Thrust bearing failure mode risk assessment method and system based on fault tree

Abstract

The invention discloses a thrust bearing failure mode risk assessment method and system based on a fault tree, and the method comprises the steps: firstly, building a thrust bearing fault tree considering failure relevance through a mode of introducing a feedback link, sorting out the occurrence context of each failure mode, and carrying out the relative probability assignment of a thrust bearing failure bottom event; secondly, the relative frequency characterization of the thrust bearing failure mode is completed based on the relative probability of a fault tree and a bottom event, the severity of the thrust bearing failure mode is subjected to hierarchical evaluation characterization by considering the system influence, failure deterioration and failure diffusion properties, and the detectable index characterization of the thrust bearing failure mode is carried out through the analysis of a detectable means; and finally, calculating the comprehensive risk priority coefficient of the thrust bearing in combination with the three indexes, and performing comprehensive risk assessment sorting on the failure modes of the thrust bearing. The method does not depend on the number of fault data, comprehensive failure mode coupling reasoning is achieved, and a basis is provided for safety evaluation, design and maintenance decision making of the large thrust bearing.

Description

Background Art

[0002] Thrust bearings are important load-bearing components in large hydropower and nuclear power generating units. Under complex operating conditions, failure of thrust bearings often causes downtime accidents. According to statistics, failures of hydropower units caused by thrust bearing failure account for about 40% of the total 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.], so the reliability of thrust bearings has attracted much attention. Research on improving the reliability of thrust bearings mainly focuses on lubrication calculation, structural optimization, test analysis, and fault detection methods. Although these studies have greatly reduced the failure probability of thrust bearings through structural improvement and condition monitoring, the lack of bearing failure data under low failure probability makes it difficult to intelligentize failure assessment, making it impossible to make accurate operation and maintenance decisions during manual maintenance. With the continuous increase in the installed capacity of major projects such as Hualong and Baihetan, the reliability assessment system of large thrust bearings has become a technical bottleneck for my country's "smart hydropower" strategy.

[0003] Failure of thrust bearings during operation is usually warned by abnormal temperature and manifested by wear and burning of bearing shells or mirror plates, but the internal failure mechanisms and causes of failure are varied. Existing studies have focused on the traceability analysis after the failure occurs, focusing on a single failure mode. Such studies have also found a correlation between bearing failure modes, but have not formed a risk assessment method that considers such coupling relationships. FMEA is a typical method for analyzing the causes and effects of potential failure modes of a system. It can calculate the risk priority number of each failure mode by evaluating the failure mode in terms of three risk indicators: frequency, severity, and detectability, and implement risk assessment of the failure mode through the size of the final risk priority number. However, in the case of lack of data and failure coupling, the failure mode risk assessment of thrust bearings faces the following difficulties: first, there is coupling between the failure modes of thrust bearings, and the coupling relationship between the failure modes lacks a unified expression; second, there is a lack of thrust bearing failure data and the failure modes are intertwined, making it difficult to accurately count the failure frequency, resulting in FMEA analysis being overly dependent 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 evaluates the severity of a single type of failure and cannot cope with the coupling of multiple failure modes in thrust bearings. Summary of the invention

[0004] The present invention provides a method and system for risk assessment of failure modes of thrust bearings based on a fault tree, which are used to solve the problem that data scarcity and failure mode coupling restrict the comprehensive risk assessment technology of failure modes of thrust bearings.

[0005] The present invention adopts the following technical solutions: A method for risk assessment of failure modes of thrust bearings based on a fault tree comprises the following steps: S1. Establish a thrust bearing fault tree for failure mode risk assessment that takes failure association into consideration, and express the feedback impact of events in the form of feedback loops; assign relative probabilities to the thrust bearing failure bottom events in the fault tree; S2. Based on the relative probability of the bottom event and the thrust bearing fault tree structure, complete the relative frequency characterization of the thrust bearing failure mode; through a hierarchical evaluation method, consider the failure deterioration and failure diffusion properties to characterize the severity of the thrust bearing failure mode; analyze the failure detection means for the failure mode in the thrust bearing fault tree in step S1, and obtain the detectability index of each failure mode of the thrust bearing; S3. Based on the relative frequency, severity and detectability indicators characterized by step S2, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and risk priority is sorted to achieve the risk assessment of the thrust bearing failure mode based on the fault tree.

