Failure analysis method, device and equipment of elevator key components and storage medium
By constructing a combination of differentiated detection modules and quantitative evaluation, the problem of ambiguous positioning in the detection of key elevator components was solved, the accuracy and efficiency of detection were improved, and a detailed failure analysis report was generated.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN INST OF SPECIAL EQUIP INSPECTION & TEST
- Filing Date
- 2026-05-13
- Publication Date
- 2026-07-31
AI Technical Summary
Existing methods for inspecting key elevator components lack a systematic and collaborative mechanism, resulting in insufficient positioning accuracy and lengthy inspection cycles, failing to meet the demand for high timeliness, and relying on experience-based judgment for inspection results, which is not accurate enough.
Construct a combination of differentiated detection modules, match the module combination and weight allocation scheme according to the failure type, perform quantitative weighted fusion of detection data, and match it with the failure mode knowledge base to generate a failure analysis report.
It enables multi-dimensional failure location and cause analysis, improves the accuracy and efficiency of detection, reduces redundant detection, and shortens the analysis cycle.
Smart Images
Figure CN122491504A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of elevator safety testing, and in particular to a failure analysis method, apparatus, equipment and storage medium for key elevator components. Background Technology
[0002] Failure analysis of critical elevator components is a core aspect of ensuring safe elevator operation. Currently, the industry commonly uses methods such as visual inspection, X-ray fluoroscopy, scanning electron microscopy, and electrical performance testing for critical components like emergency power supplies, integrated controllers, and leveling sensors. However, in practical applications, existing technologies often rely on single inspection methods. For example, visual inspection can only identify visible defects such as surface cracks and corrosion; X-ray inspection focuses on internal welding defects; and electrical performance testing can only determine if a function has failed, but cannot trace the physical root cause of the failure. The lack of a systematic coordination mechanism between these inspection techniques means that inspectors often choose methods randomly based on experience, making it difficult to establish a quantitative correlation between inspection data and failure modes, resulting in insufficient accuracy in pinpointing the cause.
[0003] Furthermore, traditional methods often involve repeatedly performing multiple tests, resulting in lengthy testing cycles that fail to meet the high timeliness requirements of elevator maintenance. Simultaneously, the entire analysis process relies heavily on qualitative judgments based on human experience, failing to quantify the data generated by different types of failures, leading to highly subjective and inaccurate failure cause analysis.
[0004] Therefore, the ambiguity in failure localization caused by the poor adaptability and insufficient coverage of current detection methods is a problem that urgently needs to be solved. Summary of the Invention
[0005] The main objective of this application is to provide a failure analysis method, device, equipment, and storage medium for key elevator components, aiming to solve the technical problem of ambiguous failure location caused by poor adaptability and insufficient coverage of current detection methods.
[0006] To achieve the above objectives, this application proposes a failure analysis method for key elevator components, the method comprising: Obtain the failure types of key components in the elevator to be tested; According to the failure type, the corresponding module combination and weight allocation scheme are matched from the preset detection modules; The corresponding detection module is called according to the module combination to detect the key components and obtain quantitative detection data; The quantitative detection data is weighted and fused according to the weight allocation scheme to obtain a comprehensive quantitative value of failure characteristics. The comprehensive failure quantification value is matched with the failure mode knowledge base to obtain the failure analysis report of the key component.
[0007] In one embodiment, the step of matching the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type includes: Determine the module combination strategy corresponding to the failure type; According to the module combination strategy, the corresponding module combination is matched from the preset detection modules; According to the module combination, a weight allocation scheme corresponding to the detection module in the module combination is matched from the preset weight allocation rules.
[0008] In one embodiment, the failure types include corrosion failure, welding failure, electrical failure, and mechanical fatigue failure, and the step of determining the module combination strategy corresponding to the failure type includes: When the failure type is corrosion failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscope analysis module and electrical performance testing module matched from the preset detection modules; When the failure type is welding failure, the module combination strategy is determined to be a combination of an X-ray imaging module and an ultrasonic scanning module obtained from a preset detection module. When the failure type is electrical failure, the module combination strategy is determined to be a combination of an electrical performance testing module and an X-ray fluoroscopy module obtained from a preset detection module. When the failure type is mechanical fatigue failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscope analysis module and ultrasonic scanning module obtained from the preset detection modules.
[0009] In one embodiment, the step of weighted fusion of the quantized detection data according to the weight allocation scheme to obtain a comprehensive quantized value of failure features includes: The quantitative detection data is quantitatively extracted to obtain the quantitative detection results corresponding to each detection module. The quantitative detection results include quantitative results of appearance defects, quantitative results of radiographic testing, quantitative results of electron microscopy analysis, quantitative results of electrical performance, and quantitative results of ultrasonic testing. The quantitative detection results are normalized to map the quantitative detection results of different dimensions to the target detection value; The weight coefficients for each detection module are determined according to the weight allocation scheme. The weighted coefficients are summed with the corresponding target detection values to obtain the comprehensive quantitative value of the failure features.
[0010] In one embodiment, the step of matching the comprehensive failure quantification value with the failure mode knowledge base to obtain the failure analysis report of the key component includes: The comprehensive quantized value of the failure features and the target detection value corresponding to each detection module are matched with the typical failure modes stored in the failure mode knowledge base to obtain the matching result; The typical failure mode with the highest matching degree in the matching results is taken as the target failure mode; Based on the target failure mode and the preset failure physical mechanism, determine the failure location and failure cause that led to the target failure mode; A failure analysis report is generated based on the failure location and the failure cause.
