Intelligent inspection method for lifting equipment based on multiple failure modes

Abstract

The invention discloses an intelligent inspection method for hoisting equipment based on multiple failure modes, and relates to the technical field of health management of the hoisting equipment, and the method comprises the steps: carding the whole life cycle typical damage and failure modes of a port crane structure, mechanism and electric control system, and constructing a typical failure / failure mode library; and based on the typical failure / fault mode library, analyzing a fault causal chain, and constructing a multi-level failure / fault tree model covering the structure and the transmission and electric control system. Cooperative monitoring of a multi-system coupling damage mode is achieved by constructing a multi-level failure / fault tree model covering a structure, a transmission system and an electric control system, traditional routing inspection is only aimed at a single system and cross-system faults prone to missing detection, and then relevance of mechanical abrasion, hydraulic leakage and electrical faults is synchronously tracked through fault causal chain analysis, so that the fault detection accuracy is improved. Electric-mechanical-hydraulic full-chain diagnosis is formed, and the positioning precision of complex faults is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hoisting equipment health management, and particularly relates to a hoisting equipment intelligent inspection method based on multiple failure modes. BACKGROUND

[0002] Hoisting equipment, such as cranes, tower cranes, elevators, etc., is an important equipment indispensable in modern industry and construction engineering, which undertakes important tasks such as material handling and heavy lifting, and is widely used in many fields such as construction, port, mine and power. With the increase of equipment use intensity and the complication of working environment, hoisting equipment may fail due to multiple reasons, such as mechanical part wear, electrical system failure, hydraulic system failure, etc. These failure modes usually have diversity and complexity, and different failure / fault modes may have different effects on the operation safety and efficiency of the equipment. Therefore, real-time monitoring, diagnosis and prediction can be carried out for different failure modes to effectively prevent accidents.

[0003] Traditional crane inspection methods are often aimed at a single failure mode, lack the ability to cooperatively detect multiple systems such as structure, mechanism and electrical control system, resulting in missed detection or misjudgment. Moreover, the traditional inspection method is time-consuming and difficult to efficiently locate the key damage position, resulting in low inspection efficiency. Therefore, how to establish a typical failure / fault mode library and a fault tree, determine the key damage types of each system and their relevance, and build a point inspection integrated framework based on failure modes, combined with an intelligent inspection path planning algorithm to improve the inspection accuracy, is the problem to be solved by the present application. Therefore, a hoisting equipment intelligent inspection method based on multiple failure modes is proposed. SUMMARY

[0004] The present application aims to provide a hoisting equipment intelligent inspection method based on multiple failure modes to solve the problems raised in the background art.

[0005] To solve the above technical problems, the technical solution adopted by the present application is: A hoisting equipment intelligent inspection method based on multiple failure modes, comprising the following steps: Step 1: Sort out the typical damage and failure modes of the structure, mechanism and electrical control system of the port crane throughout its life cycle, and build a typical failure / fault mode library; Step 2: Based on the typical failure / fault mode library, analyze the fault causal chain and build a multi-level failure / fault tree model covering the structure, transmission and electrical control system; Step 3: For the typical failure / fault mode, perform failure / fault mode impact analysis to identify the key damage position; Step 4, in combination with the key damage site identified and the crane working condition, the key point inspection index and period are determined, a multi-source damage point inspection index system is established, and a dynamic adjustment inspection standard is formed; Step 5, in combination with the crane layout and path planning algorithm, an intelligent inspection path is planned to fully cover the key parts of the crane; Step 6, the multi-sensor intelligent point inspection technology of metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging is integrated, and the intelligent inspection path is combined to cooperatively detect the multi-system damage mode; Step 7, based on the crane intelligent inspection result, the intelligent point inspection specification and dynamic adjustment strategy are formulated to realize precise control of the whole system health state.

[0006] The further improvement of the technical scheme of the application is that the step 1 specifically comprises: Damage and failure case data of the port crane in each stage of the whole life cycle are collected through a multi-source channel system, and preprocessing operations such as cleaning, classification and standardization are performed to eliminate redundancy and construct an initial structured database, wherein the multi-source channel system comprises literature research, industry report, simulation analysis report, theoretical analysis report, test analysis report, equipment maintenance record and expert interview; The damage and failure case data after preprocessing are classified according to the system and the damage type, typical failure modes in each stage are summarized, and the causes and development mechanism are analyzed in combination with a theoretical model to form a typical failure / fault mode library framework; The classification and mechanism analysis results are converted into standardized mode items to construct a typical failure / fault mode library, and the completeness and accuracy of the typical failure / fault mode library are verified through expert review, case comparison and simulation verification.

[0007] The further improvement of the technical scheme of the application is that the step 2 specifically comprises: Based on the typical failure / fault mode library, the system-level fault top event is determined, the deductive analysis method is used to trace the direct cause from the fault top event, the fault top event and the fault logic correlation of the three subsystems are established according to the system boundary first layer decomposition, the top layer framework of the fault tree is formed to cover the multi-system cooperative failure scene; Taking each subsystem fault as an intermediate event, the fault cause chain is expanded downward in combination with the mechanism knowledge and causal data of the typical failure / fault mode library, the direct sub-reason is identified, the logical gate is used to describe the mutual relationship, and the recursive decomposition is performed to the detectable basic event or bottom event, and then a multi-level and multi-system failure / fault tree model is constructed; The preliminary constructed failure / fault tree model is subjected to logical consistency and completeness checking, historical case data and expert knowledge of a typical failure / fault mode library are used to verify the rationality of the cause-effect relationship and the path probability, and redundant branches are removed, repeated nodes are merged, and a multi-field knowledge structured fault tree model conforming to engineering practice and logically simple is formed.