[0006] Preferably, step S1 specifically includes: S101. Establish 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, wherein the bottom event of the introduced feedback loop corresponds to the failure mode one by one to express the feedback influence of the thrust bearing failure mode; S103. Through investigation and statistics, the frequency of occurrence of each thrust bearing failure bottom event in the case is ranked, and based on the ranking results, the relative probability of the thrust bearing failure bottom event is assigned. The relative probability for:

[0007] in, is the probability assignment coefficient; P is the unknown probability reference value.

[0008] Preferably, step S2 specifically includes: S201, solving the initial relative probability of the top event in combination with the initial fault tree structure of the thrust bearing, 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 feedback loop bottom event, solve the probability importance index I of the feedback loop bottom event to characterize the failure deterioration nature of the thrust bearing; calculate the number of times M that each failure mode appears as an intermediate event to characterize the failure diffusion nature of the thrust bearing; S203. Perform frequency characterization based on the updated relative probability ranking of the top events of the thrust bearing failure; perform a hierarchical assessment of the severity of the thrust bearing failure mode, and comprehensively characterize the severity of the thrust bearing by combining indicators I and M in each layer; perform failure detection means analysis on the failure modes in the thrust bearing fault tree, and obtain detectability indicators of the thrust bearing failure modes.

[0009] Preferably, in step S201, the initial relative probability of the thrust bearing failure mode represented by the top event is The solution formula for updating the relative probability is:

[0010] Where k is the fault tree The minimum number of cut sets; Fault Tree The jth minimum cut set; The probability of the feedback loop bottom event is assigned as follows:

[0011] in, is the relative probability of the bottom event of the feedback loop, is the initial probability of the failure mode corresponding to the bottom event of the feedback loop.

[0012] Preferably, in step S202, the probability importance index of the feedback loop bottom event Yi is for:

[0013] Where i=1, 2, …, 7; is the probability importance of the feedback loop bottom event Yi in the fault tree treek; is the probability of the top event Fk occurring when the feedback loop Yi occurs; is the probability of the top event Fk occurring when the feedback loop Yi does not occur.

[0014] Preferably, in step S203, the method for characterizing the relative frequency of the thrust bearing failure mode is: The failure mode frequency with the lowest update relative probability ranking is 1, the highest failure mode frequency is 10, and the failure mode frequencies in the middle are represented by arithmetic progression between 1 and 10 according to the ranking.

[0015] Preferably, in step S203, the thrust bearing failure mode severity hierarchical assessment method is: The severity of thrust bearing failure modes is divided into high-risk failure layer, medium-risk failure layer, and low-risk failure layer; high-risk failure means that failure occurs, directly causing system-level failure, threatening personnel safety or causing loss of bearing function; medium-risk failure means that after failure occurs, the bearing function is lost; low-risk failure means that after failure occurs, secondary failure causes loss of bearing function; In each layer, the failure deterioration property and failure diffusion property are considered for secondary evaluation. The failure deterioration property of the thrust bearing is characterized by the probability importance index of the feedback loop as index I; the failure diffusion property of the thrust bearing is characterized by the number of times each failure mode appears as an intermediate event as index M; The average of the rankings of the I and M indicators is taken as the final ranking of the secondary evaluation of the severity index. The comprehensive ranking of the thrust bearing severity is obtained by combining the severity stratification results and the severity ranking within each layer. Based on the comprehensive ranking results, the failure mode severity index is comprehensively assigned a value within the range of 1-10.

[0016] Preferably, in step S203, the thrust bearing detectability index is divided into levels 1-10, and the detection difficulty increases step by step from level 1 to level 10, 1 represents 100% detection when failure occurs, and 10 represents no detection when failure occurs.

[0017] Preferably, in step S3, the comprehensive risk priority coefficient of the thrust bearing failure mode is for:

[0018] in, , , They are the frequency, severity and detectability indicators of thrust bearings respectively.