[0011] In one embodiment, before the step of matching the comprehensive failure quantification value with the failure mode knowledge base to obtain the failure analysis report of the key component, the method further includes: The analysis objects of the elevator to be tested are obtained, including the emergency power supply, the elevator integrated controller, and the leveling sensor. Determine the corresponding typical failure modes based on the analysis object; Risk impact analysis was performed on the typical failure modes to obtain a comprehensive risk score; The typical failure modes are screened based on the comprehensive risk score, and a failure mode knowledge base is constructed based on the screening results.
[0012] In one embodiment, the step of performing risk impact analysis on the typical failure modes to obtain a comprehensive risk score includes: The severity score of failure is determined based on the degree of functional loss, safety hazard, and fault severity corresponding to the typical failure modes. The probability score is determined based on the cumulative running time, environmental stress level, and material fatigue life index corresponding to the typical failure modes. The testing difficulty score is determined based on the test type and testing equipment corresponding to the typical failure modes; A comprehensive risk score is calculated based on the failure severity score, the occurrence probability score, and the detection difficulty score.
[0013] Furthermore, to achieve the above objectives, this application also proposes a failure analysis device for key elevator components, the device comprising: The data receiving module is used to obtain the failure type of key components in the elevator under test; The detection scheme matching module is used to match the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type; The combined detection module is used to call the corresponding detection module according to the module combination to detect the key component and obtain quantitative detection data. The weighted fusion module is used to perform weighted fusion on the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure characteristics. The failure analysis module is used to match the comprehensive failure quantification value with the failure mode knowledge base to obtain a failure analysis report of the key component.
[0014] Furthermore, to achieve the above objectives, this application also proposes a failure analysis device for key elevator components, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the failure analysis method for key elevator components as described above.
[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the failure analysis method for key elevator components as described above.
[0016] This application provides a failure analysis method for key elevator components. The method includes: obtaining the failure type of the key component in the elevator to be tested; matching a corresponding module combination and weight allocation scheme from a preset detection module according to the failure type; calling the corresponding detection module to test the key component according to the module combination to obtain quantified detection data; weighting and fusing the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure characteristics; and matching the comprehensive quantified failure value with a failure mode knowledge base to obtain a failure analysis report for the key component. In summary, this application, by constructing differentiated detection module combinations corresponding to failure types, prioritizing the use of adaptable detection methods to reduce redundancy, and comprehensively quantifying and evaluating detection data of different dimensions, achieves multi-dimensional failure localization and cause analysis. This solves the problem of ambiguous failure localization caused by poor adaptability and insufficient coverage of current detection methods, and improves the accuracy and efficiency of failure analysis. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1This is a flowchart illustrating the first embodiment of the failure analysis method for key elevator components in this application. Figure 2 This is a flowchart illustrating the second embodiment of the failure analysis method for key elevator components in this application. Figure 3 This is a flowchart illustrating the third embodiment of the failure analysis method for key elevator components in this application. Figure 4 This is a schematic diagram of the module structure of the failure analysis device for key elevator components according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the failure analysis method for key elevator components in this application embodiment.
[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0023] It should be noted that the executing entity in this embodiment can be a failure analysis system for key elevator components, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the aforementioned failure analysis function for key elevator components. This embodiment does not specifically limit this. The following uses a failure analysis system for key elevator components as an example to describe this embodiment and the following embodiments.
[0024] Based on this, the embodiments of this application provide a failure analysis method for key elevator components, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the failure analysis method for key elevator components in this application.
[0025] In this embodiment, the failure analysis method for key elevator components includes steps S10 to S50: Step S10: Obtain the failure type of the key components in the elevator to be tested.
[0026] It should be noted that the failure type refers to the category of functional abnormalities or physical damage exhibited by the key components of the elevator under inspection during operation. In this embodiment, the failure types include, but are not limited to, four types: corrosion failure, welding failure, electrical failure, and mechanical fatigue failure. Corrosion failure mainly refers to chemical or electrochemical damage to the metal parts of components caused by environmental media (such as salt spray or humidity); welding failure refers to defects such as incomplete welds, detached welds, and porosity at weld points or joints; electrical failure refers to short circuits, open circuits, or parameter drift caused by deviations in electrical parameters such as circuit conductivity, insulation, and signal output accuracy from the normal range; mechanical fatigue failure refers to progressive damage such as fatigue cracks and fatigue wear caused by cyclic stress on materials. The failure types can be obtained by reading alarm information from the elevator self-diagnostic system, reviewing component maintenance history records, or by having the inspector input a preliminary assessment of the failure type based on the electrical fault conditions.
[0027] Step S20: Match the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type.
[0028] It should be noted that the preset detection modules include a visual inspection module, an X-ray imaging module, a scanning electron microscope (SEM) analysis module, an electrical performance testing module, and an ultrasonic scanning module. The module combination refers to the set of optimal detection methods selected from these five modules for a specific failure type. The weighting allocation scheme refers to the proportional coefficient assigned to each detection module in the module combination in the weighted fusion calculation, based on failure physics theory. Specifically, in this step, the system automatically calls the detection module combination most relevant to the physical mechanism of the determined failure type and assigns differentiated weights to each module to achieve optimized allocation of detection resources and maximize detection accuracy.
[0029] In one feasible implementation, step S20 specifically includes: Step S201: Determine the module combination strategy corresponding to the failure type.
