FMEA data acquisition, processing and application method

Abstract

The invention belongs to the technical field of potential fault modes and influence analysis tools FMEA, and particularly relates to an FMEA data acquisition, processing and application method. The technical problems that existing FMEA data acquisition is time-consuming and low in accuracy and utilization rate, implementation and effectiveness evaluation of FMEA measures are insufficient, and the FMEA data do not play a role in dynamic early warning to analysts and triggering of FMEA in-time iterative updating are solved. According to the method, an FMEA data acquisition, processing and application system is constructed. The system comprises an FMEA data acquisition module, an FMEA data processing module, an FMEA knowledge storage module and an FMEA knowledge application module. According to the method, automatic acquisition of FMEA data from an enterprise digital platform and full-life-cycle association are supported, efficient FMEA knowledge is formed through automatic classification and collection and dynamic risk assessment, automatic implementation tracking and effectiveness continuous iteration assessment of FMEA measures are supported, and close association of the FMEA data and design, manufacturing and operation and maintenance processes is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of potential failure mode and effects analysis tool FMEA, and in particular to a method for FMEA data acquisition, processing and application. Background Art

[0002] As a representative of high-end manufacturing, the rail transit equipment industry has characteristics such as a complete industrial chain, high technological leadership, large product application scale, great social influence, and large industry driving role. With the wide application of China's high-speed trains at home and abroad, the reliability requirements for rail transit equipment are getting higher and higher. Therefore, it is particularly important for rail transit equipment manufacturing enterprises to have the ability of pre-event prevention and comprehensive control of risks / failures.

[0003] As a basic analysis tool for pre-event prevention of risks / failures in China's rail transit equipment - potential failure mode and effects analysis (FMEA), which is an analysis technique that identifies each potential failure mode and its effects in a product and classifies them according to their severity and probability. Currently, EXCEL documents are generally used for management or commercial FMEA software is introduced. Although the application of software has improved the analysis convenience to a certain extent, in terms of the acquisition, processing and interactive application of the underlying data supporting FMEA, there is a lack of theoretical method guidance for integrating the whole industrial chain data based on the enterprise application scenario and digital platform to efficiently support the FMEA analysis application.

[0004] In the engineering practice processes such as product R & D design, production manufacturing and operation and maintenance services of China's rail transit equipment manufacturing enterprises, a large amount of theoretical mechanisms, engineering experience and decision-making strategies have been condensed. Valuable data is often scattered in enterprise document materials or employees' memories, with problems such as being easy to lose, difficult to share and lacking management. The experience and essence of rail transit equipment manufacturing are constantly being lost, and it is even more difficult to form knowledge through condensation and conversion for effective use by FMEA, seriously affecting the pre-event prevention ability of FMEA.

[0005] With the emergence of a new round of technological innovation wave represented by information networks, intelligent manufacturing, new energy and new materials globally, the rail transit equipment field is also breeding a new round of all-round changes. In the next decade, China's rail transit equipment will focus on the development of product digital design, intelligent manufacturing and information service. Therefore, carrying out research on the method for FMEA data acquisition, processing and application based on the data integration of the whole rail transit industrial chain is of great significance for enabling the precipitated data and experience to better serve industry innovation.

[0006] Figure 1It is a FMEA data acquisition and application solution for existing rail transit equipment manufacturing companies. 1 is the data acquisition task before the start of FMEA. Reliability engineers manually collect and organize information such as product configuration, process route, similar product manufacturing, operation and failure to form an EXCEL or WORD document, which is provided to FMEA analysts or imported into FMEA software. 2 is for analysts to first use product configuration and process route information to draw functional block diagrams and function tree analysis, and then use information such as failure modes, failure causes, failure effects, detection methods, corrective and preventive measures that have occurred in similar products. Finally, combined with the FMEA team brainstorming method, potential product failure analysis is carried out, and corrective and preventive measures are formulated for high-risk failures, which are implemented in product design drawings and technical documents (safety key parts list, importance classification table, test outline, process code, user manual, etc.). At this time, the FMEA document is finalized. Subsequently, when the product undergoes design / process changes, changes in operating environment conditions or major problems, the FMEA re-analysis is manually triggered and the document is updated and upgraded.

[0007] The disadvantages of the above prior art are as follows: 1) FMEA data acquisition is time-consuming and has low accuracy and utilization. The data supporting FMEA are mostly scattered in different business systems or in the hands of personnel. Most of them need to be manually collected and organized into EXCEL documents or imported into FMEA software. Even if the FMEA software has a data interface with a certain business system, it only extracts the original fault data for analysts to search and query. It lacks data processing and professional team review to transform it into efficient FMEA knowledge, and cannot achieve recommended applications that directly match the FMEA process.

