A method and system for risk assessment of failure modes of thrust bearing based on fault tree

Abstract

The application discloses a kind of based on fault tree's thrust bearing failure mode risk assessment method and system, first, by introducing the way of feedback link to establish considering failure correlation thrust bearing fault tree, clarify the occurrence of each failure mode context, and to thrust bearing failure bottom event is relative probability assignment;Second, based on fault tree and bottom event relative probability completes the relative frequency representation of thrust bearing failure mode, considers system influence, failure deterioration and failure diffusion nature to the severity of thrust bearing failure mode is layered evaluation representation, by detectable means analysis is carried out to the detectability index representation of thrust bearing failure mode;Finally, three indexes are combined to calculate thrust bearing comprehensive risk priority coefficient, and the comprehensive risk of thrust bearing failure mode is evaluated and sorted.This application method does not depend on the number of fault data, realizes comprehensive failure mode coupling reasoning, provides basis for the safety evaluation and design, maintenance decision of large thrust bearing.

Description

Technical Field

[0001] This invention belongs to the field of thrust bearing failure mode risk assessment technology, specifically relating to a thrust bearing failure mode risk assessment method and system based on fault tree. Background Technology

[0002] Thrust bearings are crucial load-bearing components in large hydropower and nuclear power generating units. Under complex operating conditions, thrust bearing failure often leads to downtime. Statistics show that thrust bearing failure accounts for approximately 40% of all hydropower unit failures [ZHAI Liming, LUO Yongyao, WANG Zhengwei, et al. A review on the large tilting pad thrust bearings in the hydropower units[J]. Renewable and Sustainable Energy Reviews, 2016, 691182-1198.]. Therefore, thrust bearing reliability has received considerable attention. Research on improving thrust bearing reliability mainly focuses on lubrication calculations, structural optimization, experimental analysis, and fault detection methods. Although these studies have significantly reduced the probability of thrust bearing failure through structural improvements and condition monitoring, the scarcity of bearing failure data under low failure probabilities makes intelligent failure assessment difficult, hindering accurate maintenance decisions during manual inspections. With the continuous increase in installed capacity of major projects such as Hualong and Baihetan, the reliability assessment system for large thrust bearings has become a technical bottleneck in my country's "smart hydropower" strategy.

[0003] Failures in thrust bearings typically present with abnormal temperatures as a warning sign and wear or burning of the bearing shell or mirror plate as outward manifestations. However, their underlying failure mechanisms and causes are diverse. Existing research focuses on post-failure attribution analysis, concentrating on single failure modes. While these studies have identified correlations between bearing failure modes, they have not developed risk assessment methods that consider such coupling relationships. FMEA is a typical method for analyzing the causes and impacts of potential failure modes in a system. It evaluates failure modes in terms of frequency, severity, and detectability, calculates the risk priority number for each failure mode, and uses the final risk priority number to assess the risk of the failure mode. However, under the conditions of data scarcity and failure coupling, the failure mode risk assessment of thrust bearings faces the following challenges: First, there is coupling between failure modes of thrust bearings, and the coupling relationship between various failure modes lacks a unified expression; second, the lack of failure data and the intertwining of failure modes make it difficult to accurately count the failure frequency, leading to FMEA analysis relying excessively on expert experience [MUZAKKIR SM, LIJESH KP, HIRANI H. Failure mode and effect analysis of journal bearing[J]. International Journal of Applied Engineering Research, 2015, 10(16):37752-37759.]; finally, FMEA analysis only assesses the severity of a single type of failure and cannot cope with the situation of multiple failure modes coupled in thrust bearings. Summary of the Invention

[0004] This invention provides a fault tree-based method and system for risk assessment of thrust bearing failure modes, which addresses the limitations imposed by data scarcity and failure mode coupling on comprehensive risk assessment technology for thrust bearing failure modes.

[0005] The present invention adopts the following technical solution:

[0006] A fault tree-based method for assessing the failure modes of thrust bearings includes the following steps:

[0007] S1. Establish a thrust bearing fault tree for failure mode risk assessment that considers failure associations, and express the feedback impact of events in the form of a feedback loop; assign relative probability values ​​to the thrust bearing failure events in the fault tree.

[0008] S2. Based on the relative probability of the bottom event and the fault tree structure of the thrust bearing, complete the relative frequency characterization of the thrust bearing failure modes; through a hierarchical evaluation method, consider the failure deterioration and failure propagation properties to characterize the severity of the thrust bearing failure modes; analyze the failure detection methods for the failure modes in the thrust bearing fault tree in step S1, and obtain the detectability index of each failure mode of the thrust bearing.