[0030] It should be noted that the module combination strategy refers to the rule of selecting a set of detection modules from the preset detection modules that can cover the core physical characteristics of a specific failure type. The functions of each module are as follows: Visual Inspection Module: Employs a high-resolution industrial camera and a compatible optical imaging system and image recognition algorithm to identify visible defects such as surface cracks, corrosion, deformation, and ablation in components; X-ray Imaging Module: Uses a micro-focus X-ray detector with high-resolution digital imaging capabilities to detect welding defects (such as incomplete welds, weld failures, and porosity) and internal structural features of samples, with a thickness range of 0.1mm to 50mm; Scanning Electron Microscopy Analysis Module: Employs a field emission scanning electron microscope equipped with an energy dispersive spectroscopy (EDS) spectrometer to observe microscopic surface morphology and micro-area composition analysis, enabling in-depth analysis of corrosion product composition, fatigue crack propagation paths, and material wear characteristics; Electrical Performance Testing Module: Integrates a high-precision digital multimeter, insulation resistance tester, oscilloscope, and other equipment to test electrical parameters such as circuit continuity, insulation resistance, voltage stability, and signal output accuracy; Ultrasonic Scanning Module: Employs an ultrasonic detector supporting C-scan / B-scan imaging to detect internal layering, bubbles, porosity, and internal defects in non-metallic materials.
[0031] Step S202: Match the corresponding module combination from the preset detection modules according to the module combination strategy.
[0032] It should be noted that, since the core failure characteristics corresponding to different failure types are significantly different, different combinations of detection modules need to be used to achieve accurate coverage.
[0033] In one feasible implementation, step S202 specifically includes: Step A10: When the failure type is corrosion failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscopy analysis module and electrical performance testing module obtained from the preset detection modules.
[0034] It should be noted that when the failure type is corrosion failure, the system will prioritize the use of a combination of the visual inspection module, the scanning electron microscope (SEM) analysis module, and the electrical performance testing module. The principle behind this is that the core characteristics of corrosion failure are the surface corrosion morphology (which requires visual inspection for identification), the composition of corrosion products and the microscopic corrosion depth (which require SEM analysis for identification), and the electrical performance degradation caused by corrosion (such as changes in contact resistance, which require electrical performance testing for identification).
[0035] Step A20: When the failure type is welding failure, the module combination strategy is determined to be a combination of an X-ray imaging module and an ultrasonic scanning module obtained from the preset detection modules.
[0036] It should be noted that when the failure type is welding failure, the system will prioritize the use of the combination of the X-ray imaging module and the ultrasonic scanning module. The principle is that the core feature of welding failure is internal welding structural defects (such as incomplete welds, porosity, and cracks). X-ray imaging can achieve two-dimensional / three-dimensional imaging of the welding area, while ultrasonic scanning can detect defects such as delamination and lack of fusion inside the weld. The two working together can cover all dimensions of welding failure characteristics.
[0037] Step A30: When the failure type is electrical failure, the module combination strategy is determined to be a combination of an electrical performance test module and an X-ray fluoroscopy module obtained from the preset detection modules.
[0038] It should be noted that when the failure type is electrical failure, the system will prioritize the use of the combination of the electrical performance testing module and the X-ray imaging module. The principle is as follows: electrical failure requires the electrical performance testing module to accurately quantify parameter deviations (such as insulation resistance attenuation rate and voltage deviation rate), while the X-ray imaging module is used to detect structural causes of electrical failure, such as short circuits, open circuits, or abnormal solder joints within the circuit board.
[0039] Step A40: When the failure type is mechanical fatigue failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscope analysis module and ultrasonic scanning module obtained from the preset detection modules.
[0040] It should be noted that when the failure type is mechanical fatigue failure, the system will prioritize the use of the combination of the visual inspection module, the scanning electron microscope (SEM) analysis module, and the ultrasonic scanning module. The principle is that mechanical fatigue failure involves the generation of surface fatigue cracks (visual inspection), crack propagation path and fracture surface microstructure (SEM analysis), and the accumulation of internal fatigue damage (ultrasonic scanning). The three modules work together to achieve early warning and precise location of fatigue failure.
[0041] Step S203: Match the weight allocation scheme corresponding to the detection module in the module combination from the preset weight allocation rules according to the module combination.
[0042] It should be noted that the weighting scheme refers to the set of differentiated weight values assigned to each detection module in the module combination for different failure types. The weighting is based on PoF (Physics of Failure) and determined in conjunction with the core influencing factors of different failure types. The specific weighting rules are as follows: For surface failures (such as corrosion and cracks), visual inspection accounts for 30%, scanning electron microscopy (SEM) analysis for 40%, and electrical performance testing for 30%; for internal structural failures (such as welding defects and delamination), X-ray imaging accounts for 45%, ultrasonic scanning for 45%, and visual inspection for 10%; for electrical failures (such as short circuits and insulation aging), electrical performance testing accounts for 50%, X-ray imaging for 30%, and SEM analysis for 20%; for mechanical fatigue failures (such as component fatigue cracks and fatigue wear), SEM analysis accounts for 45%, ultrasonic scanning for 35%, and visual inspection for 20%.
[0043] Additionally, it should be noted that the above weight allocation is not fixed and can be dynamically optimized based on feedback from historical detection data.
[0044] Step S30: Based on the module combination, call the corresponding detection module to detect the key component and obtain quantitative detection data.
[0045] It should be noted that the system will sequentially or in parallel call the corresponding detection modules to perform actual detection on the key components to be inspected, based on the module combination determined by 0. The quantitative detection data refers to the raw detection values with clear physical dimensions output by each detection module, rather than qualitative descriptions. Specifically, it includes: quantitative results of appearance defects, such as defect area, crack length, corrosion coverage, etc.; quantitative results of radiographic testing, such as welding defect size, porosity, etc.; quantitative results of electron microscopy analysis, such as corrosion depth, crack width, surface roughness, etc.; quantitative results of electrical performance, such as parameter deviation rate, insulation resistance attenuation rate, voltage deviation value, etc.; and quantitative results of ultrasonic testing, such as equivalent size of internal defects, etc.