[0008] 2) Insufficient implementation and effectiveness evaluation of FMEA measures. At present, the implementation of FMEA measures in rail transit manufacturing enterprises is mostly manually added to product design drawings and texts by analysts, and then the drawings and texts are distributed to the implementers. Due to the lack of response implementation and efficient transmission of FMEA analysis results by using information system docking, there are generally problems of inadequate implementation and untimely transmission of measures. For the effective evaluation of FMEA measures, reliability engineers spend a lot of energy to manually collect relevant information on the evidence of FMEA measures implementation, and then conduct a one-time manual analysis to determine whether the FMEA measures are effective. There are problems such as time-consuming and labor-intensive evaluation work and difficulty in ensuring the accuracy of the evaluation. At the same time, due to the lack of high-frequency data interaction and embedded evaluation mechanism with product design, manufacturing and operation processes, the effectiveness of FMEA measures will only be evaluated using data from a certain period of time / time point, which has the risks of one-sided and insufficient evaluation, and there is no monitoring and evaluation of whether FMEA measures are continuously effective on the product.

[0009] 3) The FMEA data fails to dynamically warn analysts and trigger the timely iterative update of FMEA. Currently, most applications of FMEA in rail transit manufacturing enterprises are carried out at the product design or process plan stage, with a one-time analysis and risk assessment based on current knowledge. Regarding the FMEA update mechanism, the FMEA update analysis is only manually triggered when there are three situations: design or process changes, major quality problems, and changes in operating environment conditions. There is a common problem of untimely update analysis. At the same time, enterprises often lack the association with reliability data in the subsequent manufacturing and operation stages, and cannot timely feedback the actual product failure information and the evaluation results of the effectiveness of FMEA measures to design / process personnel, failing to play the role of dynamic warning. This results in the frequent existence of free-floating failures (i.e., failures not analyzed and controlled by FMEA) in enterprises or the ineffectiveness of FMEA measures due to the emergence of uncertain factors during the continuous manufacturing and operation of products, making FMEA an analysis tool for post-event remedies of products and greatly reducing the ex-ante prevention ability of FMEA. Summary of the Invention

[0010] To solve the technical problems in the prior art, such as the time-consuming acquisition of FMEA data, low accuracy and utilization rate, insufficient implementation and effectiveness evaluation of FMEA measures, and the failure of FMEA data to dynamically warn analysts and trigger the timely iterative update of FMEA, the present invention provides a method for acquiring, processing, and applying FMEA data. This method improves the support effect of FMEA data on the FMEA process through methods such as information platform docking, big data processing, model retrieval, invocation, and feedback mechanisms, enhances the ex-ante prevention and control capabilities of product risks / failures, and enables the accumulated data and experience to better serve industry innovation.