[0009] S3. Based on the relative frequency, severity, and detectability indices characterized in step S2, solve the comprehensive risk priority coefficient of the thrust bearing failure modes and perform risk priority ranking to realize the risk assessment of thrust bearing failure modes based on fault tree.

[0010] Preferably, step S1 specifically includes:

[0011] S101. Establish a thrust bearing fault tree for failure mode risk assessment;

[0012] S102. A feedback loop is introduced to express the feedback effect of the thrust bearing failure mode. The bottom event of the introduced feedback loop corresponds one-to-one with the failure mode to express the feedback influence of the thrust bearing failure mode.

[0013] S103. Through survey and statistics, the frequency of occurrence of each thrust bearing failure event in the case was ranked, and relative probability values ​​were assigned to the thrust bearing failure events based on the ranking results. relative probability for:

[0014]

[0015] in, The coefficient is assigned to the probability; P is the baseline value of the unknown probability.

[0016] Preferably, step S2 specifically includes:

[0017] S201. Solve the initial relative probability of the top event by combining the initial fault tree structure of the thrust bearing, assign probability values ​​to the bottom events of the feedback loop, and solve the updated relative probability of the top event of the thrust bearing failure.

[0018] S202. Based on the probability assignment of the bottom event of the feedback loop, solve for the probability importance index I of the bottom event of the feedback loop to characterize the failure deterioration property of the thrust bearing; calculate the number of times each failure mode occurs as an intermediate event M to characterize the failure diffusion property of the thrust bearing.

[0019] S203. Frequency characterization is performed based on the updated relative probability ranking of the top failure event of the thrust bearing; the severity of the thrust bearing failure mode is assessed in layers, and the severity of the thrust bearing is comprehensively characterized by combining indicators I and M in each layer; failure detection methods are analyzed for the failure modes in the thrust bearing fault tree to obtain the detectability index of the thrust bearing failure mode.

[0020] Preferably, in step S201, the initial relative probability of the thrust bearing failure mode represented by the top event is... The formula for calculating the relative probability of the update is:

[0021]

[0022] Where k is the fault tree The minimum number of cut sets; Fault tree The j-th minimum cut set;

[0023] The probability of the bottom event in the feedback loop is assigned as follows:

[0024]

[0025] in, To provide the relative probability of events at the bottom of the feedback loop. This represents the initial probability of the failure mode corresponding to the bottom event of the feedback loop.

[0026] Preferably, in step S202, the probability importance index of the bottom event Yi in the feedback loop is... for:

[0027]

[0028] Where i = 1, 2, ..., 7; To provide the probability importance of the bottom event Yi in the fault tree treek; Let Fk be the probability of the top event Fk occurring when the feedback loop Yi occurs. Let Fk be the probability of the top event Fk occurring when the feedback loop Yi does not occur.

[0029] Preferably, in step S203, the method for characterizing the relative frequency of thrust bearing failure modes is as follows:

[0030] The frequency of the failure mode with the lowest relative probability is updated to 1, the frequency of the highest failure mode is 10, and the frequency of the failure modes in the middle position is represented by arithmetic progression between 1 and 10 according to the ranking.

[0031] Preferably, in step S203, the method for stratifying the severity of the thrust bearing failure modes is as follows:

[0032] The severity of thrust bearing failure modes is divided into high-risk failure level, medium-risk failure level, and low-risk failure level. High-risk failure means that the failure occurs and directly causes system-level failure, threatens personnel safety, or leads to loss of bearing function. Medium-risk failure means that the failure occurs and leads to loss of bearing function. Low-risk failure means that the failure occurs and leads to loss of bearing function through secondary failures.

[0033] Within each layer, a secondary evaluation is performed considering the failure degradation and failure propagation properties. The failure degradation property of the thrust bearing is characterized by the probability importance index of the feedback loop, which is used as index I; the failure propagation property of the thrust bearing is characterized by the number of times each failure mode occurs as an intermediate event, which is used as index M.

[0034] The average of the rankings of the I and M indicators is taken as the final ranking of the severity index in the secondary evaluation. Combining the severity stratification results and the severity ranking within each stratum, the comprehensive ranking of the thrust bearing severity is obtained. Based on the comprehensive ranking results, the failure mode severity index is comprehensively assigned a value in the range of 1-10.

[0035] Preferably, in step S203, the detectability index of the thrust bearing is divided into 1-10 levels, with the detection difficulty increasing progressively from level 1 to level 10. Level 1 represents 100% detection when failure occurs, and level 10 represents undetectable failure when failure occurs.