[0046] Step S40: Perform weighted fusion on the quantified detection data according to the weight allocation scheme to obtain the comprehensive quantified value of failure characteristics.
[0047] It should be noted that this step aims to address the issue of inconsistent dimensions and inability to directly fuse multi-source heterogeneous detection data. By quantifying and extracting the raw data, normalizing its dimensions, and performing weighted fusion processing, the data output by different detection modules, which have different physical dimensions, are transformed into a unified dimensionless comprehensive quantitative value, thereby achieving an objective quantitative assessment of the degree of failure.
[0048] In one feasible implementation, step S40 specifically includes: Step S401: Quantify and extract the quantitative detection data to obtain the quantitative detection results corresponding to each detection module. The quantitative detection results include quantitative results of appearance defects, quantitative results of radiographic testing, quantitative results of electron microscopy analysis, quantitative results of electrical performance, and quantitative results of ultrasonic testing.
[0049] It should be noted that the quantitative extraction refers to extracting core quantitative indicators directly related to failure assessment from the raw output data of each detection module. For example, the visual inspection module outputs defect images, which need to be extracted using image processing algorithms to obtain quantitative values such as defect area and crack length; the scanning electron microscope outputs microscopic morphology images, which need to be extracted to obtain values such as corrosion depth and crack width.
[0050] Step S402: Normalize the quantized detection results to map the quantized detection results of different dimensions to the target detection value.
[0051] It should be noted that the normalization process refers to using a linear normalization formula to convert the original quantized values of each module into dimensionless target detection values, so that their value range is uniformly mapped to the [0,1] interval. The closer the target detection value is to 1, the more obvious the failure characteristics detected by that module are.
[0052] Step S403: Determine the weight coefficients corresponding to each detection module according to the weight allocation scheme.
[0053] It should be noted that the weighting coefficient is the weight value determined in step S203. The weighting is determined based on the weighting scheme corresponding to the current failure type. For example, if the current failure type is corrosion failure (a type of surface failure), then the visual inspection weighting is... =0.3, Scanning Electron Microscopy Analysis Weights =0.4, Electrical performance test weight =0.3.
[0054] Step S404: The weight coefficients are weighted and summed with the corresponding target detection values to obtain the comprehensive quantitative value of the failure features.
[0055] It should be noted that the comprehensive quantitative value of the failure characteristics (denoted as F) is calculated using a weighted summation algorithm, as shown in the following formula:
[0056] Where F is the comprehensive quantitative value of failure characteristics. Let be the weight coefficient of the i-th detection module. is the target detection value after normalization processing by the i-th detection module.
[0057] Step S50: Match the comprehensive failure quantification value with the failure mode knowledge base to obtain the failure analysis report of the key component.
[0058] It should be noted that the aforementioned failure mode knowledge base is a pre-constructed database of "failure mode - detection feature - quantization threshold" based on the PoF-FMEA (Physical Failure-Failure Mode and Effects Analysis) method. The matching process refers to combining the calculated comprehensive quantization value F of the failure features with the normalized target detection value of each detection module. The system compares the failure mode with the quantification threshold range of typical failure modes stored in the knowledge base to determine the closest failure mode, thereby tracing the root cause of the failure and generating an analysis report.
[0059] This embodiment provides a failure analysis method for key elevator components. The method includes: acquiring the failure type of the key component in the elevator to be tested; matching a corresponding module combination and weight allocation scheme from a preset detection module according to the failure type; calling the corresponding detection module according to the module combination to detect the key component and obtain quantified detection data; weighting and fusing the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure characteristics; and matching the comprehensive quantified failure value with a failure mode knowledge base to obtain a failure analysis report for the key component. In summary, this embodiment, by constructing differentiated detection module combinations corresponding to failure types, prioritizing the use of adaptable detection methods to reduce redundancy, and comprehensively quantifying and evaluating detection data of different dimensions, achieves multi-dimensional failure localization and cause analysis. This solves the problem of ambiguous failure localization caused by poor adaptability and insufficient coverage of current detection methods, improving the accuracy and efficiency of failure analysis.
[0060] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the failure analysis method for key elevator components of this application. Step S50 includes: Step S501: Match the comprehensive quantization value of the failure features and the target detection value corresponding to each detection module with the typical failure modes stored in the failure mode knowledge base to obtain the matching result.
[0061] It should be noted that the failure mode knowledge base includes typical failure modes for three types of key components: emergency power supply, elevator integrated controller, and leveling sensor. It also records the range of comprehensive quantitative values (F-values) of typical failure characteristics for each mode, as well as the target detection values for each detection module. Threshold.
[0062] Additionally, it should be noted that the typical failure modes specifically include six high-risk failure modes: contactor contact corrosion failure in the elevator integrated controller, solder joint detachment failure in the leveling sensor circuit board, emergency power supply battery bulging failure, fatigue fracture failure of internal wiring in the emergency power supply, welding failure of the elevator integrated controller power module, and electrical failure of the leveling sensor signal output. Examples of application for each mode in the knowledge base are as follows: For contactor contact corrosion failure, the typical F-value range is 0.51~0.71, while requiring a target detection value of corrosion depth ≥0.6 under scanning electron microscopy and a target detection value of contact resistance deviation rate ≥0.7 in electrical performance; for solder joint detachment failure, the typical F-value range is 0.42~0.62, requiring a target detection value of porosity ≥0.5 under X-ray inspection and a target detection value of defect equivalent size ≥0.6 under ultrasonic scanning.