[0019] In a second aspect, an embodiment of the present invention provides a thrust bearing failure mode risk assessment system based on a fault tree, comprising: Reference module, establish a thrust bearing fault tree for failure mode risk assessment and considering failure association, and express the feedback impact of events in the form of feedback loops; assign relative probabilities to the thrust bearing failure bottom events in the fault tree; The characterization module completes the relative frequency characterization of the thrust bearing failure mode based on the relative probability of the bottom event and the thrust bearing fault tree structure; the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties through a hierarchical evaluation method; the failure detection means are analyzed for the failure modes in the reference module thrust bearing fault tree to obtain the detectability index of each failure mode of the thrust bearing; The evaluation module solves the comprehensive risk priority coefficient of the thrust bearing failure mode based on the relative frequency, severity and detectability indicators represented by the characterization module, and performs risk priority sorting to realize the risk assessment of the thrust bearing failure mode based on the fault tree.

[0020] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned fault tree-based thrust bearing failure mode risk assessment method when executing the computer program.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned fault tree-based thrust bearing failure mode risk assessment method.

[0022] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned fault tree-based thrust bearing failure mode risk assessment method when executing the computer program.

[0023] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned fault tree-based thrust bearing failure mode risk assessment method.

[0024] Compared with the prior art, the present invention has at least the following beneficial effects: A fault tree-based thrust bearing failure mode risk assessment method solves the failure mode coupling influence and data scarcity constraints faced by traditional FMEA when applied to the thrust bearing field by establishing a thrust bearing fault tree that considers failure correlation, a frequency characterization method based on relative probability assignment, and a severity hierarchical assessment method, reduces the subjectivity of the assessment results, and realizes a comprehensive risk assessment of failure modes of large thrust bearings by solving and sorting risk priority coefficients.

[0025] Furthermore, by establishing a fault tree with feedback loop correction, the causes and correlations of thrust bearing failure modes are sorted out, providing an analytical basis for thrust bearing failure mode risk assessment that takes failure coupling into consideration.

[0026] Furthermore, frequency index characterization is realized based on survey statistics and relative probability assignment of bottom events, and relative probability assignment is used to replace absolute failure probability, overcoming the restriction of insufficient data on thrust bearing FMEA. A hierarchical evaluation method is used to realize severity index characterization, which takes into account both the system impact of failure mode and the local impact of failure mode, making up for the insufficiency of failure correlation consideration in existing FMEA methods and establishing a unified evaluation process.

[0027] Furthermore, by combining the frequency, severity and detectability indicators, a comprehensive risk assessment of common failure modes of thrust bearings was completed through solving and sorting the risk factor priority numbers. It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0028] In summary, the present invention can effectively reduce the subjectivity of traditional FMEA in the case of insufficient data and strong coupling, improve the reliability of the results, and provide a thrust bearing failure mode risk assessment method for large thrust bearings that does not rely on the number of fault data and comprehensive failure mode coupling reasoning. It is of great significance to the failure assessment, design and maintenance of thrust bearings.

[0029] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 The initial fault tree structure for thrust bearing overtemperature; Figure 2 Updated fault tree structure for thrust bearing overtemperature; Figure 3 This is a schematic diagram of the severity tiered assessment method; Figure 4 It is a comparison diagram of the results of the present invention and the results of the traditional method; Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 A block diagram of an electronic device provided according to an embodiment of the present invention; Figure 7 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0034] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0035] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0036] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. 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.

[0037] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0038] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0039] The present invention provides a method for risk assessment of failure modes of thrust bearings based on a fault tree. First, a thrust bearing fault tree considering failure correlation is established by introducing a feedback link, the occurrence context of each failure mode is clarified, and a relative probability is assigned to the bottom event of thrust bearing failure; secondly, the relative frequency characterization of the failure mode of the thrust bearing is completed based on the relative probability of the fault tree and the bottom event, and the severity of the failure mode of the thrust bearing is evaluated and characterized in a hierarchical manner considering the system impact, failure deterioration and failure diffusion properties, and the detectability index of the failure mode of the thrust bearing is characterized by a detectable means analysis; finally, the comprehensive risk priority coefficient of the thrust bearing is calculated by combining the three indicators, and the failure mode of the thrust bearing is ranked by a comprehensive risk assessment. The method of the present invention does not rely on the number of fault data, realizes the coupled reasoning of comprehensive failure modes, and provides a basis for the safety assessment, design and maintenance decision of large thrust bearings.

