 + 5·p2·t 4 + 4·p3·t 3 + 3·p4·t 2 + 2·p5·t + p6.

[0054] Wherein, f(t) is the failure probability density, p1, p2, p3, p4, p5 and p6 are constants with different values, and t is time.

[0055] In another exemplary embodiment of the present application, step 205 specifically includes: using the Mann-Kendall trend analysis method and the Mann-Kendall mutation detection method to analyze the trend of the failure probability density curve to evaluate the current state of complex electromechanical equipment.

[0056] The current state of the complex electromechanical equipment is the early failure period or the accidental failure period or the wear failure period.

[0057] If the failure probability density curve shows a downward trend over time, the current state of the complex electromechanical equipment is the early failure period.

[0058] If the failure probability density curve shows an upward trend over time and the change rate is less than or equal to the set value, the current state of the complex electromechanical equipment is the accidental failure period.

[0059] If the failure probability density curve shows an upward trend over time and the change rate is greater than the set value, the current state of the complex electromechanical equipment is the wear failure period.

[0060] The failure probability density curve is as Figure 4 shown. It can be seen from Figure 4 that this failure probability density curve presents an atypical bathtub shape. The whole curve can be divided into three stages: the early failure period, the accidental failure period and the wear failure period. In the early failure period, the failure probability density curve rapidly decreases as the components of the electromechanical equipment are run in; in the accidental failure period, the failure probability density shows a slow upward trend over time; in the wear failure period, the upward trend of the failure probability density over time is enhanced.

[0061] To evaluate the current state of the electromechanical equipment, it is necessary to find the inflection point of the failure probability density curve, divide the entire service life cycle into three stages: early failure period, accidental failure period, and wear failure period, and evaluate which stage the current state belongs to. This application uses the Mann-Kendall trend analysis method to judge the trend of the failure probability density changing with time.

[0062] The Mann-Kendall trend analysis method is a non-parametric statistical test method for trend testing of sequence data. It is usually used to test whether the trend is significant and is widely used in the research of trend testing of sequence data. Assume that the current set of failure probability density sequences is X.

[0063] X = (x1, x2, x3,..., x n ).

[0064] Among them, x1, x2, x3, x n represent n failure probability densities, n is the number of failure probability densities, and n ≥ 10. Establish a standard normal distribution statistic Z:

[0065]

[0066] Among them, Var(S) is the variance, S is the sum of the cumulative numerical change signs, and it approximately follows a normal distribution. q is the number of groups with the same failure probability density, d is the serial number of the group with the same failure probability density, c d is the number of the same failure probability density as the d-th group, x i is the i-th failure probability density, x j is the j-th failure probability density, i and j represent the serial numbers of the failure probability densities, and sgn() is the sign function. In the whole trend test process, first assume that H0 means that the failure probability density samples are independent and identically distributed and there is no trend. If Among them, represents the 100(1 - α / 2)-th percentile of the standard normal distribution, α is the significance level, that is, it indicates that the original hypothesis H0 does not hold, indicating that the original failure probability density sequence has an obvious change trend; if Z > 0, it indicates that the original failure probability density sequence has an obvious upward trend; if Z < 0, it indicates that the original failure probability density sequence has an obvious downward trend. Otherwise, the original hypothesis H0 is accepted.

[0067] The Mann-Kendall mutation detection method judges the upward or downward trend of the curve by calculating some auxiliary quantities.

[0068]

[0069] Among them, k = 2, 3,... n, representing the number of failure probability densities greater than 1, Ra Denote x a Greater than x b The number of failure probability density, S, for (1 ≤ a ≤ b) k Denote x a Greater than x b The cumulative number of failure probability density for (1 ≤ a ≤ b), where a and b represent the sequence numbers of failure probability density greater than 1.

[0070] Assume that the failure probability density sequence is randomly independent, then the statistic UF k Is as follows:

[0071]

[0072] Where, when k = 1, UF1 = 0. Var(S k ) and E(S k ) are the variance and mean of S k respectively. When the failure probability density sequence is mutually independent and has the same continuous distribution, it can be calculated by the following:

[0073]

[0074] UF a Is a sequence of statistics calculated successively according to the failure probability density sequence X = (x1, x2, x3,..., x n ). In this application, take α = 0.05, and the corresponding confidence interval is ±1.96. If UF k Exceeds the confidence interval, it indicates that there is a very obvious change trend in the failure probability density sequence. Then, according to the inverse sequence x n of the failure probability density sequence, x n-1 , x n-2 ,..., x1, and repeat the above process to obtain the corresponding UF k , UB k As an intermediate variable and such that:

[0075] UB k = -UF k k = n, n - 1,..., 1.

[0076] Finally, draw the curve graphs of UF k and UB k , as Figure 5 shown. In the figure, the UF curve is at the minimum value at time t1, and its value is less than zero, indicating that at time t1, the probability density curve is at a turning point of a downward trend change. Combining Figure 6 shows that t1 is very close to the end point of the early failure stage. At time t2, the UF curve crosses the confidence interval, indicating that at time t2, the upward trend of the probability density curve is significantly enhanced. CombiningFigure 6 It can be seen that t2 is very close to the end point of the intermediate fault stage.

[0077] From Figure 6 It can be seen that the entire probability density curve presents an atypical bathtub curve shape. After the Mann-Kendall trend test, the corrected time points t1 and t2 divide the probability density curve into three stages: the early fault stage, the accidental fault stage, and the wear-out fault stage. In the early fault stage, that is, the time period before t1, due to defects in the design, manufacturing, assembly, and transportation processes of complex mechatronic equipment, early faults occur relatively frequently, and the probability density curve shows a rapid downward trend. In the time period between t1 and t2, the fault probability density curve in this stage shows a slow upward trend with time, and the complex mechatronic equipment enters the accidental fault stage with relatively stable fault intensity. In the time period after t2, the mechatronic equipment enters the wear-out fault stage, and as time goes by, the components of the mechatronic equipment gradually wear and age, and the fault intensity will show an increasing trend again.