[0036] Preferably, in step S3, the comprehensive risk priority coefficient of the thrust bearing failure mode is... for:

[0037]

[0038] in, , , These are the thrust bearing frequency, severity, and detectability indicators, respectively.

[0039] Secondly, embodiments of the present invention provide a fault tree-based thrust bearing failure mode risk assessment system, including:

[0040] The reference module establishes a thrust bearing fault tree for failure mode risk assessment that considers failure associations, and expresses the feedback impact of events in the form of a feedback loop; relative probability values ​​are assigned to the thrust bearing failure base events in the fault tree.

[0041] The characterization module, based on the relative probability of bottom events and the fault tree structure of the thrust bearing, completes the relative frequency characterization of the thrust bearing failure modes; through a hierarchical evaluation method, it considers the failure deterioration and failure propagation properties to characterize the severity of the thrust bearing failure modes; and it analyzes the failure modes in the thrust bearing fault tree of the reference module to obtain the detectability index of each failure mode of the thrust bearing.

[0042] The evaluation module, based on the relative frequency, severity, and detectability indices represented by the characterization module, solves the comprehensive risk priority coefficient of the thrust bearing failure modes and performs risk priority ranking to achieve risk assessment of thrust bearing failure modes based on fault tree.

[0043] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described fault tree-based thrust bearing failure mode risk assessment method.

[0044] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described fault tree-based thrust bearing failure mode risk assessment method.

[0045] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described fault tree-based thrust bearing failure mode risk assessment method.

[0046] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described fault tree-based thrust bearing failure mode risk assessment method.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects:

[0048] A fault tree-based risk assessment method for thrust bearing failure modes solves the problems of failure mode coupling and data scarcity constraints faced by traditional FMEA applications in the field of thrust bearings by establishing a fault tree for thrust bearings that considers failure associations, a frequency characterization method based on relative probability assignment, and a severity hierarchical assessment method. It reduces the subjectivity of assessment results and realizes a comprehensive risk assessment of failure modes of large thrust bearings by solving and ranking risk priority coefficients.

[0049] Furthermore, by establishing a fault tree with feedback loop correction, the causes and correlations of thrust bearing failure modes are analyzed, providing an analytical basis for risk assessment of thrust bearing failure modes that considers failure coupling.

[0050] Furthermore, frequency index representation is achieved based on survey statistics and the assignment of relative probabilities of basic events. Relative probability assignment replaces absolute failure probabilities, overcoming the constraint of insufficient data on thrust bearing FMEA. A hierarchical evaluation method is adopted to represent severity index, considering both the systemic and local impacts of failure modes. This compensates for the shortcomings of existing FMEA methods in considering failure correlation and establishes a unified evaluation process.

[0051] Furthermore, by combining frequency, severity, and detectability indicators, a comprehensive risk assessment of common failure modes of thrust bearings was completed through the solution and ranking of risk coefficient priority numbers.

[0052] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0053] In summary, this invention can effectively reduce the subjectivity of traditional FMEA in the case of insufficient data and strong coupling, and improve the reliability of the results. It provides a method for risk assessment of thrust bearing failure modes that does not depend on the amount of fault data and integrates failure mode coupling reasoning, which is of great significance for the failure assessment, design and maintenance of thrust bearings.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 The initial fault tree structure for the thrust bearing overheating;

[0057] Figure 2 The fault tree structure was updated to address the issue of excessive thrust bearing temperature.

[0058] Figure 3 A schematic diagram of a severity stratification assessment method;

[0059] Figure 4 This is a comparison chart of the results of this invention and the results of traditional methods;

[0060] Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0061] Figure 6This is a block diagram of an electronic device according to an embodiment of the present invention;

[0062] Figure 7 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0065] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0066] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0067] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0068] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0069] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0070] This invention provides a fault tree-based method for risk assessment of thrust bearing failure modes. First, a fault tree considering failure correlations is established by introducing a feedback loop to clarify the occurrence path of each failure mode, and relative probabilities are assigned to the underlying events of thrust bearing failure. Second, based on the fault tree and the relative probabilities of the underlying events, the relative frequency of thrust bearing failure modes is characterized. The severity of thrust bearing failure modes is assessed hierarchically, considering system impact, failure deterioration, and failure propagation properties. The detectability index of thrust bearing failure modes is characterized through detectability analysis. Finally, the comprehensive risk priority coefficient of the thrust bearing is calculated by combining the three indicators, and the thrust bearing failure modes are ranked according to comprehensive risk assessment. This method does not rely on the amount of fault data and achieves coupled reasoning of comprehensive failure modes, providing a basis for safety assessment, design, and maintenance decisions for large thrust bearings.