[0008] The further improvement of the technical scheme of the application is that the step 3 specifically comprises: Based on the typical failure / fault mode library and the failure / fault tree model, the typical failure / fault modes of various types are sorted into corresponding subsystems, and their functional logic is analyzed to determine the direct impact on the system performance, and the preliminary impact range in the physical space or functional flow is defined in combination with the system boundary; For the preliminarily positioned failure / fault mode, the functional decomposition method is used to analyze the progressive impact of the failure / fault mode on the subsystem and the overall function, the occurrence probability of the failure / fault mode is analyzed, the failure / fault mode propagation path is determined by constructing a fault propagation path diagram, the critical condition of functional failure is identified, and the impact degree of the failure / fault mode is determined. According to the impact degree, impact range and occurrence probability of each failure / fault mode, the severity score, occurrence score and impact score are calculated based on the pre-set scoring standard, the mode risk score is calculated, the criticality of the failure / fault mode is sorted, the failure / fault mode exceeding the pre-set risk threshold is identified as a high-risk mode, the critical damage site is determined, and a critical damage site list is formed, thereby providing a basis for maintenance and design.

[0009] The further improvement of the technical scheme of the application is that the calculation process of the mode risk score is: The data analysis team pre-sets the scoring standard of the three dimensions of impact degree, impact range and occurrence probability according to historical data, experimental tests and engineering experience, as the scoring standard of the failure / fault mode to be evaluated, and then obtains the severity score, occurrence score and impact score, and sets an adjustment constant according to industry experience to adjust the input range of the logarithmic function; The severity score, occurrence score and impact score are subjected to average value calculation to obtain a basic risk average value, the score values of the three dimensions are multiplied, divided by the adjustment constant, and then added 1 to calculate the logarithmic function to obtain a risk coupling enhancement value; The basic risk average value and the risk coupling enhancement value are added to obtain the final mode risk score, which is compared with the pre-set risk threshold, and the failure / fault mode exceeding the risk threshold is identified as a high-risk mode, and the greater the value of the mode risk score, the higher the risk of the failure / fault mode.

[0010] The further improvement of the technical scheme of the application is that the step 4 specifically comprises: Based on the identification result of the key damage site and the corresponding failure / fault mode, the monitoring physical parameters of the structure, transmission and electric control system are determined as point inspection indexes, and according to the crane design specification, historical failure data and industry standard, the initial alarm and dangerous threshold of each point inspection index is set to form a quantitative monitoring benchmark; The point inspection indexes are integrated according to the system, component and physical type, the definition, detection method, equipment, data unit and threshold standard of the point inspection index are determined to form a multi-source damage point inspection index system, which supports unified data management and analysis; Combined with the historical occurrence frequency, average degradation rate of each failure / fault mode and the accessibility of the key damage site, the initial inspection cycle of each key damage site and point inspection index group is set to form a point inspection schedule table covering all risk points, and the monitoring frequency and resource allocation are scientific and reasonable.

[0011] The further improvement of the technical scheme of the application is that the quantitative benchmark of the point inspection index monitoring is specifically: For the structure system, the point inspection index covers the stress amplitude, crack propagation length and modal frequency; Wherein, the initial alarm threshold of the stress amplitude is 70% of the design allowable stress, and the dangerous threshold is 90% of the design allowable stress; the initial alarm threshold of the crack propagation length is that the crack length is greater than or equal to 0.5mm, and the dangerous threshold is that the crack length is greater than or equal to the critical crack size (calculated according to fracture mechanics); the initial alarm threshold of the modal frequency is that the modal frequency decreases by more than 5%, and the dangerous threshold is that the modal frequency decreases by more than 10%; For the transmission system, the point inspection index covers the vibration acceleration, temperature threshold and lubricating oil quality; Wherein, the initial alarm threshold of the vibration acceleration is that the vibration acceleration effective value is greater than or equal to 2.8m / s² (ISO 10816 standard), and the dangerous threshold is that the vibration acceleration effective value is greater than or equal to 7.1m / s²; the initial alarm threshold of the temperature threshold is that the temperature is greater than or equal to 60℃, and the dangerous threshold is that the temperature is greater than or equal to 80℃; the initial alarm threshold of the lubricating oil quality is that the acid value is greater than or equal to 2mgKOH / g, and the water content is greater than or equal to 0.1%, and the dangerous threshold is that the acid value is greater than or equal to 4mgKOH / g, and the water content is greater than or equal to 0.5%; For the electric control system, the point inspection index covers the contact point temperature, insulation resistance value and current voltage fluctuation range; Wherein, the initial alarm threshold of the contact point temperature is that the temperature is greater than or equal to 70℃, and the dangerous threshold is that the temperature is greater than or equal to 90℃; the initial alarm threshold of the insulation resistance value is that the insulation resistance is less than or equal to 1MΩ, and the dangerous threshold is that the insulation resistance is less than or equal to 0.5MΩ; the initial alarm threshold of the current voltage fluctuation range is that the voltage fluctuation is greater than or equal to ±5%, and the current fluctuation is greater than or equal to ±10%, and the dangerous threshold is that the voltage fluctuation is greater than or equal to ±10%, and the current fluctuation is greater than or equal to ±20%.

[0012] The further improvement of the technical scheme of the present application is that the step 5 specifically comprises: By the three-dimensional laser scanning crane, a high-precision digital twin model is constructed, the spatial coordinates and geometric relationship of each component of the crane are determined, the key damage positions are converted into inspection nodes that must be passed through, and attribute information of the system, detection items, operation time and safety access conditions are given, and the feasible region of the path network and the connecting edge are defined in the digital twin model in combination with the motion ability of the inspection equipment and the site environment constraints; Based on the established digital twin model, intelligent algorithms are applied for path planning, and the constraints of full coverage, endurance capability, work time upper limit and operation sequence are comprehensively considered, so that the total path length is minimized and the work load of each inspection sub-path is balanced, a plurality of inspection sequence paths are solved, and the key positions are ensured to be accessed without omission under the condition of limited resources; The simulation verification of the inspection sequence path is carried out in the digital twin model, the collision risk, operation posture requirement and time efficiency are checked, and after verification, the optimal inspection sequence path is integrated into the intelligent inspection system, the automatic scheduling and dynamic optimization of the inspection task are realized, and the inspection reliability and resource utilization rate are improved.

