Crane health assessment method and system

Abstract

The invention provides a crane health assessment method and system. The method comprises the following steps: acquiring data of a crane in a working state; performing health assessment based on the collected data and a health assessment model; and obtaining quantized data based on an evaluation result, and performing maintenance guidance based on the quantized data. According to the system, the use condition of the crane can be monitored in real time, the states of main structural parts and key parts of the crane can be analyzed and evaluated, the health condition of the crane can be evaluated, quantitative data guidance is provided for equipment maintenance, and production and maintenance departments can predict the equipment condition in advance, so that maintenance or part plans can be arranged in a targeted manner.

Description

Technical Field

[0001] The present invention relates to the field of special equipment operation and maintenance, and in particular to a crane health assessment method and system. Background Art

[0002] Cranes are widely used in the shipbuilding industry and are one of the essential key equipment in the industry. Due to the increase in ship types and the extension of ship construction cycles, cranes are generally running at full load or even with "diseases", which makes it very easy for failures to cause production safety accidents, seriously threatening the production safety situation and the safety of people's lives and property.

[0003] In the prior art, the health status assessment of cranes is still mainly based on static experience judgment, which makes it difficult to take into account in-depth maintenance at the same time, resulting in various potential safety hazards.

[0004] Therefore, a solution is needed that can evaluate the health of the crane in real time. Summary of the invention

[0005] One purpose of the present application is to provide a crane health assessment method and system to solve the problem that it is difficult to perform real-time health assessment of cranes under the existing technology.

[0006] To achieve the above objectives, some embodiments of the present application provide a crane health assessment method, which includes the following steps:

[0007] Step 1. Collect data when the crane is in working state;

[0008] Step 2. Performing a health assessment based on the collected data and the health assessment model;

[0009] Step 3. Based on the results of the evaluation, obtaining quantitative data;

[0010] Step 4: Provide maintenance guidance based on the quantitative data.

[0011] Furthermore, the data collected in step 1 include: working condition data, which refers to information data related to the working condition of the crane; vibration data, which refers to data for real-time monitoring of dynamic changes in the crane's mechanism; strain data, which refers to data for real-time collection of strain changes in the crane's structural parts under different working conditions, which is used to analyze the actual stress and deformation state of the structural parts.

[0012] Furthermore, the health assessment model in step 2 includes the following two relationships:

[0013] First, the relationship between crane structural parts and the health assessment of the whole machine:

[0014] Remaining life of the whole machine: The life of the whole machine refers to the service life of the entire crane, including all structural parts, mechanisms and components; the remaining life of the whole machine depends on the comprehensive status of each key component and structural part, and the life of the whole machine will not exceed the minimum remaining life of each key component and structural part;

[0015] Remaining life of important structural parts: important structural parts bear the main loads, and the life of the structural parts is one of the key factors for the overall life of the crane; the remaining life of the important structural parts is the lower limit of the life of the whole machine;

[0016] The second is the relationship between crane parts and mechanism health assessment:

[0017] Remaining life of key components: Key components are the core components of the mechanism. The health status of the key components directly determines the operating status of the mechanism. The remaining life of the mechanism is limited by the component with the shortest life among the key components.

[0018] Remaining life of the mechanism: The remaining life of the mechanism refers to the remaining usable time of each mechanism under normal working conditions; the life of the mechanism affects the functionality and efficiency of the whole machine.

[0019] Further, the health assessment model in step 2 includes a parts health assessment module and a mechanism health assessment module;

[0020] Wherein, the parts health assessment module includes:

[0021] ① Data input: the input data includes working condition data, including lifting load, lifting speed and lifting height;

[0022] ②Calculation layer operation

[0023] Calculating stress spectrum coefficients: performing force analysis according to the working condition data of the parts to obtain stress spectrum coefficients of the parts, wherein the stress spectrum coefficients reflect the actual stress conditions of the parts during operation;

[0024] Verify the stress spectrum coefficient: compare the calculated stress spectrum coefficient with the design standard or the safety range provided by the manufacturer to verify whether the stress spectrum coefficient of the part is within a reasonable range;

[0025] Calculate and verify the working level of parts: Calculate the working level of the parts based on the stress spectrum coefficients, and compare it with the design standards to determine whether preventive maintenance or replacement is required;

[0026] ③Verification layer operation

[0027] Health assessment model based on part stress change trend: Combine the stress change trend and historical working condition data of the part to evaluate the health of the part; the model analyzes the stress spectrum coefficient of the part and its change trend to predict possible failure risks;

[0028] The Institutional Health Assessment Module includes:

[0029] ①Data input

[0030] The input data includes vibration data and working condition data;