[0040] The present invention provides a thrust bearing failure mode risk assessment method that is independent of the amount of fault data and uses integrated failure mode coupling reasoning.

[0041] Example 1 See also Figure 7 The present invention provides a method for risk assessment of failure modes of thrust bearings based on a fault tree, comprising the following steps: S1. Establish a thrust bearing fault tree for failure mode risk assessment that takes failure association into consideration, and express the feedback impact of events in the form of feedback loops; assign relative probabilities to the thrust bearing failure bottom events in the fault tree; Step S1 is specifically as follows: S101. Establish a thrust bearing fault tree for failure mode risk assessment; Based on the investigation of thrust bearing failure cases, a thrust bearing fault tree was established. Common failure modes of thrust bearings were selected as top events, and a fault tree considering failure correlation was established based on the causes of the failure modes in the cases.

[0042] S102, introducing a feedback loop to express the feedback effect of the thrust bearing failure mode; The introduced feedback loop bottom events correspond one-to-one with the failure modes to express the feedback influence of the thrust bearing failure mode.

[0043] S103. Through investigation and statistics, the occurrence frequencies of the thrust bearing failure bottom events in the cases are ranked, and the relative probability values ​​of the thrust bearing failure bottom events are assigned based on the ranking results. The relative probability for:

[0044] in, is the probability assignment coefficient, the coefficient with the lowest relative probability is 1, the highest is the multiple of the highest and lowest times obtained by statistics, and the middle value is assigned arithmetic progression between 1 and the highest value according to the sorting; P is the unknown probability reference value, 0<P<1.

[0045] S2. Based on the relative probability of the bottom event and the thrust bearing fault tree structure, the relative frequency characterization of the thrust bearing failure mode is completed; through a hierarchical evaluation method, the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties; the failure detection means are analyzed for the failure modes in the S1 thrust bearing fault tree to obtain the detectability indicators of each failure mode of the thrust bearing.

[0046] Step S2 is specifically as follows: S201, solving the initial relative probability of the top event in combination with the initial fault tree structure of the thrust bearing, 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; Initial relative probability of thrust bearing failure mode represented by the top event The solution formula for updating the relative probability is:

[0047] Where k is the fault tree The minimum number of cut sets; Fault Tree The jth minimal cut set.

[0048] The formula for assigning probability to the bottom event of the feedback loop is as follows: As shown in formula (3), let (i=1,2,…,7), then:

[0049] In the formula, is the relative probability of the bottom event of the feedback loop, is the initial probability of the failure mode corresponding to the bottom event of the feedback loop.

[0050] S202, based on the probability assignment of the feedback loop bottom event, solve the probability importance index I of the feedback loop bottom event to characterize the failure deterioration nature of the thrust bearing; calculate the number of times M that each failure mode appears as an intermediate event to characterize the failure diffusion nature of the thrust bearing; Probability importance index of the feedback loop bottom event Yi for:

[0051] Where i=1,2,…,7; is the probability importance of the feedback loop bottom event Yi in the fault tree treek; is the probability of the top event Fk occurring when the feedback loop Yi occurs; is the probability of the top event Fk occurring when the feedback loop Yi does not occur.

[0052] S203. Perform frequency characterization based on the updated relative probability ranking of the top events of the thrust bearing failure; perform a hierarchical assessment of the severity of the thrust bearing failure mode, and comprehensively characterize the severity of the thrust bearing by combining indicators I and M in each layer; perform failure detection means analysis on the failure modes in the thrust bearing fault tree, and obtain detectability indicators of the thrust bearing failure modes.

[0053] The characterization method of the relative frequency index of thrust bearing failure mode is as follows: the failure mode frequency with the lowest relative probability ranking is updated to 1, the highest is 10, and the failure mode frequency in the middle is characterized by an arithmetic assignment between 1 and 10 according to the ranking.