[0013] The further improvement of the technical scheme of the present application is that the step 6 specifically comprises: Based on the intelligent inspection path planning, the sensors of metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging are integrated, the sensor combination is automatically called and configured according to the path node attribute, in the moving state, each sensor synchronously collects data in the continuous or fixed-point mode, and is bound with the high-precision spatial coordinates and time stamp, so that the space-time consistency is ensured; After the multi-source sensor data is transmitted to the data processing platform, the noise reduction, alignment and normalization preprocessing are carried out, the key characteristic parameters are extracted from the multi-source sensor data based on the pre-defined damage feature library, wherein the stress mutation gradient feature is extracted from the magnetic memory signal, the fault characteristic frequency amplitude is extracted from the vibration spectrum, and the abnormal temperature rise and distribution feature is extracted from the thermograph, the comprehensive identification and preliminary positioning of the coupled damage mode are carried out; The damage mode identification and diagnosis analysis are carried out by comprehensively considering the current characteristic parameters, historical data and equipment state, a structured diagnosis conclusion is generated, and detailed information is visually presented in the digital twin model.

[0014] The further improvement of the technical scheme of the present application is that the step 7 specifically comprises: A point inspection specification system covering the whole system is constructed, the inspection units are divided according to the equipment function modules, the detection items, detection methods and technical standards of each unit are determined, the high-frequency, medium-frequency or low-frequency inspection frequency is set in combination with the running time of the crane, the data coding rules are unified, and the traceability and cross-system compatibility are ensured; Based on real-time collected multi-source sensor data, the deviation of each key characteristic parameter between the allowable value and the actual value is analyzed, the inspection strategy and path are dynamically adjusted to preferentially cover high-risk points, the maintenance suggestions and early warning information are generated by combining the historical data, typical failure / fault mode library and expert knowledge, and are pushed to the operation and maintenance management platform. The effectiveness of the inspection strategy is verified reversely according to the maintenance execution result, the detection items and frequency rules are optimized, the dynamic matching of the inspection strategy and the equipment state evolution is carried out through continuous iteration, and the precision and resource allocation efficiency of the whole system health management and control are improved.

[0015] Due to the adoption of the above technical solutions, the technical progress achieved by the present application relative to the prior art is: The present application provides a kind of hoisting equipment intelligent inspection method based on multiple failure modes, by building multi-level failure / fault tree model covering structure, transmission and electric control system, the correlation of mechanical wear, hydraulic leakage and electrical fault is synchronously tracked by failure cause chain analysis, forming electrical-mechanical-hydraulic whole chain diagnosis, and the positioning precision of complex fault is significantly improved.

[0016] The present application provides a kind of hoisting equipment intelligent inspection method based on multiple failure modes, by building typical failure mode library and multi-level fault tree model, covering structure, transmission and electric control system, the comprehensive identification of multi-system coupling damage mode is realized, based on multi-sensor fusion technology and intelligent path planning, multi-source data can be synchronously collected in the inspection process, and accurate feature extraction and pattern recognition are carried out, reducing the missed detection and misjudgment in traditional single mode detection, improving the comprehensiveness and accuracy of fault identification, and providing reliable basis for health state evaluation of crane.

[0017] The present application provides a kind of hoisting equipment intelligent inspection method based on multiple failure modes, by three-dimensional laser scanning to build high-precision digital twin model, realizes the automatic generation and simulation verification of inspection path, converts key damage parts into necessary nodes, and gives detection items, safety access conditions and other attributes, combines intelligent algorithm to optimize path, minimizes total length while balancing the workload of each sub-path, can detect collision risk and operation posture compliance, and ensures the feasibility of path. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0019] Figure 1A work flow schematic diagram of a hoisting equipment intelligent inspection method based on multiple failure modes of the present application; Figure 2 A method flow schematic diagram of a hoisting equipment intelligent inspection method based on multiple failure modes of the present application. DETAILED DESCRIPTION