[0063] Understandably, in this step, the system will perform interval matching between the currently calculated F-value and the typical F-value range of each typical failure mode, filtering out candidate failure modes whose F-values fall within their range; and then, the various detection modules will... The value is compared item by item with the quantization threshold of the core detection features of the candidate failure mode to calculate the matching degree. The matching degree can be determined by weighted Euclidean distance, cosine similarity, or rule-based distance measurement methods. The higher the matching degree, the higher the degree of fit between the current detection data and the failure mode.
[0064] Step S502: Select the typical failure mode with the highest matching degree in the matching results as the target failure mode.
[0065] It should be noted that in this step, the system will sort the candidate failure modes in descending order of their matching scores from the obtained matching results, and select the typical failure mode with the highest matching score as the target failure mode. The target failure mode is the most likely failure mode for the critical component to be detected, as determined by the system.
[0066] Step S503: Based on the target failure mode and the preset failure physical mechanism, determine the failure location and failure cause that led to the target failure mode.
[0067] It should be noted that the preset failure physics mechanism refers to the theoretical explanation of the intrinsic physical or chemical processes of various failure modes based on the principles of materials science, physics, and chemistry. Specifically, the system will determine the underlying physical or chemical processes based on the target failure mode and the various modules in the detection data. By combining the spatial distribution characteristics of the values (such as concentrated areas of visual defects, X-ray inspection defect coordinates, and ultrasonic scanning defect depth locations), and the three-dimensional structural model of the component, the physical location of the failure can be precisely determined. For example, for "solder joint detachment failure of a flat-layer sensor circuit board," X-ray inspection can output the position of the defect in the plane coordinate system of the circuit board, and by combining the circuit board layer thickness information, it can be determined which layer of solder joint the defect is located on.
[0068] Additionally, it should be noted that the system also calls a preset failure physics mechanism library to map the target failure mode to its corresponding failure cause. The specific mapping relationships are as follows: Salt spray corrosion mechanism: For corrosion failure target modes (such as contactor contact corrosion), the failure cause is that chloride ions penetrate the corrosion product film in a humid environment, initiating pitting corrosion and accelerating electrochemical corrosion, leading to a surge in contact resistance; Thermal stress fatigue mechanism: For mechanical fatigue failure target modes (such as circuit fatigue fracture), the failure cause is that the thermal expansion and contraction stress caused by temperature cycling does not match the material's thermal expansion coefficient, resulting in the initiation and propagation of microcracks in the stress concentration area; Electrical stress overload mechanism: For electrical failure target modes (such as signal output electrical failure), the failure cause is insulation breakdown, circuit short circuit, or component parameter drift caused by electromagnetic interference or voltage surge; Mechanical vibration fatigue mechanism: For welding failure target modes (such as solder joint detachment), the failure cause is that the mechanical vibration stress during elevator operation is transmitted to the circuit board, initiating fatigue cracks at the stress concentration point of the solder joint and propagating to complete fracture.
[0069] Step S504: Generate a failure analysis report based on the failure location and the failure cause.
[0070] It should be noted that after obtaining the failure location and cause, the system automatically integrates all evidence information from this inspection to generate a standardized failure analysis report. The report content includes at least: the failed component and its specific failure location, the determined target failure mode, core inspection evidence (i.e., the target detection values and comprehensive quantitative values of each inspection module), the traced failure cause, and recommended maintenance or preventative measures. The report can be output as a PDF document, a structured data file, or a visual dashboard interface, supporting inspection personnel in viewing, archiving, and uploading to the elevator operation and maintenance management system.
[0071] In this embodiment, by synchronously incorporating the comprehensive quantification value and the target detection value of each module into the matching dimension, the accuracy and anti-interference capability of failure mode identification are improved; by introducing the physical mechanism of failure for root cause tracing, a standardized report containing failure location, core evidence and cause analysis is generated, realizing the automation and standardization of failure cause analysis, avoiding repeated detection, shortening the failure detection cycle of a single component from several days to several hours, and improving the efficiency of failure analysis.
[0072] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the failure analysis method for key elevator components of this application. Before step S50, the method further includes: Step B10: Obtain the analysis objects of the elevator to be tested, including the emergency power supply, the elevator integrated controller, and the leveling sensor.
[0073] It should be noted that the analysis object refers to the specific category of key elevator components targeted by the method of this invention. In this embodiment, the analysis object is limited to three categories: emergency power supply, elevator integrated controller, and leveling sensor. The selection is based on the fact that these three types of components correspond to the three core functions of elevator: energy supply, operation control, and leveling positioning. Failure of any one of them may lead to major safety accidents such as elevator entrapment, overshooting / bottoming, etc., and they are key targets for elevator safety inspection.
[0074] Step B20: Determine the corresponding typical failure mode based on the analysis object.
[0075] It should be noted that typical failure modes refer to specific failure methods that occur frequently or cause significant damage in each type of component, identified by analyzing historical failure data, industry failure cases, and combining component structural design and material characteristics. For example, typical failure modes for emergency power supplies include battery bulging failure and internal circuit fatigue fracture failure; typical failure modes for elevator integrated controllers include contactor contact corrosion failure and power module welding failure; and typical failure modes for leveling sensors include circuit board solder joint desoldering failure and signal output electrical failure.
[0076] Step B30: Perform risk impact analysis on the typical failure modes to obtain a comprehensive risk score.
[0077] It should be noted that in this step, the risk impact analysis mainly adopts the method based on failure physics, failure mode and effects analysis, namely the PoF-FMEA method. This method objectively assesses the risk level of each typical failure mode by introducing quantitative scores across three dimensions: severity (S), probability of occurrence (O), and detection difficulty (D). The comprehensive risk score refers to the combined score calculated using the quantitative scores across these three dimensions, used to objectively measure the risk of each typical failure mode.