[0071] This invention provides a method for risk assessment of thrust bearing failure modes that does not rely on the amount of fault data and integrates failure mode coupling reasoning.

[0072] Example 1

[0073] Please see Figure 7 The present invention provides a fault tree-based method for risk assessment of thrust bearing failure modes, comprising the following steps:

[0074] S1. Establish a thrust bearing fault tree for failure mode risk assessment that considers failure associations, and express the feedback impact of events in the form of a feedback loop; assign relative probability values ​​to the thrust bearing failure events in the fault tree.

[0075] Step S1 is as follows:

[0076] S101. Establish a thrust bearing fault tree for failure mode risk assessment;

[0077] A fault tree for thrust bearings was established based on a case study of thrust bearing failures. Common failure modes of thrust bearings were selected as top events, and a fault tree considering the correlation of failures was established based on the reasoning of the causes of failure modes in the cases.

[0078] S102. Introduce a feedback loop to express the feedback effect of the thrust bearing failure mode;

[0079] The introduced feedback loop bottom events correspond one-to-one with the failure modes to express the feedback effects of the thrust bearing failure modes.

[0080] S103. Through survey and statistics, the frequency of occurrence of each thrust bearing failure event in the case was ranked, and relative probability values ​​were assigned to the thrust bearing failure events based on the ranking results. relative probability for:

[0081]

[0082] in, The coefficients are assigned to the probabilities, with the lowest relative probability having a coefficient of 1, the highest being a multiple of the highest and lowest counts obtained statistically, and the intermediate values ​​between 1 and the highest value being assigned arithmetic progressions based on the order; P is the baseline value for the unknown probability, where 0 < P < 1.

[0083] S2. Based on the relative probability of bottom events and the fault tree structure of the thrust bearing, the relative frequency characterization of the thrust bearing failure modes is completed; through hierarchical evaluation, the severity characterization of the thrust bearing failure modes is carried out by considering the failure deterioration and failure propagation properties; failure detection methods are analyzed for the failure modes in the thrust bearing fault tree in S1, and the detectability index of each failure mode of the thrust bearing is obtained.

[0084] Step S2 is as follows:

[0085] S201. Solve the initial relative probability of the top event by combining the initial fault tree structure of the thrust bearing, assign probability values ​​to the bottom events of the feedback loop, and solve the updated relative probability of the top event of the thrust bearing failure.

[0086] Initial relative probability of thrust bearing failure modes represented by the top event The formula for calculating the relative probability of the update is:

[0087]

[0088] Where k is the fault tree The minimum number of cut sets; Fault tree The j-th minimum cut set.

[0089] The formula for assigning probability values ​​to the bottom event of the feedback loop is as shown in equation (3). Let (i=1,2,…,7), then:

[0090]

[0091] In the formula, To provide the relative probability of events at the bottom of the feedback loop. This represents the initial probability of the failure mode corresponding to the bottom event of the feedback loop.

[0092] S202. Based on the probability assignment of the bottom event of the feedback loop, solve for the probability importance index I of the bottom event of the feedback loop to characterize the failure deterioration property of the thrust bearing; calculate the number of times each failure mode occurs as an intermediate event M to characterize the failure diffusion property of the thrust bearing.

[0093] The probability importance index of the bottom event Yi in the feedback loop for:

[0094]

[0095] Where i = 1, 2, ..., 7; To provide the probability importance of the bottom event Yi in the fault tree treek; Let Fk be the probability of the top event Fk occurring when the feedback loop Yi occurs. Let Fk be the probability of the top event Fk occurring when the feedback loop Yi does not occur.

[0096] S203. Frequency characterization is performed based on the updated relative probability ranking of the top failure event of the thrust bearing; the severity of the thrust bearing failure mode is assessed in layers, and the severity of the thrust bearing is comprehensively characterized by combining indicators I and M in each layer; failure detection methods are analyzed for the failure modes in the thrust bearing fault tree to obtain the detectability index of the thrust bearing failure mode.

[0097] The method for characterizing the relative frequency index of thrust bearing failure modes is as follows: the frequency of the failure mode with the lowest relative probability is updated to 1, the frequency of the highest relative probability is 10, and the frequency of the failure modes in the middle position is assigned arithmetic values ​​between 1 and 10 according to the ranking.