[0020] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] Embodiment 1, as shown in Figure 1 、 Figure 2 The present application provides a hoisting equipment intelligent inspection method based on multiple failure modes, including the following steps: Step 1, collate the typical damage and failure modes of the structure, mechanism and electric control system of the port crane throughout the whole life cycle, construct a typical failure / fault mode library, collect the damage and failure case data of the port crane throughout the whole life cycle through multiple source channel systems, and perform cleaning, classification and standardized preprocessing operations, eliminate redundancy, and construct an initial structured database, wherein the multiple source channel systems include literature research, industry reports, simulation analysis reports, theoretical analysis reports, test analysis reports, equipment maintenance records and expert interviews, classify the damage and failure case data after preprocessing according to the system and damage type, summarize the typical failure modes at each stage, and combine theoretical models to deeply analyze the causes and development mechanism, form a typical failure / fault mode library framework, convert the classification and mechanism analysis results into standardized mode items, construct a typical failure / fault mode library, and verify the integrity and accuracy of the typical failure / fault mode library through expert review, case comparison and simulation verification; The specific work content is: through literature research, industry reports, equipment maintenance records and expert interviews, systematically collect the damage and failure case data of the port crane throughout the whole life cycle of the structure (main beam, end beam, etc.), mechanism (reducer and brake, etc.) and electric control system, and perform cleaning, classification and standardization processing on the data, eliminate redundant information, and form a structured database, wherein the whole life cycle includes design, manufacturing, installation, operation and retirement; Based on the pre-processed damage and failure case data, classify by system (structure, transmission, electrical control) and damage type (stress concentration, crack, abnormal vibration, overheating, etc.), summarize typical failure modes at each stage, analyze the causes, development process and influencing factors of failure modes combined with material mechanics, electrical control theory and fault physical model, reveal the failure mechanism, and form a typical failure / fault mode library framework covering multiple systems; Convert the classification and mechanism analysis results into standardized mode items, including failure mode description, causes, influence range and related system information, build a typical failure / fault mode library, and ensure the integrity and accuracy of the mode library through expert review, case comparison and simulation verification; Step 2, based on the typical failure / fault mode library, analyze the fault causal chain, and build a multi-level failure / fault tree model covering structure, transmission and electrical control systems. Based on the typical failure / fault mode library, identify the fault top event at the system level, use deductive analysis to trace the direct causes from the fault top event, and first-level decomposition according to the system boundary. Establish the logical relationship between the fault top event and the fault of the three subsystems to form the top-level framework of the fault tree to cover the multi-system collaborative failure scenario. Take each subsystem fault as an intermediate event, combine the mechanism knowledge and causal data in the typical failure / fault mode library, and expand the deep-level fault causal chain downward. For each intermediate event, identify all its direct sub-reasons, including component failure, external disturbance or human operation error, and use logical gates to describe their relationships. Recursively decompose until the basic events or bottom events that can be detected are obtained, and then build a multi-level, multi-system failure / fault tree model. Perform logical consistency and completeness verification on the preliminary failure / fault tree model, verify the rationality of the causal relationship and the path probability using the historical case data and expert knowledge of the typical failure / fault mode library, eliminate redundant branches, merge repeated nodes, and integrate to form a multi-field knowledge structured fault tree model that conforms to engineering practice and has a simple and logical logic. Specific work content: Based on the typical failure / fault mode library already built, identify the fault top event at the system level, such as "sudden functional shutdown of the crane" or "structural failure of the key component". Use deductive analysis to trace the direct causes from the fault top event, and first-level decomposition according to the system boundary (structure, transmission, electrical control). Establish the logical relationship between the fault top event and the fault of the three subsystems to form the top-level framework of the fault tree, ensuring that the model covers multi-system collaborative failure scenarios. Take each subsystem fault as an intermediate event, combine the mechanism knowledge and causal data in the typical failure / fault mode library, and expand the deep-level fault causal chain downward. For each intermediate event, identify all its direct sub-reasons, including component failure, external disturbance or human operation error, and use logical gates to describe their relationships. Recursively decompose until the basic events or bottom events that can be detected are obtained, and then build a multi-level, multi-system failure / fault tree model. The logical consistency and completeness of the preliminary constructed failure / fault tree model are checked, historical case data and expert knowledge in the typical failure / fault mode library are used to verify the rationality and path probability of the causal relationship of each level, redundant branches are removed, repeated nodes are merged, and it is ensured that the typical failure / fault mode library is consistent with engineering practice and maintains logical simplicity, and is integrated to form a structured failure / fault tree model covering multi-field knowledge of structure, transmission and electrical control; Step 3, for the typical failure / fault mode, failure / fault mode impact analysis is performed, key damage sites are identified, based on the typical failure / fault mode library and the failure / fault tree model, each type of typical failure / fault mode is sorted into the corresponding subsystem, and its function logic is analyzed to determine the direct impact on the system performance, combined with the system boundary, the preliminary impact range in the physical space or function flow is defined, for the preliminary located failure / fault mode, the function decomposition method is used to analyze its progressive influence on the subsystem and the overall function layer by layer, the occurrence probability of the failure / fault mode is analyzed, the failure / fault mode propagation path is determined by constructing a fault propagation path diagram, the critical condition of functional failure is identified, the influence degree of the failure / fault mode is determined, according to the influence degree, influence range and occurrence probability of each failure / fault mode, based on the pre-set scoring standard, the severity score, occurrence score and influence score are calculated, and the mode risk score is calculated, the criticality of the failure / fault mode is sorted, and the failure / fault mode exceeding the pre-set risk threshold is identified as a high-risk mode, the key damage site is determined to form a key damage site list, which provides a basis for maintenance and design; In addition, the calculation process of the mode risk score is: The data analysis team pre-sets the scoring standard of the three dimensions of influence degree, influence range and occurrence probability according to historical data, experimental tests and engineering experience, as the scoring standard of the failure / fault mode to be evaluated, and then obtains the severity score, occurrence score and influence score, and sets an adjustment constant according to industry experience to adjust the input range of the logarithmic function, wherein the severity score is used to evaluate the severity of the consequences of the failure / fault mode, the scoring standard is 1 to 10 points, and 1 point represents no impact and 10 points represents catastrophic consequences. Occurrence score is used to assess the likelihood of a failure / failure mode occurring, with a scoring system of 1 to 10, where 1 indicates extremely low probability of occurrence and 10 indicates extremely high frequency of occurrence. Impact score is used to assess the extent of the impact of a failure / failure mode, with a scoring system of 1 to 10, where 1 indicates the impact is limited to the component itself and 10 indicates the impact affects the entire system or multiple subsystems. The average of the severity, occurrence, and impact scores is calculated to obtain the mean base risk. The scores of the three dimensions are then multiplied, divided by an adjustment constant, and then 1 is added to calculate the logarithmic function to obtain the risk coupling enhancement value. The mean base risk is added to the risk coupling enhancement value to obtain the final mode risk score. This score is compared with a preset risk threshold. Failure / failure modes that exceed the risk threshold are considered high-risk modes. The higher the mode risk score, the higher the risk of the failure / failure mode. The expression for the pattern risk score is as follows: ; In the formula: For the risk score of the i-th failure / failure mode, The severity score indicates the degree of severity of the ultimate impact of the failure / failure mode on equipment safety, functionality, environment, or production. The occurrence score indicates the likelihood of this failure / failure mode occurring; The impact score indicates the extent of the failure / failure mode's spread in the physical space and functional flow; C is an adjustment constant, a constant greater than 0, used to prevent the value in parentheses from being too large or too small, adjusting the input range of the logarithmic function to make it more sensitive to changes. Value and , , All are positively correlated; as any one value increases, All will increase; The specific work content is as follows: Based on the established typical failure / failure mode library and failure / failure tree model, sort out various typical failure / failure modes, clarify their respective subsystems (structure, transmission or electrical control), and determine their direct impact on the overall system performance by analyzing the functional logic of the failure / failure modes. At the same time, in conjunction with the system boundary definition, define the initial impact range of the failure / failure modes in the physical space or functional process, such as specific components, connection nodes or operation phases. For the failure / fault mode of the preliminary positioning, the functional decomposition method is adopted to analyze the progressive influence of the failure / fault mode on the subsystem and the whole function layer by layer, the occurrence probability of the failure / fault mode is determined, the failure / fault mode is propagated through the interaction or signal transmission between components or the propagation path of the failure / fault mode is determined through the construction of the fault propagation path diagram, so as to identify the critical condition of the functional failure and determine the influence degree of the failure / fault mode; based on the influence degree, influence range and occurrence probability of the failure / fault mode, the severity score, occurrence score and influence score are calculated respectively according to the preset scoring standard, and then the mode risk score is calculated comprehensively, the criticality of each failure / fault mode is sorted, and the failure / fault mode exceeding the preset risk threshold is identified as the high-risk mode, wherein the physical component, structure point or electric control unit corresponding to the high-risk mode is determined as the key damage part needing to be monitored, and then a key damage part list is formed, thereby providing a basis for formulating a targeted maintenance strategy and optimizing a design scheme; Step 4, in combination with the identified key damage parts and the crane working condition, the key point inspection index and cycle are determined, a multi-source damage point inspection index system is established, and a dynamic adjustment inspection standard is formed; Step 5, in combination with the crane layout and the path planning algorithm, an intelligent inspection path is planned, and the key parts of the crane are fully covered; Step 6, a multi-sensor intelligent point inspection technology of metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging is integrated, and a multi-system damage mode is cooperatively detected in combination with the intelligent inspection path; Step 7, based on the crane intelligent inspection result, an intelligent point inspection specification and a dynamic adjustment strategy are formulated, and precise health state control of the whole system is realized.