[0011] The present invention provides a method for acquiring, processing, and applying FMEA data. This method constructs a system for acquiring, processing, and applying FMEA data, which includes an FMEA data acquisition module, an FMEA data processing module, an FMEA knowledge storage module, and an FMEA knowledge application module. The enterprise digital platform includes a PLM business system, an ERP business system, an MRO business system, a QMS business system, and a LIMS business system. Based on the enterprise digital platform, an FMEA data acquisition module is established by using software interface development technology between business systems. The FMEA data acquisition module regularly and automatically acquires the original data of the product's entire life cycle required for FMEA by means of a standard interface program and intermediate table transmission between business systems. The original data of the product's entire life cycle includes design data, manufacturing data, operation data, failure data, and verification data. The FMEA data processing module is established by using big data technology to process and transform the scattered and independent raw data of the product life cycle obtained in the FMEA data acquisition module into related data with certain regular conclusions that can quickly and efficiently support FMEA analysis. The data processing and transformation process is as follows: according to a certain data cleaning logic, duplicate data are eliminated, missing data are filled, and noise data is removed, and data cleaning is performed on the raw data of the product life cycle; after data cleaning, a unique identification number is found, and the man-machine-material-method-environmental test data related to the design, manufacturing, operation and maintenance of a single product / component in the whole life cycle is connected in series to complete the single number data connection; in the model, series Find the common attribute data in the statistical information of columns, types, and fields, and perform information association according to the data association rules to complete the association of similar data; automatically generate the function network / fault network based on the product BOM / process route, structure tree / process tree and fault data for the data that has completed the association of similar data, complete the collection of fault-related information, and automatically calculate the SOD value and AP value by embedding the operation failure rate / manufacturing failure frequency algorithm model and adding FMEA team judgment nodes; evaluate the effectiveness of FMEA measures based on simulation, test, quality stability and operation evaluation results; recommend the best measures in combination with the AP value and measure effectiveness evaluation results, and store the best measures in the FMEA knowledge storage module; The FMEA knowledge storage module is used to realize the automatic acquisition and high-frequency iterative update of FMEA data. In the FMEA knowledge storage module, experts are extracted from the FMEA expert library for FMEA knowledge review, and the data of failure parts, modes, causes, effects, detections, and preventive measures after review are stored to form a failure experience database; the newly calculated failure frequency, failure rate, quality stability value, SOD value, and AP value after iterative calculation of the storage platform are stored to form a risk prediction database; the feedback data of implemented measures, the quality stability, failure rate, and AP value before and after improvement are compared and saved to form a measure evaluation database; at the same time, a knowledge graph module is embedded in the FMEA knowledge storage module. By the given characteristics of the product function structure and environmental working conditions, the relationship chain between causes, modes, and effects is explored, and relevant failure information is intelligently recommended to design / process personnel. For example, the knowledge graph function pushes failure dictionary information and the number of free failures to the enterprise quality management and maintenance after-sales system to realize the functions of knowledge exploration, knowledge Q&A, and knowledge recommendation; the FMEA knowledge storage module also includes an FMEA trigger update mechanism, which uses information means to automatically identify the FMEA update opportunity, including when there are changes in human, machine, material, method, environment, and measurement factors during the design, manufacturing, or operation stage, when new failures occur, when the operation failure rate / manufacturing failure frequency / quality stability / PHM failure diagnosis value / AP value exceeds the threshold, the products / components and FMEA records related to the analysis to be updated will be associated, and an FMEA update instruction will be sent to the analyst in the FMEA data application module to ensure that the enterprise eliminates free failures. Free failures are failures that have not undergone FMEA analysis and control, enabling FMEA to play a role in pre-preventing product risks / failures and controlling the entire life cycle; when starting FMEA, the FMEA knowledge storage module will automatically retrieve the stored failure experience database according to the product function and structure information filled in the FMEA data application module, and match and recommend relevant failure experience data to the analyst, including possible failure parts, modes, effects, and causes. After the analyst conducts further discussions with the FMEA team and adds or corrects the failure analysis, the risk automatic assessment (SOD value and AP value calculation) and the matching recommendation of preventive measures in the FMEA knowledge storage module will be triggered. If it is a new failure, the analyst will start the SOD score and AP value calculation and formulate preventive measures by himself / herself; The FMEA data application module sequentially includes the new product structure and function analysis, new product failure impact analysis, new product failure mode analysis, new product failure cause analysis, new product SOD analysis, and the process of formulating FMEA preventive measures for new products. On the one hand, the FMEA data application module is connected to the FMEA knowledge storage module, used to receive failure experience, risk assessment, measure assessment data, and FMEA update instructions, and send back the finalized product FMEA records to the FMEA knowledge storage module; on the other hand, it is connected to each business system of the enterprise, and the measures formulated in the FMEA data application module are respectively transmitted to each business system according to the pre-set measure categories to issue the execution requirements of design / process improvement measures and simulation / test plan requirements; at the same time, it triggers the FMEA data acquisition module to continuously send requests to relevant business systems to obtain corresponding data, so that the FMEA knowledge storage module can regularly receive the result data of the implementation of FMEA measures, realize the automatic transmission and implementation of measures and the continuous evaluation of effectiveness, and finally form a high-quality interaction process of FMEA data from dispersion to concentration and then to effective application.

[0012] The present invention aims at the FMEA application scenario of rail transit equipment manufacturing enterprises, and proposes a method for FMEA data acquisition, processing and application applicable to rail transit equipment manufacturing enterprises and driven by industrial big data, which supports the automatic acquisition and penetration of the whole life cycle data related to product failures, and can be processed into FMEA knowledge with certain regular conclusions, so as to form the intelligent recommendation application ability for collaborative FMEA processes; it supports the automatic implementation tracking of FMEA measures to business systems and the continuous iterative evaluation of effectiveness.

[0013] The present invention proposes a process for automatically calculating and iteratively updating indicators such as manufacturing quality stability, manufacturing / operation failure rate, number of free failures, AP value, etc. based on the FMEA original data penetrated by rail transit equipment manufacturing enterprises by using big data technology and information means, and a method for dynamically evaluating the effectiveness of FMEA measures by using these indicators.