[0031] ②Calculation layer operation

[0032] Calculating load spectrum coefficients: calculating load spectrum coefficients of the mechanism according to the working condition data;

[0033] Verify the load spectrum coefficient: compare the calculated load spectrum coefficient with the design or standard requirements to verify whether the actual working load of the mechanism meets the health requirements;

[0034] Calculate and verify the working level: Based on the load spectrum coefficient, further calculate the working level of the mechanism, and compare the calculated working level with the design standard to determine whether the mechanism is within a reasonable working range;

[0035] ③Verification layer operation

[0036] Health assessment model based on changes in operating conditions and maintenance parameters: The health status of the mechanism is analyzed by combining load spectrum coefficients, working levels, historical operating conditions and maintenance records; the model determines whether the mechanism has abnormal conditions based on the vibration data and load fluctuations.

[0037] Furthermore, the health assessment model in step 2 further includes a structural component health assessment module and a whole machine health assessment module;

[0038] Wherein, the structural component health assessment module includes:

[0039] ①Data input

[0040] The input data includes strain data and working condition data;

[0041] ②Calculation layer operation

[0042] Calculating the load spectrum coefficient: calculating the load spectrum coefficient of the structural component according to the lifting load, speed and height in the working condition data, wherein the load spectrum coefficient reflects the average stress level and stress fluctuation of the structural component under different working conditions;

[0043] Verify the load spectrum coefficient: compare the load spectrum coefficient with the design standard to verify whether the actual load condition of the structural member is within a reasonable range;

[0044] Calculate and verify the working level: Calculate the working level of the structural parts based on the load spectrum coefficient; compare the working level with the design standard to ensure that the structural parts are within the safe working range;

[0045] ③Verification layer operation

[0046] Structural component stress change trend health assessment model: Based on the strain data and load spectrum coefficient, the stress change trend of the structural component is analyzed to assess its health status; if the stress changes significantly or the load is high for a long time, it indicates that the health status needs attention;

[0047] The whole machine health assessment module includes:

[0048] ①Data input

[0049] The input data includes whole machine working condition data, which includes lifting load, lifting speed and displacement; the whole machine working condition data reflects the overall operating status of the crane;

[0050] ②Calculation layer operation

[0051] Calculating stress spectrum coefficients: Calculating stress spectrum coefficients of the whole machine according to the whole machine working condition data and the results of the structural component health assessment, wherein the stress spectrum coefficients can quantify the average stress level of the whole machine under different working conditions;

[0052] Verify stress spectrum coefficients: Compare stress spectrum coefficients with the whole machine design standards to ensure that the overall operation meets the equipment health requirements;

[0053] Calculate and verify the whole machine working level: Calculate the whole machine working level based on the stress spectrum coefficient, and verify whether it is consistent with the equipment design standard to ensure that the whole machine operates within the specified safe working level;

[0054] ③Verification layer operation

[0055] Comprehensive factor health assessment model: The health status, operating condition changes, and maintenance history of structural parts and other key components are comprehensively considered to evaluate the health status of the entire machine through the model.

[0056] Furthermore, the inference layer operation includes working cycle determination, wherein the working cycle determination process of the part includes: collecting working condition data, the working condition data including the lifting load, lifting height and lifting speed of the lifting mechanism; removing redundant information in the collected data, and processing missing values ​​and abnormal values; data integration: integrating the crane working condition data, design data and specification data on a unified database platform to form a comprehensive data view; judging the lifting action based on the lifting speed; performing data splitting on the integrated data to split out the working condition data; finding the working cycle based on the lifting load and lifting height; and statistically analyzing the load based on the working cycle, and the determination process ends.

[0057] Furthermore, the inference layer operation includes the crane load spectrum and remaining life estimation:

[0058] ① Load spectrum coefficient of the whole machine:

[0059] The load spectrum coefficient and the number of used working cycles of the crane are estimated based on the actual working condition data, so as to further estimate the service life and unserviceable service life of the crane. The load spectrum coefficient K of the crane is p The calculation formula is as follows:

[0060]

[0061] Where: C i ——Work cycle data corresponding to each representative lifting load of the crane; the crane continuously collects data to obtain the real-time working condition data of the crane, and calculates the data of each work cycle, that is, C i =1; C T ——Total number of working cycles of the crane; P Qi ——The maximum lifting load in each working cycle; P Qmax ——rated load of the crane; m——power index, take m=3;

[0062] ② Remaining service life of the whole machine

[0063] Taking the design life of the crane as a reference value, considering the actual number of working cycles and the actual load spectrum coefficient, the actual remaining life of the crane is estimated. Qy , the estimation formula is as follows:

[0064]

[0065] Where: K P0 ——Crane design load spectrum factor; K P1 ——actual load spectrum coefficient of the crane; N Q0 ——Design life of crane; N Qz ——The number of working cycles the crane has been used, that is, its service life.