[0022] In the embodiment 2 as shown in the embodiment 1, the application provides a technical solution: Figure 1 , Figure 2 Preferably, step 4 specifically includes: Based on the identification result of the key damage part and the corresponding failure / fault mode, the monitoring physical parameters of the structure, transmission and electric control system are determined as the point inspection index, and the initial alarm and danger threshold of each point inspection index is set according to the crane design specification, historical failure data and industry standard, so as to form a quantitative monitoring benchmark, the point inspection index is integrated according to the system, component and physical type, the definition, detection method, equipment, data unit and threshold standard of the point inspection index are determined, a multi-source damage point inspection index system is formed, data unified management and analysis are supported, the initial inspection cycle is set for each key damage part and point inspection index group according to the historical occurrence frequency, average degradation rate of each failure / fault mode and the accessibility of the key damage part, a point inspection schedule table covering all risk points is formed, and the monitoring frequency and resource allocation are ensured to be scientific and reasonable; In addition, the quantitative benchmark of the point inspection index monitoring is specifically: For the structural system, the point inspection indicators include stress amplitude, crack propagation length and modal frequency; Among them, the stress amplitude is the stress range of the structural component under alternating load, which is monitored in real time by the fiber grating sensor, and the initial alarm threshold is 70% of the design allowable stress, and the dangerous threshold is 90% of the design allowable stress; the crack propagation length is the cumulative propagation distance of the surface or internal crack of the structural component, which is detected by non-destructive testing, and the initial alarm threshold is crack length ≥ 0.5mm, and the dangerous threshold is crack length ≥ critical crack size (calculated according to fracture mechanics); the modal frequency is the natural vibration frequency of the structural component, which reflects the stiffness change, which is collected by acceleration sensor, and the initial alarm threshold is that the modal frequency drops by ≥5%, and the dangerous threshold is that the modal frequency drops by ≥10%; For the transmission system, the point inspection indicators include vibration acceleration, temperature threshold and lubricating oil quality; Among them, the vibration acceleration is the vibration intensity of the transmission component (such as gear, bearing) when running, which is detected by installing the acceleration sensor on the bearing seat or gear box, and the initial alarm threshold is vibration acceleration RMS ≥ 2.8m / s² (ISO10816 standard), and the dangerous threshold is vibration acceleration RMS ≥ 7.1m / s²; the temperature threshold is the operating temperature of the transmission component (such as bearing, speed reducer), which is detected by infrared thermometer or embedded temperature sensor, and the initial alarm threshold is temperature ≥ 60℃, and the dangerous threshold is temperature ≥ 80℃; the lubricating oil quality is the index of viscosity, acid value and moisture content of lubricating oil, which is detected by periodic sampling and using oil analyzer, and the initial alarm threshold is acid value ≥ 2mgKOH / g, moisture content ≥ 0.1%, and the dangerous threshold is acid value ≥ 4mgKOH / g, moisture content ≥ 0.5%; For the electrical control system, the point inspection indicators include contact point temperature, insulation resistance value and current voltage fluctuation range; Among them, the contact point temperature is the operating temperature of the electrical connection point (such as circuit breaker, contactor), which is detected by temperature patch, and the initial alarm threshold is temperature ≥ 70℃, and the dangerous threshold is temperature ≥ 90℃; the insulation resistance value is the insulation performance of electrical components (such as motor, cable), which is measured by megohmmeter, and the initial alarm threshold is insulation resistance ≤ 1MΩ, and the dangerous threshold is insulation resistance ≤ 0.5MΩ; the current voltage fluctuation range is the instantaneous fluctuation of the power supply system or motor input current / voltage, which is detected by power analyzer, and the initial alarm threshold is voltage fluctuation ≥ ±5%, current fluctuation ≥ ±10%, and the dangerous threshold is voltage fluctuation ≥ ±10%, current fluctuation ≥ ±20%; Specific work content is: based on the identified key damage site and its corresponding failure / fault mode, the physical parameters to be monitored are determined as point inspection indicators, wherein, for structural systems, point inspection indicators include stress amplitude, crack propagation length and modal frequency; for transmission systems, point inspection indicators are vibration acceleration, temperature threshold and lubricating oil quality; for electric control systems, point inspection indicators include contact point temperature, insulation resistance value and current and voltage fluctuation range; At the same time, according to the crane design specification, historical failure data and industry standard, the initial alarm threshold and danger threshold are set for each point inspection indicator, forming the quantitative basis of point inspection indicator monitoring; all point inspection indicators are integrated according to the system, component and physical type, a structured multi-source damage point inspection indicator system is constructed, the definition, detection method and equipment, data unit and threshold standard of each point inspection indicator are determined, and the historical occurrence frequency, average degradation rate of each failure / fault mode and the accessibility of key damage site are comprehensively analyzed, an initial inspection cycle is set for each key damage site and point inspection indicator group, a point inspection schedule is formed, and full coverage monitoring of all high-risk points is ensured; Step 5 specifically includes: By three-dimensional laser scanning crane, a high-precision digital twin model is constructed, the spatial coordinates and geometric relationship of each component of the crane are determined, the key damage site is converted into an inspection node, and the attribute information of the system, detection project, operation time and safety access condition is given, and combined with the motion ability of the inspection equipment and the constraints of the site environment, the feasible region and connection edge of the path network are defined in the digital twin model, based on the established digital twin model, intelligent algorithm is applied for path planning, and the constraints of full coverage, endurance capability, upper limit of working hours and operation sequence are considered; With the goal of minimizing the total path length and balancing the workload of each inspection sub-path, a plurality of inspection sequence paths are solved to ensure that the key parts are accessed without omission under resource constraints, the simulation verification of the inspection sequence path is carried out in the digital twin model, the collision risk, operation posture requirement and time efficiency are checked, after verification, the optimal inspection sequence path is integrated into the intelligent inspection system, realizing the automatic scheduling and dynamic optimization of the inspection task, improving the inspection reliability and resource utilization rate; The specific work content is: the physical layout of the crane is spatially digitized modeling, a high-precision digital twin model is constructed by three-dimensional laser scanning, the spatial coordinates and geometric relationship of each component of the crane are determined, all key damage parts identified are converted