[0078] Step B40: Filter the typical failure modes based on the comprehensive risk score, and construct a failure mode knowledge base based on the filtering results.
[0079] It should be noted that the purpose of the screening is to concentrate resources on the critical failure modes that have the greatest impact on the safe operation of elevators, avoiding an overly complex knowledge base that would reduce matching efficiency. This embodiment sets an RPN ≥ 80 as a high-risk threshold, retaining only typical failure modes with a comprehensive risk score greater than or equal to 80 in the failure mode knowledge base; modes with lower risk scores are not included. The retained modes after screening will be further combined with industry standards (such as the elevator testing standard GB / T10058-2023), component factory testing indicators, and failure threshold test data to determine the quantitative threshold of the corresponding detection characteristics for each failure mode, i.e., the failure judgment threshold. For example, for contactor contact corrosion failure, a salt spray corrosion test is used to test the corrosion depth and contact resistance deviation rate of the contacts at different corrosion times. When the corrosion depth ≥ 50 μm and the contact resistance deviation rate ≥ 30%, the contacts cannot conduct normally and are judged as failures. Therefore, these values are determined as the quantitative threshold for this failure mode. Finally, the selected failure modes, their corresponding core detection features, quantification thresholds, and typical F-value ranges calculated based on the weight allocation of the corresponding failure types are integrated into a failure mode knowledge base for matching and calling in subsequent steps.
[0080] In one feasible implementation, step B30 specifically includes: Step B301: Determine the failure severity score based on the degree of functional loss, safety hazard, and fault degree corresponding to the typical failure mode.
[0081] It should be noted that the failure severity score (S) reflects the degree of impact of the failure mode on the function, safety, and system cascading of elevator components. The degree of functional loss refers to the extent to which the failure leads to a degradation or complete loss of core functions; the degree of safety hazard refers to whether the failure involves passenger safety and the severity of the accident; and the degree of failure refers to the scope of system downtime and maintenance complexity caused by the failure. The score uses a 1-10 scale: when the failure leads to major safety accidents such as elevator entrapment, overshooting, or bottoming out, or complete loss of core operating functions, the score is 9-10; when the failure leads to a degradation of major functions, posing potential safety hazards and requiring immediate shutdown for repair, the score is 7-8; when the failure leads to partial functional failure, not affecting basic operation but with performance degradation, the score is 5-6; and when it is only minor cosmetic damage with no impact on operation, the score is 2-4. For example, a bulging emergency power battery failure directly leads to power outage and disconnection, which is a serious failure endangering safety, and its severity score is determined to be 9 points.
[0082] Step B302: Determine the probability score based on the cumulative running time, environmental stress level, and material fatigue life index corresponding to the typical failure mode.
[0083] It should be noted that cumulative runtime refers to the actual operating time of a component since its last overhaul or replacement; environmental stress level refers to the classification of stress factors such as temperature, humidity, salt spray concentration, and vibration intensity in the component's service environment. For example, coastal salt spray areas are considered extremely harsh environments, corresponding to a high environmental stress level; the material fatigue life index refers to the number of cycles required for a material to reach fatigue failure under a specific stress, predicted based on the component's SN curve (stress-life curve) and accelerated life test data. The scoring uses a 1-10 point scale: when the failure mode occurs frequently, at least once a month, or in an extremely harsh environment, the score is 9-10 points; when the failure is of medium frequency, occurring once a quarter, the score is 7-8 points; when the failure is of low frequency, occurring once a year, the score is 4-6 points; and when the failure is of extremely low probability, occurring only occasionally under extreme conditions, the score is 1-3 points.
[0084] Step B303: Determine the testing difficulty score based on the test type and testing equipment corresponding to the typical failure modes.
[0085] It should be noted that the experimental type refers to the category of detection method required to identify the failure characteristic (such as destructive testing, non-destructive testing, microscopic analysis, etc.). The detection equipment refers to the accuracy level, penetration capability, and operational complexity of the required detection equipment. The scoring uses a 1-10 point scale: when the failure is an internal microscopic defect, such as subsurface fatigue cracks, which cannot be detected by visual inspection or conventional electrical testing and requires destruction of the sample or reliance on high-precision microscopic equipment, the score is 9-10 points; when the failure is an internal structural defect, such as a cold solder joint, which requires specialized imaging for identification, the score is 7-8 points; when the failure is a surface-obvious defect or a single parameter deviation, which can be detected by conventional instruments, the score is 4-6 points; when the failure is a visually visible damage or an obvious alarm signal, the score is 1-3 points. For example, fatigue fracture failure of internal wiring in an emergency power supply is a deep microscopic defect, extremely difficult to detect by conventional methods, and requires high-magnification electron microscopy; its detection difficulty score is determined to be 9 points.
[0086] Step B304: Calculate a comprehensive risk score based on the failure severity score, the occurrence probability score, and the detection difficulty score.
[0087] It should be noted that the formula for calculating the comprehensive risk score RPN is as follows:
[0088] Here, S represents the failure severity score, O represents the probability of occurrence score, and D represents the detection difficulty score. The higher the overall risk score (RPN), the higher the risk level of the failure mode, and the more priority and resources are needed for repair.
[0089] In this embodiment, a three-dimensional quantitative scoring system based on failure physics, PoF-FMEA, is introduced. Objective scores for severity, probability of occurrence, and detection difficulty are determined based on specific judgment parameters such as functional loss and safety hazard level, runtime and environmental stress level, failure feature concealment and detection equipment limits. A comprehensive risk score is calculated to screen failure modes. Quantitative thresholds and typical F-value ranges verified by industry standards and failure criticality test data are incorporated into the knowledge base, completing the scientific construction of the failure mode knowledge base. This solves the problems of low detection coverage and poor adaptability for different failure types in traditional analysis, and improves the accuracy of failure analysis under different influencing factors.