[0066] Furthermore, the inference layer operation includes crane parts stress spectrum and life estimation:

[0067] ① Part stress spectrum coefficient

[0068] According to the actual working conditions, the stress spectrum coefficient and stress cycle number of the parts are estimated, so as to further estimate the service life and unserviceable service life of the parts. The stress spectrum coefficient K of the parts is S The calculation formula is as follows:

[0069]

[0070] Where: n i ——Number of stress cycles corresponding to different stresses occurring in mechanical parts; n T ——Total stress cycle number of mechanical parts; σ i ——Different stresses occurring in mechanical parts during working hours; σ max ——σ i The maximum stress in; C - power index, determined by the tensile strength of the material and the fatigue limit of the part;

[0071] ② Remaining service life of parts

[0072] Taking the design life of the part as a reference value, considering the actual number of stress cycles and the actual stress spectrum coefficient, the actual remaining life N of the part is estimated. Gy , the estimation formula is as follows:

[0073]

[0074] Where: K S0 ——Part design stress spectrum coefficient; K S1 ——actual stress spectrum coefficient of the part; N G0 ——Design life of parts; N Gz ——The number of stress cycles the part has been used, that is, its service life.

[0075] Furthermore, the quantitative data in step 3 includes:

[0076] ① Stress spectrum coefficient: reflects the stress conditions of the equipment under different frequency bands and is a direct quantitative indicator of the health of the equipment structure. The higher the stress spectrum coefficient, the more complex the stress conditions of the equipment and the worse the health condition.

[0077] ② Fatigue damage accumulation value D: indicates the cumulative fatigue damage degree of the equipment under the current working conditions. D=1 is set as the damage limit. When the D value is close to or exceeds 1, it means that the fatigue damage of the equipment is close to the failure threshold;

[0078] ③ Health Index: It is usually a quantitative index of 0-1, where 0 indicates good health and 1 indicates extremely poor health.

[0079] approaching failure;

[0080] ④ Remaining useful life RUL: predict the remaining useful life RUL of the equipment based on the fatigue damage accumulation value and stress spectrum coefficient.

[0081] On the other hand, the present application also provides a crane health assessment system, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the system is triggered to execute the above-mentioned crane health assessment method.

[0082] Compared with the prior art, the crane health assessment provided by this application mainly predicts and monitors the fatigue life of the main structural parts and the stress-bearing parts of the key components of the crane, and estimates the remaining service life of the crane by establishing a life prediction model based on the stress spectrum coefficient. The remaining life prediction method combines the physical drive method with the data drive method. First, a life prediction physical model based on fatigue damage based on the linear cumulative damage theory is adopted. The model is used to estimate the cumulative fatigue damage of the structure or mechanical parts in multiple loading cycles and predict its failure life. Then a data-driven method is used to train a prediction model (machine learning model, support vector machine) based on historical health data, and the two methods are mutually verified to improve the accuracy of the health assessment conclusion. In addition, this evaluation process is based on the real-time use of the crane, and the status of the main structural parts and key components of the crane is analyzed and evaluated, and its health status is evaluated, providing quantitative data guidance for equipment maintenance and maintenance, so as to realize the advance prediction of the equipment status by the production and maintenance departments, so as to arrange maintenance or parts plans in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is the architecture diagram of the crane health assessment system of the present invention;

[0084] Figure 2 A flow chart for determining the working cycle of the drum shaft of the crane of the present invention;

[0085] Figure 3 This is a flow chart of the crane remaining life assessment method of the present invention. DETAILED DESCRIPTION

[0086] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0087] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0088] Here, the crane health assessment method and system of the embodiments of the present application are suitable for scenarios where real-time health assessment of the crane's working process is performed.

[0089] In this scenario, the crane is continuously lifting loads during operation. The loads are usually heavy industrial components. The continuous load lifting of the crane has a great impact on the health status of the entire crane, major structural parts and key components, and will continuously reduce the life of the entire crane, the life of major structural parts and key components.

[0090] However, it is difficult for on-site safety managers to accurately assess the overall health status of the crane, the health status of its main structural parts and key components. Health status assessments are often based on static experience-based judgments, and are unable to grasp the relevant health status of the entire crane, its main structural parts and key components in real time. It is difficult to control potential safety hazards, and remedial measures can often only be taken after an accident occurs, resulting in major problems in real-time safety.