into nodes that must be passed in the inspection path network, and attribute information is assigned to each node, including the system to which it belongs, the detection items to be performed, the expected operation time and safety access conditions, at the same time, according to the movement ability of the inspection equipment and the constraints of the site environment, the feasible region and connection edge of the path network are defined in the digital twin model; based on the established digital twin model, intelligent path planning algorithm is applied for global optimization, multiple constraints and optimization objectives are comprehensively considered; Among them, the constraint conditions include the full coverage requirement of the inspection nodes, the endurance ability of the inspection equipment or the upper limit of the artificial working hours and the specific operation sequence logic, the optimization objective is to minimize the total path length while balancing the workload of each inspection sub-path, and the most efficient inspection sequence path under the given constraints is solved, ensuring that all key parts are accessed without omission under limited resources; the simulation verification of the inspection sequence path is carried out in the digital twin model, whether there is a collision risk in the physical space, whether the operation posture requirements of all nodes are met, and the overall time efficiency is evaluated, after verification, the optimal inspection sequence path is integrated into the intelligent inspection system; Step 6 specifically includes: Based on intelligent inspection path planning, sensors of metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging are integrated, sensor combinations are automatically called and configured according to path node attributes, during movement, each sensor synchronously collects data in continuous or fixed-point mode, and is bound with high-precision spatial coordinates and time stamp to ensure spatio-temporal consistency, after multi-source sensor data is transmitted to the data processing platform, noise reduction, alignment and normalization preprocessing are carried out, based on the pre-defined damage feature library, key feature parameters are extracted from multi-source sensor data, among them, stress mutation gradient features are extracted from magnetic memory signals, fault feature frequency amplitude is extracted from vibration spectrum, abnormal temperature rise and distribution features are extracted from thermal images, comprehensive damage mode identification and preliminary positioning are carried out, current feature parameters, historical data and equipment state are comprehensively considered, damage mode identification and diagnosis analysis are carried out, structured diagnosis conclusions are generated, and detailed information is visualized in the digital twin model; Specific work content is: based on intelligent inspection path planning results, integrate metal magnetic memory, vibration analysis and infrared thermal imaging sensors, according to the preset optimal inspection sequence path automatic operation, according to the attribute information of each node in the optimal inspection sequence path, automatically call and configure the corresponding sensor combination, in the moving process, each sensor according to its sampling demand, continuous or fixed point type synchronous data acquisition, metal magnetic memory sensor scans the structure stress concentration area, collects the magnetic field signal of the structure stress concentration area, analyzes the stress distribution of the structure under the action of alternating load, vibration analysis sensor records the time domain and frequency domain signal of the transmission component, infrared thermal imaging sensor synchronously captures the temperature field distribution of the electric control system, all data are bound with high-precision spatial position coordinates and time stamp, to ensure the spatio-temporal consistency of data stream; The collected multi-source sensor data is transmitted to the data processing platform for fusion and feature extraction. The sensor data is preprocessed, including noise reduction, alignment and normalization. Based on the predefined damage feature library, key feature parameters are extracted from the multi-source sensor data: stress mutation gradient features are extracted from the magnetic memory signal; fault related characteristic frequency amplitude is extracted from the vibration spectrum; abnormal temperature rise and its distribution characteristics are extracted from the thermal image, realizing comprehensive identification and preliminary positioning of structure-mechanism-electric control system coupling damage mode. Based on the current extracted feature parameters, historical inspection data and equipment operating state, the damage mode is identified, and the diagnosis conclusion and detailed information are generated and visualized in real time; Step 7 specifically includes: A point inspection specification system covering the whole system is constructed, the inspection unit is divided according to the function module of the equipment, the detection items, detection methods and technical standards of each unit are clearly defined, and the high-frequency, medium-frequency or low-frequency inspection frequency is set according to the running time of the crane. Unified data coding rules ensure traceability and cross-system compatibility. Based on the real-time collected multi-source sensor data, the deviation between the allowable value and the actual value of each key feature parameter is analyzed, and the inspection strategy and path are dynamically adjusted to preferentially cover high-risk points. Combined with historical data, typical failure / fault mode library and expert knowledge, maintenance suggestions and warning information are generated and pushed to the operation and maintenance management platform. According to the maintenance execution result, the effectiveness of the inspection strategy is verified in reverse, the detection items and frequency rules are optimized, and the dynamic matching of inspection strategy and equipment state evolution is carried out through continuous iteration, which improves the precision of whole system health management and control and the resource allocation efficiency; Specific work content is: based on the structure characteristics of the crane, operation condition and historical failure data, build a point inspection specification system covering the whole system, according to the function module of equipment, clear detection items, detection methods and technical standards of each unit, formulate inspection cycle rules, combined with the running time of crane, dynamic set high frequency (daily), medium frequency (weekly) or low frequency (monthly) inspection frequency, and standardize data record format, unified sensor data, space coordinates, time stamp and diagnostic conclusion coding rules, ensure data traceability and cross system compatibility; Relying on real-time acquisition of multi-source sensor data, analyze the allowable value of each key characteristic parameter, compare the allowable value of each key characteristic parameter with its actual value, and then dynamically adjust the inspection strategy to update the inspection path planning, preferentially cover high-risk areas, optimize resource allocation efficiency, and then compare the inspection results with the crane historical data, the allowable value of the key characteristic parameters of the same type of equipment, analyze the typical failure / fault mode library and expert knowledge, generate maintenance suggestions and warning information, and push to the operation and maintenance management platform; According to the maintenance execution result, verify the effectiveness of the inspection strategy in reverse, optimize the inspection specification, update through continuous iteration, dynamically match the inspection strategy and equipment state evolution, and improve the accuracy of the whole system health management and control.