[0014] The present invention proposes a method applicable to rail transit equipment manufacturing enterprises, which uses information means to dynamically monitor and trigger FMEA iterative updates to FMEA analysts by automatically monitoring change information such as design, process, environment, operation conditions, etc., monitoring new failure information, monitoring trends of indicators such as failure rate, quality stability, AP value, etc., and monitoring the effectiveness evaluation results of FMEA measures.

[0015] The technical solution provided by the present invention has the following technical effects compared with the prior art: The method of the present invention can realize the automatic acquisition of FMEA data from the enterprise digital platform and the association throughout the life cycle, support the formation of efficient FMEA knowledge by automatically classifying and summarizing FMEA data and dynamically assessing risks, support the automatic implementation tracking of FMEA measures and the continuous iterative evaluation of effectiveness, realize the close association of FMEA data with the design, manufacturing and operation and maintenance processes, and provide a convenient and efficient method for obtaining, processing and interacting with FMEA data based on the industrial big data platform for rail transit equipment manufacturing enterprises, thereby improving the ability of FMEA pre-prevention and life cycle risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic structural diagram of the FMEA system in the background art; Figure 2 It is a schematic structural diagram of a system in a method for obtaining, processing and applying FMEA data disclosed in an embodiment of the present invention; Figure 3 It is a schematic structural diagram of the FMEA data acquisition module in a system for obtaining, processing and applying FMEA data disclosed in an embodiment of the present invention; Figure 4 It is a schematic structural diagram of the FMEA data processing module in a system for obtaining, processing and applying FMEA data disclosed in an embodiment of the present invention; Figure 5 It is a schematic structural diagram of the FMEA knowledge storage module in a system for obtaining, processing and applying FMEA data disclosed in an embodiment of the present invention; Figure 6 It is a schematic structural diagram of the FMEA knowledge application module in a system for obtaining, processing and applying FMEA data disclosed in an embodiment of the present invention; Figure 7 It is a statistical table of off-site failure information involved in a specific case of the present invention; Figure 8 It is a QMS product configuration table involved in a specific case of the present invention; Figure 9 This is the QMS incoming inspection statistical table involved in a specific case of an embodiment of the present invention. Figure 10 This is a schematic diagram of the basic information interface for using the FMEA data acquisition, processing, and application system in a specific case of an embodiment of the present invention. Figure 11 This is a schematic diagram of the inspection card interface for using the FMEA data acquisition, processing, and application system in a specific case of an embodiment of the present invention. Figure 12 This is the design FMECA potential failure mode effects and criticality analysis table in a specific case of an embodiment of the present invention. Detailed implementation manners

[0019] In order to more clearly understand the above objects, features, and advantages of the present invention, the solution of the present invention will be further described below. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0020] In the description, it should be noted that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. It should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0021] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present invention, rather than all the embodiments.

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] The technical solution proposed by the present invention relates to a method for data acquisition and penetration, data processing, and interactive application of data between the FMEA system and each business system in the product full life cycle for FMEA based on the product full life cycle information platform of rail transit equipment manufacturing enterprises. The specific implementation path is as follows.