[0098] The severity stratification assessment method for thrust bearings is as follows: First, the severity of thrust bearing failure modes is divided into three levels: high-risk failure, medium-risk failure, and low-risk failure. High-risk failure means that once it occurs, it will directly cause system-level failure, threaten personnel safety, or lead to loss of bearing function; medium-risk failure means that after a certain degree of accumulation, it will lead to loss of bearing function; low-risk failure means that after it occurs, it will mainly lead to loss of bearing function through secondary failures.

[0099] Within each layer, the average of the rankings of the I and M indicators is taken as the final ranking of the severity index in the secondary evaluation. Combining the severity stratification results and the severity ranking within each layer, a comprehensive ranking of the thrust bearing severity is obtained. Based on the comprehensive ranking results, the failure mode severity index is comprehensively assigned a value in the range of 1-10.

[0100] The method for evaluating detectability index is as follows: the detectability index of thrust bearing is divided into ten levels from 1 to 10, where 1 represents the easiest to detect and 10 represents almost impossible to detect.

[0101]

[0102] S3. Based on the relative frequency, severity, and detectability indices characterized in step S2, solve the comprehensive risk priority coefficient of the thrust bearing failure modes and perform risk priority ranking to realize the risk assessment of thrust bearing failure modes based on fault tree.

[0103] Step S3 is as follows:

[0104] The comprehensive risk priority coefficients of thrust bearing failure modes are calculated, and their risks are ranked. The higher the ranking, the greater the relative risk of the thrust bearing failure modes. The key feature is the comprehensive risk priority coefficient of the thrust bearing. for:

[0105]

[0106] in, , , These are the thrust bearing frequency, severity, and detectability indicators, respectively.

[0107] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0108] Example 2

[0109] This invention provides a fault tree-based thrust bearing failure mode risk assessment system. This system can be used to implement the aforementioned fault tree-based thrust bearing failure mode risk assessment method. Specifically, the fault tree-based thrust bearing failure mode risk assessment system includes a feedback module, a characterization module, a hierarchical module, and an assessment module.

[0110] The reference module establishes a thrust bearing fault tree for failure mode risk assessment that considers failure associations, and expresses the feedback impact of events in the form of a feedback loop; it assigns relative probability values ​​to the thrust bearing failure events in the fault tree.

[0111] The characterization module, based on the relative probability of bottom events and the fault tree structure of the thrust bearing, completes the relative frequency characterization of the thrust bearing failure modes; through a hierarchical evaluation method, it considers the failure deterioration and failure propagation properties to characterize the severity of the thrust bearing failure modes; and it analyzes the failure modes in the thrust bearing fault tree of the reference module to obtain the detectability index of each failure mode of the thrust bearing.

[0112] The evaluation module, based on the relative frequency, severity, and detectability indices represented by the characterization module, solves the comprehensive risk priority coefficient of the thrust bearing failure modes and performs risk priority ranking to achieve risk assessment of thrust bearing failure modes based on fault tree.

[0113] Example 3

[0114] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used in the operation of a fault tree-based thrust bearing failure mode risk assessment method, including:

[0115] A thrust bearing fault tree, considering failure associations, is established for failure mode risk assessment, and the feedback impact of events is expressed in the form of a feedback loop. Relative probabilities are assigned to the base events of thrust bearing failure in the fault tree. Based on the relative probabilities of the base events and the structure of the thrust bearing fault tree, the relative frequency of thrust bearing failure modes is characterized. The severity of thrust bearing failure modes is characterized by considering failure deterioration and failure propagation properties through a hierarchical assessment approach. Failure detection methods are analyzed for the failure modes in the thrust bearing fault tree to obtain the detectability index of each failure mode. Based on the characterized relative frequency, severity, and detectability index, the comprehensive risk priority coefficient of the thrust bearing failure modes is calculated, and risk priority is ranked to achieve fault tree-based thrust bearing failure mode risk assessment.

[0116] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fault tree-based thrust bearing failure mode risk assessment method described in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the fault tree-based thrust bearing failure mode risk assessment system described in this embodiment. To avoid repetition, details are omitted here.

[0117] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0118] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0119] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0120] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0121] Please see Figure 6 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0122] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 7 The steps are shown in the figure.