[0078] Based on the same inventive concept, as Figure 7 shown, an embodiment of the present application further provides a state evaluation system for complex mechatronic equipment, and the state evaluation system for complex mechatronic equipment includes:

[0079] A fault time data module 701, configured to obtain fault time data during the operation of complex mechatronic equipment; the fault time data includes the accumulated number of faults and the time corresponding to each number of faults.

[0080] An accumulated fault function acquisition module 702, connected to the fault time data module 701, configured to obtain an accumulated fault function based on the fault time data.

[0081] A fault probability density function acquisition module 703, connected to the accumulated fault function acquisition module 702, configured to take the derivative of the accumulated fault function to obtain a fault probability density function.

[0082] A fault probability density curve acquisition module 704, connected to the fault probability density function acquisition module 703, configured to obtain a fault probability density curve based on the fault probability density function; the abscissa of the fault probability density curve is time, and the ordinate is the fault probability density.

[0083] A complex mechatronic equipment evaluation module 705, connected to the fault probability density curve acquisition module 704, configured to analyze the trend of the fault probability density curve to evaluate the current state of the complex mechatronic equipment.

[0084] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structural diagram can be as shown in Figure 8 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store fault time data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for evaluating the state of a complex electromechanical device.

[0085] Those skilled in the art can understand that Figure 8 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the above-mentioned method for evaluating the state of a complex electromechanical device.

[0086] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, it implements the above-mentioned method for evaluating the state of a complex electromechanical device.

[0087] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, it implements the above-mentioned method for evaluating the state of a complex electromechanical device.

[0088] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0089] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0090] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0091] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0092] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for evaluating the condition of complex electromechanical equipment, characterized in that: The state assessment method of the complex electromechanical equipment comprises: Obtaining fault time data during the operation of complex electromechanical equipment; the fault time data includes the accumulated number of faults and the time corresponding to each fault number; Based on the failure time data, a cumulative failure function is obtained; Derivative the cumulative fault function to obtain a fault probability density function; Based on the fault probability density function, a fault probability density curve is obtained; the abscissa of the fault probability density curve is time, and the ordinate is the fault probability density; The trend of the fault probability density curve is analyzed to evaluate the current status of the complex electromechanical equipment.

2. The state assessment method of complex electromechanical equipment according to claim 1, characterized in that: Based on the failure time data, a cumulative failure function is obtained, which specifically includes: Performing linear fitting on the fault time data to obtain a cumulative fault fitting curve; the abscissa of the cumulative fault fitting curve is time, and the ordinate is the cumulative number of faults; According to the cumulative fault fitting curve, a cumulative fault function is obtained.

3. The state assessment method of complex electromechanical equipment according to claim 1, characterized in that: The cumulative fault function is: W(t)=p1·t 6 +p2·t 5 +p3·t 4 +p4·t 3 +p5·t 2 +p6·t+p7; Wherein, W(t) is the accumulated number of failures, p1, p2, p3, p4, p5, p6 and p7 are constants with different values, and t is time.

4. The state assessment method of complex electromechanical equipment according to claim 1, characterized in that: The fault probability density function is: f(t)=6·p1·t 5 +5·p2·t 4 +4·p3·t 3 +3·p4·t 2 +2·p5·t+p6; Wherein, f(t) is the failure probability density, p1, p2, p3, p4, p5 and p6 are constants with different values, and t is time.

5. The method for evaluating the state of complex electromechanical equipment according to claim 1, characterized in that: The trend of the fault probability density curve is analyzed to evaluate the current state of the complex electromechanical equipment, specifically including: using the Mann-Kendall trend analysis method and the Mann-Kendall mutation detection method to analyze the trend of the fault probability density curve to evaluate the current state of the complex electromechanical equipment.

6. The method for evaluating the state of complex electromechanical equipment according to claim 1, characterized in that: The current state of the complex electromechanical equipment is an early failure period, an accidental failure period, or a loss failure period; If the fault probability density curve shows a downward trend over time, the current state of the complex electromechanical equipment is in the early failure stage; If the fault probability density curve shows an upward trend over time and the rate of change is less than or equal to the set value, the current state of the complex electromechanical equipment is an accidental fault period; If the fault probability density curve shows an upward trend over time and the rate of change is greater than a set value, the current state of the complex electromechanical equipment is a wear and tear failure period.

7. A complex electromechanical equipment status assessment system, using the complex electromechanical equipment status assessment method according to any one of claims 1 to 6, characterized in that: The complex electromechanical equipment condition assessment system comprises: A fault time data module is used to obtain fault time data during the operation of complex electromechanical equipment; the fault time data includes the accumulated number of faults and the time corresponding to each fault number; A cumulative fault function acquisition module, connected to the fault time data module, for obtaining a cumulative fault function based on the fault time data; A fault probability density function acquisition module, connected to the cumulative fault function acquisition module, is used to derive the cumulative fault function to obtain a fault probability density function; A fault probability density curve acquisition module is connected to the fault probability density function acquisition module and is used to obtain a fault probability density curve based on the fault probability density function; the horizontal coordinate of the fault probability density curve is time, and the vertical coordinate is the fault probability density; The complex electromechanical equipment evaluation module is connected to the fault probability density curve acquisition module and is used to analyze the trend of the fault probability density curve to evaluate the current state of the complex electromechanical equipment.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the state assessment method for a complex electromechanical device according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the state of a complex electromechanical device according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating the state of a complex electromechanical device according to any one of claims 1 to 6 is implemented.

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