[0023] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent inspection of lifting equipment based on multiple failure modes, characterized in that, Includes the following steps: Step 1: Identify typical damage and failure modes throughout the entire lifecycle of the port crane's structure, mechanism, and electrical control system, and construct a library of typical failure / failure modes; Step 2: Based on the typical failure / failure mode library, analyze the failure causal chain and construct a multi-level failure / failure tree model covering the structure, transmission and electronic control system. Step 3: For typical failure / failure modes, conduct failure / failure mode impact analysis to identify key damaged areas; Step 4: Combine the identified key damage locations with the crane's operating conditions to determine the key point inspection indicators and cycles, and establish a multi-source damage point inspection indicator system. Step 5: Combine crane layout and path planning algorithms to plan intelligent inspection paths; Step 6: Integrate multi-sensor intelligent point inspection technology with metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging, and combine it with intelligent inspection path to perform collaborative detection of damage modes of multiple systems. Step 7: Based on the results of the intelligent inspection of the crane, formulate intelligent point inspection specifications and dynamic adjustment strategies.

2. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 1 specifically includes: Damage and failure case data of port cranes at all stages of their life cycle are collected through a multi-source channel system. The data is then cleaned, classified, and standardized to remove redundancy and build an initial structured database. The multi-source channel system includes literature review, industry reports, simulation analysis reports, theoretical analysis reports, test analysis reports, equipment maintenance records, and expert interviews. Based on the system and damage type, the pre-processed damage and failure case data are classified, typical failure modes at each stage are summarized, and their causes and development mechanisms are analyzed in depth in combination with theoretical models to form a typical failure / failure mode library framework. The classification and mechanism analysis results are transformed into standardized pattern entries to construct a typical failure / failure mode library. The completeness and accuracy of the typical failure / failure mode library are verified through expert review, case comparison, and simulation.

3. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 2 specifically includes: Based on the typical failure / failure mode library, the top failure event at the system level is identified. The deductive analysis method is used to trace the direct cause from the top failure event layer by layer. According to the first-level decomposition of the system boundary, the logical association between the top failure event and the failure of the three major subsystems is established, forming the top-level framework of the fault tree to cover multi-system collaborative failure scenarios. Taking the failure of each subsystem as an intermediate event, and combining the mechanism knowledge and causal data of the typical failure / failure mode library, the failure causal chain is expanded downward, the direct sub-cause is identified, and the relationship between them is described using logic gates. The model is recursively decomposed to detectable basic events or bottom events, and then a multi-level, multi-system failure / failure tree model is constructed. The initial failure / fault tree model is logically consistent and complete. Historical case data from a typical failure / fault mode library and expert knowledge are used to verify the rationality of causal relationships and path probabilities. Redundant branches are removed, duplicate nodes are merged, and a multi-domain knowledge-structured fault tree model that conforms to engineering practice is formed.

4. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 3 specifically includes: Based on the typical failure / failure mode library and failure / failure tree model, we sort out the typical failure / failure modes of each type, classify them into the corresponding subsystems, and analyze their functional logic to determine their direct impact on system performance. Combined with the system boundary, we define the initial scope of their impact in the physical space or functional process. For the initially identified failure / failure modes, the functional decomposition method is used to analyze their progressive impact on subsystems and overall functions layer by layer, analyze the probability of occurrence of failure / failure modes, and clarify the propagation path of failure / failure modes by constructing a failure propagation path diagram, identify the critical conditions for functional failure, and determine the degree of impact of failure / failure modes. Based on the degree of impact, scope of impact, and probability of occurrence of each failure / failure mode, and using pre-set scoring criteria, severity score, occurrence score, and impact score are calculated, and mode risk score is calculated. Failure / failure modes are ranked by criticality, and failure / failure modes that exceed the preset risk threshold are identified as high-risk modes. Critical damage sites are determined and a list of critical damage sites is formed.

5. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 4, characterized in that: The calculation process for the pattern risk score is as follows: The data analysis team pre-sets scoring criteria for three dimensions—impact degree, impact range, and probability of occurrence—based on historical data, experimental tests, and engineering experience. These criteria serve as the scoring standards for the failure / failure modes to be evaluated, thereby obtaining severity scores, occurrence scores, and impact scores. Adjustment constants are set based on industry experience to adjust the input range of the logarithmic function. The average of the severity score, occurrence score, and impact score is calculated to obtain the mean risk. The scores of the three dimensions are multiplied, divided by the adjustment constant, and then 1 is added to calculate the logarithmic function to obtain the risk coupling enhancement value. The mean risk value is added to the risk coupling enhancement value to obtain the final mode risk score. This score is then compared with a preset risk threshold. Failure / malfunction modes that exceed the risk threshold are identified as high-risk modes.

6. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 4 specifically includes: Based on the identification results of key damaged parts and the corresponding failure / failure modes, the monitoring physical parameters of the structure, transmission and electrical control system are identified as point inspection indicators. In accordance with crane design specifications, historical fault data and industry standards, initial alarm and danger thresholds for each point inspection indicator are set to form a quantitative monitoring benchmark. Integrate inspection indicators according to system, component and physical type, clarify the definition, detection method, equipment, data unit and threshold standard of inspection indicators, and form a multi-source damage point inspection indicator system; By combining the historical frequency of each failure / failure mode, the average degradation rate, and the accessibility of key damaged areas, an initial inspection cycle is set for each key damaged area and point inspection indicator group, forming a point inspection plan covering all risk points.

7. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 6, characterized in that: The quantitative benchmark for monitoring the point inspection indicators is as follows: For structural systems, the point inspection indicators cover stress amplitude, crack propagation length, and modal frequency; Specifically, the initial alarm threshold for stress amplitude is 70% of the allowable design stress, and the danger threshold is 90% of the allowable design stress; the initial alarm threshold for crack propagation length is a crack length ≥ 0.5 mm, and the danger threshold is a crack length ≥ the critical crack size; the initial alarm threshold for modal frequency is a modal frequency decrease ≥ 5%, and the danger threshold is a modal frequency decrease ≥ 10%. For the transmission system, the inspection indicators cover vibration acceleration, temperature threshold and lubricating oil quality; The initial alarm threshold for vibration acceleration is an effective value of vibration acceleration ≥ 2.8 m / s², and the danger threshold is an effective value of vibration acceleration ≥ 7.1 m / s²; the initial alarm threshold for temperature is a temperature ≥ 60℃, and the danger threshold is a temperature ≥ 80℃; the initial alarm threshold for lubricating oil quality is an acid value ≥ 2 mg KOH / g and a moisture content ≥ 0.1%, and the danger threshold is an acid value ≥ 4 mg KOH / g and a moisture content ≥ 0.5%. For electrical control systems, the point inspection indicators cover contact point temperature, insulation resistance value, and current and voltage fluctuation range. The initial alarm threshold for contact point temperature is ≥70℃, and the danger threshold is ≥90℃; the initial alarm threshold for insulation resistance is ≤1MΩ, and the danger threshold is ≤0.5MΩ; the initial alarm threshold for current and voltage fluctuation range is ≥±5% for voltage fluctuation and ≥±10% for current fluctuation, and the danger threshold is ≥±10% for voltage fluctuation and ≥±20% for current fluctuation.

8. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 5 specifically includes: By using 3D laser scanning of the crane, a digital twin model is constructed to clarify the spatial coordinates and geometric relationships of each component of the crane. Key damaged parts are transformed into necessary inspection nodes, and attribute information such as the system to which they belong, inspection items, operation time, and safety access conditions are assigned. In combination with the movement capabilities of the inspection equipment and the constraints of the on-site environment, the feasible domain and connecting edges of the path network are defined in the digital twin model. Based on the established digital twin model, intelligent algorithms are applied for path planning. Taking into account constraints such as full coverage, endurance, working time limit and work sequence, multiple inspection sequence paths are solved with the goal of minimizing the total path length and balancing the workload of each inspection sub-path. The inspection sequence path is simulated and verified in the digital twin model to check the collision risk, operation posture requirements and time efficiency. After verification, the optimal inspection sequence path is integrated into the intelligent inspection system.

9. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 8, characterized in that: Step 6 specifically includes: Based on intelligent inspection path planning, sensors integrating metal magnetic memory, stress analysis, vibration analysis and infrared thermal imaging are automatically called and configured according to the path node attributes. During movement, each sensor collects data synchronously in continuous or fixed-point mode and binds it with high-precision spatial coordinates and timestamps. After the multi-source sensor data is transmitted to the data processing platform, it undergoes noise reduction, alignment and normalization preprocessing. Based on the predefined damage feature library, key feature parameters are extracted from the multi-source sensor data. Among them, stress mutation gradient features are extracted from magnetic memory signals, fault feature frequency amplitudes are extracted from vibration spectrum, and abnormal temperature rise and distribution features are extracted from thermal images. The coupled damage modes are then comprehensively identified and preliminarily located. By combining current feature parameters, historical data, and equipment status, damage pattern recognition and diagnostic analysis are performed to generate structured diagnostic conclusions.

10. The intelligent inspection method for lifting equipment based on multiple failure modes according to claim 1, characterized in that: Step 7 specifically includes: Construct a point inspection standard system covering the entire system, divide the inspection units according to the equipment functional modules, clarify the inspection items, inspection methods and technical standards of each unit, and set high-frequency, medium-frequency or low-frequency inspection frequencies according to the crane's running time, and unify the data coding rules; Based on real-time collected multi-source sensor data, the deviation between the allowable and actual values ​​of each key characteristic parameter is analyzed, and the inspection strategy and path are dynamically adjusted to prioritize the coverage of high-risk points. Combined with historical data, typical failure / fault mode library and expert knowledge, maintenance suggestions and early warning information are generated and pushed to the operation and maintenance management platform. The effectiveness of the inspection strategy is verified by reverse verification of the maintenance execution results, the inspection items and frequency rules are optimized, and the inspection strategy and equipment status evolution are dynamically matched through continuous iteration.

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