[0054] The method for assessing the severity of 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 a failure occurs, it will directly cause a system-level failure, threaten personnel safety, or cause the loss of bearing function; medium-risk failure means that after a failure occurs, the accumulation of a certain degree of failure will cause the loss of bearing function; low-risk failure means that after a failure occurs, the loss of bearing function is mainly caused by secondary failures.

[0055] In each layer, the average of the rankings of the two indicators I and M is taken as the final ranking of the secondary evaluation of the severity index. The comprehensive ranking of the thrust bearing severity is obtained by combining the severity stratification results and the severity ranking in each layer. Based on the comprehensive ranking results, the failure mode severity index is comprehensively assigned a value within the range of 1-10.

[0056] The detection index evaluation method is as follows: the thrust bearing detection index is divided into ten levels from 1 to 10, 1 represents the easiest to detect, and 10 represents almost impossible to detect.

[0057]

[0058] S3. Based on the relative frequency, severity and detectability indicators characterized by step S2, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and risk priority is sorted to achieve the risk assessment of the thrust bearing failure mode based on the fault tree.

[0059] Step S3 is specifically as follows: The comprehensive risk priority coefficient of the thrust bearing failure mode is solved and the risk priority is ranked. The ranking represents the relative high and low comprehensive risk of the thrust bearing failure mode. It is characterized by the fact that the thrust bearing comprehensive risk priority coefficient for:

[0060] in, , , They are the frequency, severity and detectability indicators of thrust bearings respectively.

[0061] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0062] Example 2 The present invention provides a thrust bearing failure mode risk assessment system based on a fault tree, which can be used to implement the above-mentioned thrust bearing failure mode risk assessment method based on a fault tree. Specifically, the thrust bearing failure mode risk assessment system based on a fault tree includes a feedback module, a characterization module, a hierarchical module and an assessment module.

[0063] Among them, the reference module establishes a thrust bearing fault tree for failure mode risk assessment and considering failure correlation, and expresses the feedback impact of events in the form of feedback loops; assigns relative probabilities to the thrust bearing failure bottom events in the fault tree; The characterization module completes the relative frequency characterization of the thrust bearing failure mode based on the relative probability of the bottom event and the thrust bearing fault tree structure; the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties through a hierarchical evaluation method; the failure detection means are analyzed for the failure modes in the reference module thrust bearing fault tree to obtain the detectability index of each failure mode of the thrust bearing; The evaluation module solves the comprehensive risk priority coefficient of the thrust bearing failure mode based on the relative frequency, severity and detectability indicators represented by the characterization module, and performs risk priority sorting to realize the risk assessment of the thrust bearing failure mode based on the fault tree.

[0064] Example 3 The present invention provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute 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 (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the failure mode risk assessment method of the thrust bearing based on the fault tree, including: A thrust bearing fault tree considering failure association is established for failure mode risk assessment, and the feedback impact of events is expressed in the form of feedback loops; relative probabilities are assigned to the thrust bearing failure bottom events in the fault tree; based on the relative probabilities of the bottom events and the thrust bearing fault tree structure, the relative frequency characterization of the thrust bearing failure mode is completed; through a hierarchical evaluation method, the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties; the failure detection means are analyzed for the failure modes in the thrust bearing fault tree, and the detectability indicators of each failure mode of the thrust bearing are obtained; based on the characterized relative frequency, severity and detectability indicators, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and risk priority is sorted to achieve the risk assessment of the thrust bearing failure mode based on the fault tree.

[0065] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for risk assessment of failure mode of thrust bearing based on fault tree in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the system for risk assessment of failure mode of thrust bearing based on fault tree in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0066] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0067] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0068] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0069] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0070] See also Figure 6 The terminal device is an electronic device 600, which is 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 the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0071] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 7 Follow the steps shown in .

[0072] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0073] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0074] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0075] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the 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, etc.