[0123] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0124] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0125] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0126] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0127] Example 4

[0128] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0129] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0130] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0131] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the fault tree-based thrust bearing failure mode risk assessment method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0132] A thrust bearing fault tree, considering failure associations, is established for failure mode risk assessment, and the feedback impact of events is expressed in the form of a feedback loop. Relative probabilities are assigned to the base events of thrust bearing failure in the fault tree. Based on the relative probabilities of the base events and the structure of the thrust bearing fault tree, the relative frequency of thrust bearing failure modes is characterized. The severity of thrust bearing failure modes is characterized by considering failure deterioration and failure propagation properties through a hierarchical assessment approach. Failure detection methods are analyzed for the failure modes in the thrust bearing fault tree to obtain the detectability index of each failure mode. Based on the characterized relative frequency, severity, and detectability index, the comprehensive risk priority coefficient of the thrust bearing failure modes is calculated, and risk priority is ranked to achieve fault tree-based thrust bearing failure mode risk assessment.

[0133] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0134] Please see Figure 1 Taking excessively high thrust bearing temperature as an example, its failure causes include abnormal heat dissipation and abnormal heat generation. Abnormal heat dissipation includes cooling system failure and poor material quality affecting heat dissipation, while abnormal heat generation is mainly caused by abnormal wear of the bearing bush. Further analysis of these causes is conducted until the underlying events can no longer be further subdivided, i.e., until the bottom event is reached, thus obtaining the initial fault tree for excessively high thrust bearing temperature.

[0135] Please see Figure 2 By introducing a "feedback loop" bottom event, representing the reverse influence of the upper-level event, the positive feedback effect in the fault occurrence process is expressed, thereby correcting the initial fault tree. For example... Figure 2As shown, lubrication failure indirectly leads to overheating failure by causing wear. Once overheating occurs, it further exacerbates lubrication failure by affecting the physicochemical properties of the lubricating oil, thus establishing a positive feedback loop between overheating and lubrication failure. Oil film rupture leads to lubrication failure, and the resulting change in stress further hinders oil film formation. Furthermore, uneven stress leads to oil film rupture, causing wear, which in turn exacerbates uneven stress distribution. Figure 2 In the corrected fault tree shown, the above three feedback effects are expressed through the bottom events "overheating feedback" Y1, "wear feedback" Y3, and "lubrication failure feedback" Y2.

[0136] The relative probabilities of the bottom events of thrust bearing failure are assigned. The relative probability of the bottom event is composed of the relative probability coefficient and the unknown minimum probability base. Then, based on the established fault tree and the bottom event probability assignment, the relative probability of the top event is solved. The relative probabilities of the top events of thrust bearing failure are sorted. The frequency of the failure mode with the lowest ranking is 1, and the frequency of the failure mode with the highest ranking is 10. The frequencies of the failure modes in the middle ranking are represented by arithmetic progression between 1 and 10 according to the ranking.

[0137] Please see Figure 3 This study conducts the first severity assessment of various failure modes of thrust bearings from a system impact perspective. Failure modes are categorized into three levels: high-risk failure, medium-risk failure, and low-risk failure, with the severity decreasing sequentially. High-risk failure indicates that, once it occurs, it will directly trigger system-level failure, threaten personnel safety, or lead to bearing malfunction. Medium-risk failure indicates that, after a certain degree of accumulation, it will lead to bearing malfunction. Low-risk failure indicates that, after its occurrence, it mainly leads to bearing malfunction through secondary failures.

[0138] Within each layer, a secondary evaluation is conducted considering the failure degradation and failure propagation properties of the thrust bearing. The failure degradation property is characterized by the probability importance index of the feedback loop (index I); the failure propagation property is characterized by the number of times each failure mode of the thrust bearing occurs as an intermediate event (index M). The average of the rankings of the two indices I and M is taken as the final ranking of the severity index in the secondary evaluation.

[0139] Then, by combining the severity stratification results and the severity ranking within each stratum, a comprehensive ranking of the severity of the thrust bearing is obtained, and the severity index of the failure mode is comprehensively assigned a value in the range of 1-10 based on the comprehensive ranking results.

[0140] Finally, the detectability index was obtained through thrust bearing failure detection methods. The detectability index was divided into ten levels from 1 to 10, where 1 represents the easiest to detect and 10 represents almost impossible to detect. The comprehensive risk priority coefficient of the thrust bearing failure modes was calculated, and their risk priorities were ranked.

[0141] Please see Figure 4 To verify the effectiveness of this method, a questionnaire survey was conducted. The survey results were used to evaluate the failure modes of thrust bearings according to the traditional FMEA process, and the advantages and disadvantages of the traditional method and this method were verified and compared.