[0024] In one embodiment, as Figure 1As shown, a method for obtaining, processing, and applying FMEA data is disclosed. This method constructs a system for obtaining, processing, and applying FMEA data, which includes an FMEA data acquisition module, an FMEA data processing module, an FMEA knowledge storage module, and an FMEA knowledge application module; The enterprise digital platform includes a PLM business system, an ERP business system, an MRO business system, a QMS business system, and a LIMS business system; Based on the enterprise digital platform, an FMEA data acquisition module is established using software interface development technology between business systems; The FMEA data acquisition module uses the standard interface program and intermediate table transmission method with the business system to regularly and automatically obtain the original data of the product's entire life cycle required for FMEA. The original data of the product's entire life cycle includes design data, manufacturing data, operation data, failure data, and verification data; Among them, the product design data includes information such as the product configuration table (BOM table), functional and performance parameters, internal and external interface methods, changes and versions, and process routes; The product manufacturing data includes information such as part process transfer, process inspection data, operators, equipment operation and maintenance, material inspection, process methods, and on-site environment; The product operation data includes information such as shipping, assignment, operation profile, natural and operating environment factors, and maintenance; The product failure data includes information such as failure time, location, phenomenon, impact, cause, diagnosis and repair situation, corrective and preventive measures, failure cases, and reliability design criteria; The product verification data includes information such as simulation results, test results, manufacturing stability assessment results, and operation reliability assessment results; These types of data are respectively collected and stored by each business platform of the enterprise (such as the PLM system, simulation platform, ERP system, QMS system, LIMS system, MRO system, etc.) for the original data generated during the entire life cycle of each product / part; For these scattered data, through software interface development technology between systems, the full-volume data is automatically and regularly extracted using the standard interface program and intermediate table transmission method with the business system, thereby obtaining the scattered and independent original FMEA product life cycle data; Use big data technology to establish an FMEA data processing module, and process and transform the scattered and independent original data of the product's entire life cycle obtained in the FMEA data acquisition module into associated data with certain regular conclusions that can quickly and efficiently support FMEA analysis. The data processing and transformation process is as follows: eliminate duplicate data, fill in missing data, and remove noise data according to a certain data cleaning logic to clean the original data of the product's entire life cycle; after data cleaning, find the unique identification number, concatenate the man-machine-material-method-environment-measurement data related to the entire life cycle stages of design, manufacturing, and operation and maintenance of a single product / component, and complete the concatenation of single-number data; find the common attribute data in the statistical information of the model, series, type, and field to which a single product / component number belongs, and perform information association according to the data association rules to complete the association of similar data; complete the automatic generation of function networks / fault networks based on the product BOM / process route, structure tree / process tree, and fault data for the data after completing the association of similar data, complete the collection of fault-related information, and realize the automatic calculation of SOD values and AP values by embedding the operating failure rate / manufacturing failure frequency algorithm model and adding FMEA team judgment nodes; evaluate the effectiveness of FMEA measures based on simulation, test, quality stability, and operation evaluation results; give the optimal measure recommendation in combination with the AP value and the measure effectiveness evaluation result, and store the optimal measure recommendation in the FMEA knowledge storage module; The original data extracted through the FMEA data acquisition module may have situations such as multi-source heterogeneity, data missing, non-standardization, and abnormal deviation, which affect the quality of FMEA data and the accuracy of application. Therefore, a specific set of ETL process and cleaning rules need to be embedded first, that is, valuable FMEA basic data is obtained after processing such as verification, filling in missing data, eliminating duplicates and noise data; the FMEA basic data after cleaning is still scattered and independent, and can only be simply retrieved by FMEA analysts, lacking information association and statistical analysis to draw regular conclusions as guiding opinions. Therefore, it still needs to be further processed into associated data with certain regular conclusions that match the FMEA process. The specific process of FMEA data processing is as Figure 3 follows; First, concatenate the data of a single product / component's entire life cycle, that is, use the number of a single product / component as the unique identification code, concatenate the man-machine-material-method-environment-measurement data generated in each stage of the entire life cycle such as design, manufacturing, operation, and maintenance, and then find the common attribute data in the statistical information of the model, series, type, and field to which the single-number product / component belongs for information association; Secondly, the associated FMEA data is subjected to the collection of the same type of data, that is, the data such as the failure location, failure mode, failure cause, failure impact, detection and corrective and preventive measures, and measure verification are respectively extracted from the failure information of each product / part number, and are automatically and batch-collected according to the granularity such as part type, product model, and life stage to obtain FMEA experience data; Finally, the collected experience data is analyzed qualitatively and quantitatively to obtain FMEA knowledge with certain regular conclusions, including using data such as product configuration (BOM table), internal and external function flows, installation platforms, and equipment interfaces, and generating product function block diagrams, function networks, and fault networks with the help of expert team analysis; using product shipping / assignment time, in-process transfer information, and fault data, automatically calculating the fault frequency and failure rate by embedding a trend analysis algorithm; using the process information of the manufacturing process and the part performance detection data, automatically calculating the quality stability value by embedding a numerical distribution algorithm; using the failure network, fault frequency / failure rate, and quality stability value, establishing association rules with the scoring criteria of severity S, occurrence O, and detection D, performing SOD intelligent recommendation and automatic calculation of the AP value (risk priority number), and at the same time using the collected data of product online fault diagnosis and health status monitoring, combining the fault information to obtain the fault diagnosis accuracy, providing accurate data support for the quantitative evaluation of detection D; finally, obtaining FMEA knowledge with certain regular conclusions that can efficiently support the FMEA process through the above processing; The FMEA knowledge storage module is used to realize the automatic acquisition and high-frequency iterative update of FMEA data. In the FMEA knowledge storage module, experts are extracted from the FMEA expert library for FMEA knowledge review. The data of failure parts, modes, causes, effects, detections, and preventive measures after review are stored to form a failure experience database; the newly calculated failure frequencies, failure rates, quality stability values, SOD values, and AP values obtained from the iterative calculation of the storage platform are stored to form a risk prediction database; the feedback data of the implemented measures, the