[0076] Example 4 The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable 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, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0077] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0078] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0079] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for risk assessment of failure mode of a thrust bearing based on a fault tree in the above embodiment; one or more instructions in the computer-readable storage medium may be loaded and executed by a processor as follows: A thrust bearing fault tree considering failure association is established for failure mode risk assessment, and the feedback impact of events is expressed in the form of feedback loops; relative probabilities are assigned to the thrust bearing failure bottom events in the fault tree; based on the relative probabilities of the bottom events and the thrust bearing fault tree structure, the relative frequency characterization of the thrust bearing failure mode is completed; through a hierarchical evaluation method, the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties; the failure detection means are analyzed for the failure modes in the thrust bearing fault tree, and the detectability indicators of each failure mode of the thrust bearing are obtained; based on the characterized relative frequency, severity and detectability indicators, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and risk priority is sorted to achieve the risk assessment of the thrust bearing failure mode based on the fault tree.

[0080] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0081] See also Figure 1 Taking the thrust bearing overtemperature 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 the heat dissipation effect. Abnormal heat generation is mainly caused by abnormal wear of the bearing shell. The above causes are further disassembled until the cause event cannot or does not need to be further divided, that is, the bottom event is reached, and the initial fault tree of the thrust bearing overtemperature is obtained.

[0082] See also Figure 2 , introduce the bottom event of the "feedback loop" to represent the reverse impact of the upper event to express the positive feedback effect in the fault occurrence process and realize the correction of the initial fault tree. Figure 2As shown, lubrication failure indirectly leads to over-temperature failure by causing wear. Once over-temperature occurs, it will affect the physical and chemical properties of the lubricant, further aggravating the degree of lubrication failure. There is a positive feedback relationship between over-temperature and lubrication failure. Oil film rupture will lead to lubrication failure. After lubrication failure occurs, the change in force will further lead to failure to establish the oil film. In addition, uneven force will cause wear by causing the rupture of the oil film, and the existence of wear will further aggravate the uneven force. Figure 2 In the modified fault tree shown, the above three feedback effects are expressed through the bottom events "overtemperature feedback" Y1, "wear feedback" Y3 and "lubrication failure feedback" Y2.

[0083] The relative probability of the bottom event of thrust bearing failure is assigned, and the relative probability of the bottom event is composed of the relative probability coefficient and the unknown minimum probability cardinality; 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 ranked, and the lowest failure mode frequency is 1, the highest is 10, and the failure mode frequencies in the middle are represented by arithmetic progression between 1 and 10 according to the ranking.

[0084] See also Figure 3 , the severity of each failure mode of the thrust bearing is evaluated for the first time from the system impact level. The failure modes are divided into three levels: high-risk failure, medium-risk failure, and low-risk failure, and the severity levels of the three levels decrease in sequence. Among them, high-risk failure means that once the failure occurs, it will directly cause system-level failure, threaten personnel safety, or cause the loss of bearing function; medium failure means that after the failure occurs, it will cause the loss of bearing function under a certain degree of accumulation; low-risk failure means that after the failure occurs, the loss of bearing function is mainly caused by secondary failures.

[0085] In each layer, the failure deterioration property and failure diffusion property of the thrust bearing are considered for secondary evaluation. The failure deterioration property (indicator I) is characterized by the probability importance index of the feedback loop. The failure diffusion property (indicator M) is characterized by the number of times each failure mode of the thrust bearing occurs as an intermediate event, and the average of the rankings of the two indicators I and M is taken as the final ranking of the secondary evaluation of the severity index.

[0086] Then, combining the severity stratification results and the severity ranking within each layer, the comprehensive ranking of thrust bearing severity is obtained, and the failure mode severity index is comprehensively assigned a value within the range of 1-10 based on the comprehensive ranking results.

[0087] Finally, the thrust bearing failure detection method is used to analyze and obtain the detectability index, which is divided into ten levels from 1 to 10, with 1 representing the easiest to detect and 10 representing almost impossible to detect. The comprehensive risk priority coefficient of the thrust bearing failure mode is solved and the risk priority is sorted.

[0088] See also Figure 4 ,In order to verify the effectiveness of this method, a questionnaire method was adopted to ,investigate the results of the comprehensive evaluation of the ,failure mode of thrust bearing according to the traditional FMEA process by investigating the ,experts, to verify and compare the advantages and disadvantages of the ,traditional method and this method.