[0090] This application also provides a failure analysis device for key elevator components; please refer to [reference needed]. Figure 4 The failure analysis device for the key elevator components includes: The data receiving module 10 is used to acquire the failure type of key components in the elevator to be tested; The detection scheme matching module 20 is used to match the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type; The combined detection module 30 is used to call the corresponding detection module according to the module combination to detect the key component and obtain quantitative detection data. The weighted fusion module 40 is used to perform weighted fusion on the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure characteristics; The failure analysis module 50 is used to match the comprehensive failure quantification value with the failure mode knowledge base to obtain a failure analysis report of the key component.
[0091] The elevator critical component failure analysis device provided in this application, employing the elevator critical component failure analysis method described in the above embodiments, can solve the technical problem of ambiguous failure localization caused by poor adaptability and insufficient coverage of current detection methods. Compared with the prior art, the beneficial effects of the elevator critical component failure analysis device provided in this application are the same as those of the elevator critical component failure analysis method provided in the above embodiments, and other technical features in the elevator critical component failure analysis device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0092] In one embodiment, the detection scheme matching module 20 is further configured to determine the module combination strategy corresponding to the failure type; match the corresponding module combination from the preset detection modules according to the module combination strategy; and match the weight allocation scheme corresponding to the detection module in the module combination from the preset weight allocation rules according to the module combination.
[0093] In one embodiment, the detection scheme matching module 20 is further configured to: when the failure type is corrosion failure, determine a module combination strategy of a combination of an appearance visual inspection module, a scanning electron microscope analysis module, and an electrical performance testing module matched from a preset detection module; when the failure type is welding failure, determine a module combination strategy of a combination of an X-ray imaging module and an ultrasonic scanning module matched from a preset detection module; when the failure type is electrical failure, determine a module combination strategy of a combination of an electrical performance testing module and an X-ray imaging module matched from a preset detection module; and when the failure type is mechanical fatigue failure, determine a module combination strategy of a combination of an appearance visual inspection module, a scanning electron microscope analysis module, and an ultrasonic scanning module matched from a preset detection module.
[0094] In one embodiment, the weighted fusion module 40 is further configured to perform quantitative extraction on the quantitative detection data to obtain quantitative detection results corresponding to each detection module. The quantitative detection results include quantitative results of appearance defects, quantitative results of radiographic testing, quantitative results of electron microscopy analysis, quantitative results of electrical performance, and quantitative results of ultrasonic testing. The quantitative detection results are normalized to map the quantitative detection results of different dimensions to target detection values. The weight coefficients corresponding to each detection module are determined according to the weight allocation scheme. The weight coefficients are weighted and summed with the corresponding target detection values to obtain a comprehensive quantitative value of failure characteristics.
[0095] In one embodiment, the failure analysis module 50 is further configured to match the comprehensive quantitative value of the failure features and the target detection value corresponding to each detection module with the typical failure modes stored in the failure mode knowledge base to obtain a matching result; take the typical failure mode with the highest matching degree in the matching result as the target failure mode; determine the failure location and failure cause that lead to the target failure mode according to the target failure mode and the preset failure physical mechanism; and generate a failure analysis report according to the failure location and the failure cause.
[0096] In one embodiment, the failure analysis module 50 is further configured to acquire the analysis object of the elevator to be tested, the analysis object including emergency power supply, elevator integrated controller and leveling sensor; determine the corresponding typical failure mode according to the analysis object; perform risk impact analysis on the typical failure mode to obtain a comprehensive risk score; filter the typical failure modes according to the comprehensive risk score, and construct a failure mode knowledge base according to the filtering results.
[0097] In one embodiment, the failure analysis module 50 is further configured to determine a failure severity score based on the degree of functional loss, safety hazard, and fault degree corresponding to the typical failure mode; determine an occurrence probability score based on the cumulative running time, environmental stress level, and material fatigue life index corresponding to the typical failure mode; determine a detection difficulty score based on the experimental type and detection equipment corresponding to the typical failure mode; and calculate a comprehensive risk score based on the failure severity score, the occurrence probability score, and the detection difficulty score.
[0098] This application provides a failure analysis device for key elevator components. The failure analysis device for key elevator components includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the failure analysis method for key elevator components in the above embodiment 1.
[0099] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a failure analysis device suitable for implementing the embodiments of this application for elevator key components. The failure analysis device for elevator key components in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The failure analysis device for key elevator components shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0100] like Figure 5As shown, the failure analysis device for critical elevator components may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the failure analysis device for critical elevator components. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the failure analysis equipment for elevator critical components to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows failure analysis equipment for elevator critical components with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.
[0101] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0102] The failure analysis device for key elevator components provided in this application, employing the failure analysis method for key elevator components described in the above embodiments, can solve the technical problem of ambiguous failure localization caused by poor adaptability and insufficient coverage of current detection methods. Compared with the prior art, the beneficial effects of the failure analysis device for key elevator components provided in this application are the same as those of the failure analysis method for key elevator components provided in the above embodiments, and other technical features of this failure analysis device for key elevator components are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
[0103] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0104] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0105] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the failure analysis method for key elevator components in the above embodiments.
[0106] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0107] The aforementioned computer-readable storage medium may be included in the failure analysis equipment for critical elevator components; or it may exist independently and not be assembled into the failure analysis equipment for critical elevator components.