quality stability, failure rate, and AP values before and after improvement are compared and saved to form a measure evaluation database; at the same time, a knowledge graph module is embedded in the FMEA knowledge storage module. By the given characteristics of the product function structure and environmental working conditions, the relationship chain between causes, modes, and effects is explored, and relevant failure information is intelligently recommended to design / process personnel. For example, the knowledge graph function pushes failure dictionary information and the number of free failures to the enterprise quality management and maintenance after-sales systems to realize the functions of knowledge exploration, knowledge Q&A, and knowledge recommendation; the FMEA knowledge storage module also includes an FMEA trigger update mechanism, which automatically identifies the FMEA update timing by means of information, including when there are changes in the factors of man, machine, material, method, environment, and measurement in the design, manufacturing, or operation stages, when new failures occur, or when the operation failure rate / manufacturing failure frequency / quality stability / PHM failure diagnosis value / AP value exceeds the threshold. The products / components and FMEA records that need to be updated and analyzed are associated, and an FMEA update instruction is sent to the analysts in the FMEA data application module to ensure that the enterprise eliminates free failures. Free failures are failures that have not been analyzed and controlled by FMEA, enabling FMEA to play a role in pre-preventing product risks / failures and controlling the entire life cycle; when starting FMEA, the FMEA knowledge storage module will automatically retrieve the stored failure experience database according to the product function and structure information filled in the FMEA data application module, and match and recommend relevant failure experience data to the analysts, including possible failure parts, modes, effects, and causes. After the analysts further discuss through the FMEA team and add or correct the failure analysis, the risk automatic assessment (SOD value and AP value calculation) and the matching recommendation of preventive measures in the FMEA knowledge storage module are triggered. If it is a new failure, the analysts will start the SOD scoring, AP value calculation, and formulation of preventive measures by themselves; To ensure the effectiveness of FMEA knowledge, an expert team review mechanism is added to review FMEA knowledge with certain regular conclusions after FMEA data processing, obtain FMEA knowledge that can be directly called by analysts or recommended through intelligent matching, and regularly store it in the failure experience database; for results automatically calculated by the platform such as failure frequency / failure rate, quality stability value, AP value, etc., iterative update calculations will be performed based on the acquisition data real-time feedback from the business system, and after setting the update frequency, it will be regularly stored in the risk prediction database; for the effectiveness of FMEA measures implementation, iterative update evaluations will also be performed based on the result data generated and fed back by the business system in real-time, and stored in the measure evaluation database; if data changes are identified at the same location during regular storage, iterative updates will be performed, and the iterative version numbers will be retained to ensure the uniqueness and traceability of FMEA knowledge; In the FMEA analysis process, the form of directly using tables to view knowledge is usually rather single, making it difficult to fully explore the value of knowledge. Therefore, the FMEA knowledge base also integrates the function of a knowledge graph, that is, a knowledge graph module is constructed. By exploring the relationship chain of causes, patterns, and impacts, the functions of knowledge exploration, knowledge Q&A, and knowledge recommendation are realized to provide analysts with diverse and multi-angle views to explore the value of FMEA knowledge; The FMEA knowledge base also sets up a multi-scenario FMEA startup and update mechanism, including triggering FMEA analysis when a new product is launched; when changes or new failures occur in factors such as man, machine, material, method, environment, and measurement during the design, manufacturing, or operation stages of the product, the change information will be pushed to the analyst and FMEA update will be triggered; when it is identified that the operation failure rate / manufacturing failure frequency / quality stability parameter / PHM health diagnosis value exceeds the threshold, the FMEA system will automatically correct the risk assessment value, and feedback the risk assessment correction result and its related data to the analyst to trigger FMEA update; The FMEA data application module sequentially includes the processes of new product structure and function analysis, new product failure impact analysis, new product failure mode analysis, new product failure cause analysis, new product SOD analysis, and formulation of new product FMEA preventive measures. On the one hand, the FMEA data application module is connected to the FMEA knowledge storage module, used to receive failure experience, risk assessment, measure assessment data, and FMEA update instructions, and send back the finalized product FMEA records to the FMEA knowledge storage module; on the other hand, it is connected to each business system of the enterprise, and the measures formulated in the FMEA data application module are respectively transmitted to each business system according to the pre-set measure categories to issue the execution requirements of design / process improvement measures and simulation / test plan requirements; at the same time, it triggers the FMEA data acquisition module to continuously send requests to relevant business systems to obtain corresponding data, so that the FMEA knowledge storage module can regularly receive the result data of the implementation of FMEA measures, realize the automatic transmission and implementation of measures and the continuous evaluation of effectiveness, and finally form a high-quality interaction process of FMEA data from dispersion to concentration and then to effective application; Specifically, by using the failure experience and risk prediction data in the FMEA knowledge base, combined with information such as product function, structure, and operating environment conditions, it associates potential failure information for analysts, automatically calculates the AP value, and recommends detection and preventive measures, improving the analysis efficiency and accuracy of the FMEA team; by developing data interfaces with business systems, the FMEA measures are respectively transmitted to business systems (including TC system, simulation platform, QMS system, LIMS system, MRO system, etc.) according to the pre-set measure categories to issue the measure execution requirements, and regularly recover the result data of the implementation of FMEA measures, that is, to ensure the rapid transmission, implementation, and closed-loop verification of FMEA measures through information means; by using the failure network data (including product configuration, manufacturing process, failure mode, failure cause, detection and corrective measures, etc.) in the FMEA knowledge base, combined with the product failure repair manual to form failure dictionary information, and regularly push it to the quality management system (i.e., QMS system) and the maintenance and after-sales management system (i.e., MRO system), providing accurate data support for the formulation of manufacturing and operation and maintenance support strategies such as product repair plans, spare parts, personnel, and equipment; by using the dynamically updated failure rate data in the FMEA knowledge base, regularly push it to the online fault diagnosis and health management (PHM) system to provide a basis for optimizing the model algorithms and thresholds of product fault diagnosis and remaining life monitoring; by using the regularly counted free failure times in the FMEA knowledge base, that is, the number of newly occurred or previously occurred failures that have not been monitored by FMEA analysis and have not formed effective corrective and preventive measures, push it to the quality management system (QMS system) to support the fault / risk control of products at all stages of the whole life cycle, strengthen the closed-loop management of product quality problems, and avoid the phenomenon of the separation of FMEA analysis and products.