[0089] From the results, it can be seen that in the results obtained by the two methods, the bearing wear F3 is the failure mode with the highest comprehensive risk factor, and the ranking results are mostly within 1. However, in the traditional FMEA results, the ranking of the support or positioning structure failure F7 has increased significantly, and the ranking result of the lubrication failure F2 has decreased significantly. When analyzing the severity index, the traditional FMEA analyzes the independent consequences of a single failure. Therefore, from the failure results, experts believe that the severity of the support or positioning structure failure is second only to the thrust plate fracture and lubrication failure. In this method, not only the overall impact of the failure is considered, but also the mutual induction between other failure modes is comprehensively considered, and the severity level of the support or positioning structure failure is judged to be lower. From the expert scoring results, we can see that the average O index score of lubrication failure is only 4.3, which is lower than that of bearing wear. In this method, the frequency of lubrication failure is second only to excessive temperature. Since the occurrence of bearing wear usually has obvious macroscopic phenomena, and in the coupled relationship of failure modes, the result of lubrication failure often appears as an intermediate process and is more easily ignored. In addition, due to the lack of failure statistics, experts can only score the frequency based on their own experience, thus underestimating the frequency of lubrication failure. This method greatly improves the reliability of the results by sorting out the occurrence process and mutual relationship of failure modes, and replacing the subjectivity of empirical judgment with the objectivity of fault tree and case statistics.

[0090] In summary, the present invention provides a method and system for risk assessment of failure mode of thrust bearings based on fault tree, which aims to solve the problem that large thrust bearings are limited by the lack of fault data and failure mode coupling, making it difficult to carry out comprehensive risk assessment. Based on the fundamental purpose of FMEA analysis and combined with the basic principles of fault tree, a method for risk assessment of failure mode of thrust bearings that does not rely on the amount of data and takes failure coupling into consideration is proposed, which provides a basis for safety assessment, design and maintenance decision-making of large thrust bearings.

[0091] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the embodiments provided by the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] If the integrated module / unit is implemented in the form of 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for risk assessment of failure modes of thrust bearings based on a fault tree, characterized in that: The following steps are involved: S1. Establish a thrust bearing fault tree for failure mode risk assessment that takes failure association into consideration, and express the feedback impact of events in the form of feedback loops; assign relative probabilities to the thrust bearing failure bottom events in the fault tree; S2. Based on the relative probability of the bottom event and the thrust bearing fault tree structure, the relative frequency characterization of the thrust bearing failure mode is completed; through a hierarchical evaluation method, the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties; the failure detection means are analyzed for the failure mode in the thrust bearing fault tree in step S1 to obtain the detectability index of each failure mode of the thrust bearing; S3. Based on the relative frequency, severity and detectability indicators characterized by step S2, the comprehensive risk priority coefficient of the thrust bearing failure mode is solved, and risk priority is sorted to achieve the risk assessment of the thrust bearing failure mode based on the fault tree.

2. The method for risk assessment of thrust bearing failure mode based on fault tree according to claim 1, characterized in that: Step S1 is specifically as follows: S101. Establish 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, wherein the bottom event of the introduced feedback loop corresponds to the failure mode one by one to express the feedback influence of the thrust bearing failure mode; S103. Through investigation and statistics, the frequency of occurrence of each thrust bearing failure bottom event in the case is ranked, and based on the ranking results, the relative probability of the thrust bearing failure bottom event is assigned. The relative probability for: in, is the probability assignment coefficient; P is the unknown probability reference value.

3. The method for risk assessment of thrust bearing failure mode based on fault tree according to claim 1, characterized in that: Step S2 is specifically as follows: S201, solving the initial relative probability of the top event in combination with the initial fault tree structure of the thrust bearing, 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 feedback loop bottom event, solve the probability importance index I of the feedback loop bottom event to characterize the failure deterioration nature of the thrust bearing; calculate the number of times M that each failure mode appears as an intermediate event to characterize the failure diffusion nature of the thrust bearing; S203. Perform frequency characterization based on the updated relative probability ranking of the top events of the thrust bearing failure; perform a hierarchical assessment of the severity of the thrust bearing failure mode, and comprehensively characterize the severity of the thrust bearing by combining indicators I and M in each layer; perform failure detection means analysis on the failure modes in the thrust bearing fault tree, and obtain detectability indicators of the thrust bearing failure modes.