[0142] The results show that, in both methods, bearing wear (F3) is the failure mode with the highest overall risk coefficient, and the difference in ranking results is mostly within 1. However, in the traditional FMEA results, the ranking of support or positioning structure failure (F7) increases significantly, while the ranking of lubrication failure (F2) decreases significantly. In severity index analysis, traditional FMEA analyzes the independent consequences of individual failures. Therefore, based on the failure results, experts consider the severity of support or positioning structure failure to be second only to thrust disc fracture and lubrication failure. In contrast, this method considers both the overall impact of the failure and the mutual induction effects between other failure modes, resulting in a lower severity rating for support or positioning structure failure.

[0143] The expert scoring results show that the average score for the O index of lubrication failure is only 4.3, lower than that of bearing wear. In this method, the frequency of lubrication failure is second only to excessive temperature. Since bearing wear usually exhibits obvious macroscopic phenomena, and lubrication failure often appears as an intermediate process in failure mode coupling, it is more easily overlooked. Furthermore, the lack of failure statistics means that experts can only score the frequency based on their own experience, thus underestimating the frequency of lubrication failure. This method, by analyzing the occurrence process and interrelationships of failure modes, replaces the subjectivity of experience-based judgment with the objectivity of fault trees and case statistics, greatly improving the reliability of the results.

[0144] In summary, this invention presents a fault tree-based method and system for assessing the failure modes of thrust bearings. Addressing the challenge of conducting comprehensive risk assessments of large thrust bearings due to limited fault data and the coupling of failure modes, this invention, based on the fundamental purpose of FMEA analysis and combined with the basic principles of fault trees, proposes a thrust bearing failure mode risk assessment method that is independent of the amount of data and considers failure coupling. This provides a basis for the safety assessment, design, and maintenance decisions of large thrust bearings.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0147] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0148] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for risk assessment of failure modes of thrust bearings based on fault tree, characterized in that, The method comprises the following steps: S1, establishing a thrust bearing fault tree for failure mode risk assessment considering failure correlation, and expressing feedback effects of events in the form of feedback loops; assigning relative probabilities to bottom events of the thrust bearing failure in the fault tree; S2, based on the relative probability of the bottom event and the structure of the thrust bearing fault tree, the relative frequency of the thrust bearing failure mode is characterized; through hierarchical evaluation, the severity of the thrust bearing failure mode is characterized considering the failure aggravation and failure diffusion properties; the detectability index of each failure mode of the thrust bearing is obtained by analyzing the failure detection means of the failure mode in the thrust bearing fault tree of step S1, specifically: S201, combining the initial fault tree structure of the thrust bearing to solve the initial relative probability of the top event, assigning probabilities to the bottom events of the feedback loop, and solving the updated relative probability of the top event of the thrust bearing failure; S202, based on the probability assignment of the bottom event of the feedback loop, solving the probability importance index I of the bottom event of the feedback loop, which represents the failure aggravation property of the thrust bearing; calculating the number M of each failure mode as an intermediate event, which represents the failure diffusion property of the thrust bearing; S203, based on the updated relative probability ranking of the top event of the thrust bearing failure, the relative frequency is characterized; the severity of the thrust bearing failure mode is evaluated in layers, and the severity of the thrust bearing is characterized by combining the indexes I and M in each layer; the detectability index of the thrust bearing failure mode is obtained by analyzing the failure detection means of the failure mode in the thrust bearing fault tree; S3, based on the relative frequency, severity and detectability indexes characterized in step S2, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and the risk priority is sorted, realizing the thrust bearing failure mode risk assessment based on the fault tree.

2. The fault tree based thrust bearing failure mode risk assessment method of claim 1, wherein, Step S1 is specifically: S101, establishing a thrust bearing fault tree for failure mode risk assessment; S102, introducing a feedback loop to express the feedback effect of the thrust bearing failure mode, and the bottom event of the introduced feedback loop corresponds to the failure mode one by one to express the feedback effect of the thrust bearing failure mode; S103, through the investigation statistics, the occurrence frequency of each thrust bearing failure bottom event in the case is sorted, and the relative probability of the thrust bearing failure bottom event is valued based on the sorting result, the relative probability of the bottom event is: ​ wherein P is the probability value; P is the unknown probability reference value.

3. The fault tree based thrust bearing failure mode risk assessment method of claim 1, wherein, In step S201, the initial relative probability of the thrust bearing failure mode represented by the top event The solving formula for updating the relative probability is: wherein, is the unknown probability reference value, k is the number of minimal cut sets of the fault tree is the unknown probability reference value, k is the number of minimal cut sets of the fault tree is the jth minimal cut set of the fault tree​ The probability assignment of the bottom event of the feedback loop is as follows: wherein, is the relative probability of the feedback loop ending in the event.