[0025] The present invention also provides an embodiment for realizing the prior warning and prevention of product failures / risks - fault correlation deduction.

[0026] The YJ85A1 sensor fails, such as Figure 7 As shown, through the off-site fault information statistical table, it is found that there are 7 cases of new-built YJ85A1 sensor failures less than 10,000 kilometers in 2022 (6 cases of 0-kilometer failures and 1 case of 5,000-kilometer failure). Among them, 2 cases are Zhuzhou Tianli sensors and 5 cases are Hunan Xiangyi sensors. By querying the YJ85A1 speed sensor failures in 2021, it is also found that the Zhuzhou Tianli sensor had 3 cases of 0-kilometer failures in 2021.

[0027] As Figure 8 shown, through the QMS product configuration table, the product speed sensor number can be found from the product number.

[0028] As Figure 9 、 Figure 10 and Figure 11 shown, through the QMS incoming inspection, it can be found that 5 cases of Xiangyi sensor failures are for 3 adjacent batches of products, and all are spot checks. In 2022, 2 cases of Zhuzhou Tianli are for the same batch of products, and 3 cases in 2021 are for the same batch of products. Feedback to design and process quality.

[0029] As Figure 12 shown, it is found that in the design DFEMA, the preventive and corrective measures for speed sensor failures do not consider the situation where the sensor failures are not detected in the incoming inspection and the outgoing test, resulting in 0-kilometer failures.

[0030] According to the fault information, it is possible to consider optimizing the sampling inspection plan for the subsequent batches of speed sensors of this supplier. At the same time, consider optimizing the test items of the outgoing speed sensors to improve the screening ability for unqualified products, and update the measures to the product DFEMA and PFEMA.

[0031] Previously, due to the dispersion of the company's various data on different data platforms, and some information was still manually recorded in the form of ledgers, it was difficult for quality and technical personnel to discover the correlation between faults. After the subsequent big data platform integrates the data, it can more efficiently support the FEMA platform to help quality and technical personnel discover the connections between data such as product operation data and manufacturing data, and quickly analyze product faults to formulate preventive and corrective measures.