[0108] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the failure analysis device for critical elevator components, the failure analysis device for critical elevator components performs the following actions: acquires the failure type of the critical component in the elevator to be tested; matches a corresponding module combination and weight allocation scheme from a preset detection module according to the failure type; calls the corresponding detection module to test the critical component according to the module combination, and obtains quantified detection data; performs weighted fusion of the quantified detection data according to the weight allocation scheme, and obtains a comprehensive quantified value of failure characteristics; and matches the comprehensive quantified failure value with a failure mode knowledge base to obtain a failure analysis report for the critical component.
[0109] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0111] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0112] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the failure analysis method for the aforementioned key elevator components. This solves the technical problem of ambiguous failure localization caused by poor adaptability and insufficient coverage of current detection methods. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the failure analysis method for key elevator components provided in the above embodiments, and will not be elaborated upon here.
[0113] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A failure analysis method for key elevator components, characterized in that, The method includes: Obtain the failure types of key components in the elevator to be tested; According to the failure type, the corresponding module combination and weight allocation scheme are matched from the preset detection modules; The corresponding detection module is called according to the module combination to detect the key components and obtain quantitative detection data; The quantitative detection data is weighted and fused according to the weight allocation scheme to obtain a comprehensive quantitative value of failure characteristics. The comprehensive failure quantification value is matched with the failure mode knowledge base to obtain the failure analysis report of the key component.
2. The method as described in claim 1, characterized in that, The step of matching the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type includes: Determine the module combination strategy corresponding to the failure type; According to the module combination strategy, the corresponding module combination is matched from the preset detection modules; According to the module combination, a weight allocation scheme corresponding to the detection module in the module combination is matched from the preset weight allocation rules.
3. The method as described in claim 2, characterized in that, The failure types include corrosion failure, welding failure, electrical failure, and mechanical fatigue failure. The step of determining the module combination strategy corresponding to the failure type includes: When the failure type is corrosion failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscope analysis module and electrical performance testing module obtained from the preset detection modules; When the failure type is welding failure, the module combination strategy is determined to be a combination of an X-ray imaging module and an ultrasonic scanning module obtained from a preset detection module. When the failure type is electrical failure, the module combination strategy is determined to be a combination of an electrical performance testing module and an X-ray fluoroscopy module obtained from a preset detection module. When the failure type is mechanical fatigue failure, the module combination strategy is determined to be a combination of appearance visual inspection module, scanning electron microscope analysis module and ultrasonic scanning module obtained from the preset detection modules.
4. The method as described in claim 1, characterized in that, The step of weighted fusing the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure features includes: The quantitative detection data is quantitatively extracted to obtain the quantitative detection results corresponding to each detection module. The quantitative detection results include quantitative results of appearance defects, quantitative results of radiographic testing, quantitative results of electron microscopy analysis, quantitative results of electrical performance, and quantitative results of ultrasonic testing. The quantitative detection results are normalized to map the quantitative detection results of different dimensions to the target detection value; The weight coefficients for each detection module are determined according to the weight allocation scheme. The weighted coefficients are summed with the corresponding target detection values to obtain the comprehensive quantitative value of the failure features.
5. The method as described in claim 1, characterized in that, The step of matching the comprehensive failure quantification value with the failure mode knowledge base to obtain the failure analysis report of the key component includes: The comprehensive quantized value of the failure features and the target detection value corresponding to each detection module are matched with the typical failure modes stored in the failure mode knowledge base to obtain the matching result; The typical failure mode with the highest matching degree in the matching results is taken as the target failure mode; Based on the target failure mode and the preset failure physical mechanism, determine the failure location and failure cause that led to the target failure mode; A failure analysis report is generated based on the failure location and the failure cause.
6. The method as described in claim 1, characterized in that, Before the step of matching the comprehensive failure quantification value with the failure mode knowledge base to obtain the failure analysis report of the key component, the method further includes: The analysis objects of the elevator to be tested are obtained, including the emergency power supply, the elevator integrated controller, and the leveling sensor. Determine the corresponding typical failure modes based on the analysis object; Risk impact analysis was performed on the typical failure modes to obtain a comprehensive risk score; The typical failure modes are screened based on the comprehensive risk score, and a failure mode knowledge base is constructed based on the screening results.
7. The method as described in claim 6, characterized in that, The steps for conducting risk impact analysis on the typical failure modes to obtain a comprehensive risk score include: The severity score of failure is determined based on the degree of functional loss, safety hazard, and fault severity corresponding to the typical failure modes. The probability score is determined based on the cumulative running time, environmental stress level, and material fatigue life index corresponding to the typical failure modes. The testing difficulty score is determined based on the test type and testing equipment corresponding to the typical failure modes; A comprehensive risk score is calculated based on the failure severity score, the occurrence probability score, and the detection difficulty score.
8. A failure analysis device for a key elevator component, characterized in that, The device includes: The data receiving module is used to obtain the failure type of key components in the elevator under test; The detection scheme matching module is used to match the corresponding module combination and weight allocation scheme from the preset detection modules according to the failure type; The combined detection module is used to call the corresponding detection module according to the module combination to detect the key component and obtain quantitative detection data. The weighted fusion module is used to perform weighted fusion on the quantified detection data according to the weight allocation scheme to obtain a comprehensive quantified value of failure characteristics. The failure analysis module is used to match the comprehensive failure quantification value with the failure mode knowledge base to obtain a failure analysis report of the key component.
9. A failure analysis device for key elevator components, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the failure analysis method for elevator critical components as claimed in any one of claims 1 to 7.
10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the failure analysis method for key elevator components as described in any one of claims 1 to 7.