4. The fault tree based thrust bearing failure mode risk assessment method of claim 1, wherein, In step S202, the probability importance index of the feedback loop bottom event Yi is fed back is: where i = 1, 2, …, 7; is the probability importance of the feedback loop bottom event Yi in the fault tree; is the probability of the top event Fk occurring when the feedback loop bottom event Yi occurs; is the probability of the top event Fk occurring when the feedback loop bottom event Yi does not occur.

5. The method of claim 1, wherein, In step S203, the relative frequency characterization method of the thrust bearing failure mode is: The frequency of the failure mode with the lowest updated relative probability ranking is 1, the frequency of the failure mode with the highest updated relative probability ranking is 10, and the frequency of the failure mode with the intermediate ranking is assigned by equal difference between 1 and 10.

6. The method of claim 1, wherein, In step S203, the hierarchical evaluation method of the severity of the thrust bearing failure mode is: The severity of the thrust bearing failure mode is divided into high-risk failure layer, medium-risk failure layer and low-risk failure layer; high-risk failure represents failure occurrence, which directly causes system-level failure, threatens personnel safety or leads to loss of bearing function; medium-risk failure represents failure occurrence, which leads to loss of bearing function; low-risk failure represents failure occurrence, which leads to loss of bearing function through secondary failure; In each layer, the secondary evaluation is conducted considering the failure aggravation property and the failure diffusion property, the probability importance index of the feedback loop is used to represent the failure aggravation property of the thrust bearing as the index I, and the number of occurrences of each failure mode as an intermediate event is used to represent the failure diffusion property of the thrust bearing as the index M; The average of the rankings of the two indexes I and M is taken as the final ranking of the secondary evaluation of the severity index, the comprehensive ranking of the severity of the thrust bearing is obtained combining the severity ranking of each layer and the severity ranking in each layer, and the comprehensive assignment of the severity index of the failure mode is performed in the range of 1-10 according to the comprehensive ranking result.

7. The method of claim 1, wherein, In step S203, the detectability index of each failure mode of the thrust bearing is: The detectability index of the thrust bearing is divided into 1-10 levels, and the detection difficulty increases from level 1 to level 10, and 1 represents that the failure is detected 100% when it occurs, and 10 represents that the failure cannot be detected when it occurs.

8. The fault tree based thrust bearing failure mode risk assessment method of claim 1, wherein, In step S3, the comprehensive risk priority factor of the thrust bearing failure mode is calculated as: is: wherein, , , are the relative frequency, severity and detectability indices of the thrust bearing failure mode, respectively.

9. A fault tree based thrust bearing failure mode risk assessment system, characterized by, It includes: The reference module establishes a thrust bearing fault tree for failure mode risk assessment considering failure correlation, and expresses the feedback effect of the event in the form of a feedback loop; the relative probability of the thrust bearing failure bottom event in the fault tree is assigned; The representation module represents the relative frequency of the thrust bearing failure mode based on the relative probability of the bottom event and the structure of the thrust bearing fault tree; the severity of the thrust bearing failure mode is represented by considering the failure aggravation and the failure diffusion property through layered evaluation; the detectability index of each failure mode of the thrust bearing is obtained by analyzing the failure detection means of the failure mode in the thrust bearing fault tree of the reference module, which is: S201, combining the initial fault tree structure of the thrust bearing to solve the initial relative probability of the top event, assigning the probability of the feedback loop bottom event, and solving the updated relative probability of the thrust bearing failure top event; S202, based on the probability assignment of the feedback loop bottom event, solving the probability importance index I of the feedback loop bottom event, representing the failure aggravation property of the thrust bearing; calculating the number of occurrences of each failure mode as an intermediate event M, representing the failure diffusion property of the thrust bearing; S203, based on the updated relative probability ranking of the thrust bearing failure top event, the relative frequency is represented; the severity of the thrust bearing failure mode is evaluated layer by layer, and the severity of the thrust bearing is represented in each layer combining the indexes I and M; the detectability index of the thrust bearing failure mode is obtained by analyzing the failure detection means of the failure mode in the thrust bearing fault tree; The evaluation module solves the comprehensive risk priority coefficient of the thrust bearing failure mode based on the relative frequency, severity and detectability index represented by the representation module, and performs risk priority sorting, realizing the thrust bearing failure mode risk assessment based on the fault tree.

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