4. The method for risk assessment of thrust bearing failure modes based on fault tree according to claim 3 is characterized in that: In step S201, the initial relative probability of the thrust bearing failure mode represented by the top event is The solution formula for updating the relative probability is: Where k is the fault tree The minimum number of cut sets; Fault Tree The jth minimum cut set; The probability of the feedback loop bottom event is assigned as follows: in, is the relative probability of the bottom event of the feedback loop, is the initial probability of the failure mode corresponding to the bottom event of the feedback loop.

5. The method for risk assessment of thrust bearing failure modes based on fault tree according to claim 3 is characterized in that: In step S202, the probability importance index of the bottom event Yi of the feedback loop is for: Where i=1, 2, …, 7; is the probability importance of the feedback loop bottom event Yi in the fault tree treek; is the probability of the top event Fk occurring when the feedback loop Yi occurs; is the probability of the top event Fk occurring when the feedback loop Yi does not occur.

6. The method for risk assessment of failure modes of thrust bearings based on a fault tree according to claim 3, characterized in that: In step S203, the relative frequency characterization method of the thrust bearing failure mode is: The failure mode frequency with the lowest update relative probability ranking is 1, the highest failure mode frequency is 10, and the failure mode frequencies in the middle are represented by arithmetic progression between 1 and 10 according to the ranking.

7. The method for risk assessment of thrust bearing failure modes based on a fault tree according to claim 3, characterized in that: In step S203, the thrust bearing failure mode severity hierarchical assessment method is: The severity of thrust bearing failure modes is divided into high-risk failure layer, medium-risk failure layer, and low-risk failure layer; high-risk failure means that failure occurs, directly causing system-level failure, threatening personnel safety or causing loss of bearing function; medium-risk failure means that after failure occurs, the bearing function is lost; low-risk failure means that after failure occurs, secondary failure causes loss of bearing function; In each layer, the failure deterioration property and failure diffusion property are considered for secondary evaluation. The failure deterioration property of the thrust bearing is characterized by the probability importance index of the feedback loop as index I; the failure diffusion property of the thrust bearing is characterized by the number of times each failure mode appears as an intermediate event as index M; The average of the rankings of the I and M indicators is taken as the final ranking of the secondary evaluation of the severity index. The comprehensive ranking of the thrust bearing severity is obtained by combining the severity stratification results and the severity ranking within each layer. Based on the comprehensive ranking results, the failure mode severity index is comprehensively assigned a value within the range of 1-10.

8. A method for risk assessment of failure modes of thrust bearings based on a fault tree according to claim 3, characterized in that: In step S203, the detectability index of each failure mode of the thrust bearing is: The thrust bearing detectability index is divided into levels 1-10, and the detection difficulty increases step by step from level 1 to level 10. Level 1 means that 100% of the failure is detected when it occurs, and level 10 means that the failure cannot be detected when it occurs.

9. The method for risk assessment of failure modes of thrust bearings based on fault tree according to claim 1, characterized in that: In step S3, the comprehensive risk priority coefficient of the thrust bearing failure mode for: in, , , They are the frequency, severity and detectability indicators of thrust bearings respectively.

10. A thrust bearing failure mode risk assessment system based on a fault tree, characterized in that: include: Reference module, establish a thrust bearing fault tree for failure mode risk assessment and considering failure association, and express the feedback impact of events in the form of feedback loops; assign relative probabilities to the thrust bearing failure bottom events in the fault tree; The characterization module completes the relative frequency characterization of the thrust bearing failure mode based on the relative probability of the bottom event and the thrust bearing fault tree structure; the severity of the thrust bearing failure mode is characterized by considering the failure deterioration and failure diffusion properties through a hierarchical evaluation method; the failure detection means are analyzed for the failure modes in the reference module thrust bearing fault tree to obtain the detectability index of each failure mode of the thrust bearing; The evaluation module solves the comprehensive risk priority coefficient of the thrust bearing failure mode based on the relative frequency, severity and detectability indicators represented by the characterization module, and performs risk priority sorting to realize the risk assessment of the thrust bearing failure mode based on the fault tree.

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