[0032] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Although the foregoing embodiments have been described in detail, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A method for obtaining, processing, and applying FMEA data, characterized in that, Constructing a FMEA data acquisition, processing and application system, which includes a FMEA data acquisition module, a FMEA data processing module, a FMEA knowledge storage module and a FMEA knowledge application module; The enterprise digital platform includes PLM business system, ERP business system, MRO business system, QMS business system and LIMS business system. On the basis of the enterprise digital platform, the FMEA data acquisition module is established by using the software interface development technology between the business systems. The FMEA data acquisition module uses the standard interface program and intermediate table transmission method with the business system to regularly and automatically obtain the original data of the product life cycle required for FMEA. The original data of the product life cycle includes design data, manufacturing data, operation data, failure data and verification data. The FMEA data processing module is established by using big data technology to process and transform the scattered and independent raw data of the product life cycle obtained in the FMEA data acquisition module into related data with certain regular conclusions that can quickly and efficiently support FMEA analysis. The data processing and transformation process is as follows: according to a certain data cleaning logic, duplicate data is eliminated, missing data is filled, and noise data is removed, and data cleaning is performed on the raw data of the product life cycle; after data cleaning, a unique identification number is found, and the man-machine-material-method-environmental test data related to the design, manufacturing, operation and maintenance of a single product / component in the whole life cycle is connected in series to complete the single number data connection; in the statistical information of the model, series, type, and field to which the single product / component number belongs, common attribute data is found to perform information association according to the data association rules to complete the association of similar data; The data associated with the same type will be automatically generated into a function network / fault network based on the product BOM / process route, structure tree / process tree and fault data, and the fault-related information will be collected. The SOD value and AP value will be automatically calculated by embedding the operation failure rate / manufacturing failure frequency algorithm model and adding FMEA team judgment nodes; the effectiveness of FMEA measures will be evaluated based on simulation, test, quality stability and operation evaluation results; the optimal measure recommendation will be given in combination with the AP value and measure effectiveness evaluation results, and the optimal measure recommendation will be stored in the FMEA knowledge storage module; The FMEA knowledge storage module is used to realize automatic acquisition and high-frequency iterative update of FMEA data. In the FMEA knowledge storage module, experts are extracted from the FMEA expert database to conduct FMEA knowledge review, and the data of the reviewed fault location, mode, cause, impact, detection and prevention measures are stored to form a fault experience database; the new fault frequency, failure rate, quality stability value, SOD value and AP value calculated iteratively by the storage platform are stored to form a risk prediction database; Compare the feedback data for implementing storage measures, the quality stability before and after improvement, the failure rate, and the AP value, and save them to form a measure evaluation library. At the same time, embed a knowledge graph module in the FMEA knowledge storage module. Through the given characteristics of the product function structure and environmental working conditions, explore the relationship chain of causes, modes, and impacts, and intelligently recommend related failure information to design / process personnel. For example, the knowledge graph function pushes failure dictionary information and the number of floating failures to the enterprise quality management and maintenance after-sales systems, realizing the functions of knowledge exploration, knowledge Q&A, and knowledge recommendation. The FMEA knowledge storage module also includes an FMEA trigger update mechanism, which uses information means to automatically identify the FMEA update timing. When there are changes in the factors of man, machine, material, method, environment, and measurement during the design, manufacturing, or operation stages, when new failures occur, or when the operating failure rate / manufacturing failure frequency / quality stability / PHM failure diagnosis value / AP value exceeds the threshold, it will be associated with the products / components and FMEA records that need to be updated and analyzed, and send an FMEA update instruction to the analysts in the FMEA data application module to ensure that the enterprise eliminates floating failures. Floating failures are failures that have not been analyzed and controlled by FMEA, enabling FMEA to play the role of pre-prevention of product risks / failures and full-life-cycle control. When starting FMEA, the FMEA knowledge storage module will automatically retrieve the stored failure experience library based on the product function and structure information filled in the FMEA data application module, and match and recommend relevant failure experience data to the analysts, including possible failure locations, modes, impacts, and causes. After the analysts conduct further discussions in the FMEA team and add or correct the failure analysis, it will trigger the automatic risk assessment (SOD value and AP value calculation) and matching recommendation of preventive measures in the FMEA knowledge storage module. If it is a new failure, the analysts will start the SOD scoring, AP value calculation, and formulation of preventive measures by themselves. The FMEA data application module sequentially includes processes such as new product structure and function analysis, new product failure impact analysis, new product failure mode analysis, new product failure cause analysis, new product SOD analysis, and new product FMEA preventive measure formulation. On the one hand, the FMEA data application module is connected to the FMEA knowledge storage module, used to receive failure experience, risk assessment, measure evaluation data, and FMEA update instructions, and send back the finalized product FMEA records to the FMEA knowledge storage module. On the other hand, it is connected to each business system of the enterprise, and transfers the measures formulated in the FMEA data application module to each business system according to the pre-set measure categories, respectively, to issue the implementation requirements of design / process improvement measures and simulation / test plan requirements; at the same time, trigger the FMEA data acquisition module to continuously send requests to relevant business systems to obtain corresponding data, so that the FMEA knowledge storage module can regularly receive the result data of the implementation of FMEA measures, realize the automatic transfer and implementation of measures and the continuous evaluation of effectiveness, and finally form a high-quality interaction process of FMEA data from dispersion to concentration and then to effective application.

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