[0091] In response to the above-mentioned problem of being unable to grasp the relevant health conditions of the crane as a whole, its main structural parts and key components in real time, some embodiments of the present application provide a crane health assessment that mainly predicts and monitors the fatigue life of the load-bearing parts of the crane's main structural parts and key components. This process collects relevant data of the crane in real time, analyzes and evaluates the status of the crane's main structural parts and key components, evaluates their health conditions, provides quantitative data guidance for equipment maintenance, and enables production and maintenance departments to predict the equipment status in advance, thereby arranging maintenance or parts plans in a targeted manner.

[0092] In some embodiments, Figure 1 As shown in the figure, the following relationships exist within the crane health assessment system architecture:

[0093] ① The relationship between crane structural parts and the whole machine health assessment:

[0094] Remaining life of the whole machine: The life of the whole machine usually refers to the service life of the entire crane, including all structural parts, mechanisms and components. The remaining life of the whole machine depends on the comprehensive status of each key component and structural part. The life of the whole machine will not exceed the minimum remaining life of each key component and structural part.

[0095] Remaining life of important structural parts: Important structural parts (such as main beams, outriggers, etc.) bear the main loads, and the structural life is one of the key factors in the overall life of the crane. The remaining life of important structural parts is often the lower limit of the life of the whole machine, because the failure of structural parts usually directly affects the safety and operability of the crane.

[0096] ②Relationship between crane parts and mechanism health assessment:

[0097] Remaining life of key components: Key components (such as motors, wire ropes, bearings, gears, etc.) are the core components of mechanisms (such as lifting mechanisms, slewing mechanisms, walking mechanisms, etc.). The health status of these components directly determines the operating status of the mechanism. The remaining life of the mechanism is usually limited by the shortest-lived part among the key components. Key components are the functional components of the mechanism. Once a component approaches its life limit, the life of the entire mechanism will be restricted and cannot continue to operate safely. For example, severe wear of the wire rope or reducer gear in the lifting mechanism will shorten the remaining life of the lifting mechanism.

[0098] Mechanism Remaining Life: The remaining life of a mechanism refers to the remaining usable time of each mechanism under normal working conditions. The life of a mechanism is affected by factors such as frequency of use and load, which affects the functionality of the entire machine. Although the life of each mechanism will not directly lead to the failure of the entire machine structure, it will affect its functionality and efficiency, and ultimately have an indirect impact on the life of the entire machine. By replacing key components, the service life of the mechanism can be extended.

[0099] Some embodiments of the present application provide a crane health assessment method, comprising the following steps:

[0100] Step 1. Collect data when the crane is in working state.

[0101] The data include: working condition data, including lifting load, lifting speed, lifting height, etc.; vibration data, for example, used for real-time monitoring of dynamic changes in the lifting mechanism; strain data, such as real-time collection of strain changes of the main beam under different working conditions, used to analyze its actual stress and deformation state.

[0102] Step 2: Perform health assessment based on the collected data and the health assessment model.

[0103] When performing health assessment on the whole machine, structural parts, mechanisms and parts of a crane, there are mainly two operation steps: the estimation layer and the verification layer. The estimation layer operation includes:

[0104] 1) Working cycle determination

[0105] It is understandable that according to the definition of working cycle in the "Crane Design Specifications": a working cycle of a crane refers to a complete process from lifting one object to starting to lift the next object, including crane operation and normal rest.

[0106] Exemplary definition of the whole machine working cycle: The main work of the crane is completed by the lifting mechanism, so the working cycle is determined by analyzing the working parameters of the lifting mechanism. Considering the changing characteristics of the various working condition data of the lifting mechanism, the lifting height value without random drift error is selected to determine the action of the crane, and then combined with the lifting load data to determine the working cycle of the lifting mechanism, and then combined with the working cycle of the main lifting mechanism to define the whole crane working cycle.

[0107] For example, the working cycle of the drum shaft is defined as follows: the stress cycle of the drum shaft is directly related to the lifting height, so the working cycle calculation of the drum shaft is defined according to the action change of the lifting mechanism. Figure 2 As shown in the figure, the process of determining the working cycle of the drum shaft includes: collecting working condition data, which includes the lifting load, lifting height, lifting speed, etc. of the lifting mechanism. Redundant information in the collected data is removed, and missing values ​​and abnormal values ​​are processed to ensure data quality. Data integration: Integrate data such as crane working condition data, design data, and specification data on a unified database platform to form a comprehensive data view. Determine the lifting action based on the lifting speed; split the integrated data to separate the working condition data; find the working cycle based on the lifting load and lifting height; and statistically analyze the load based on the working cycle, and the determination process ends.

[0108] 2) Crane load spectrum and life estimation

[0109] ① Load spectrum coefficient of the whole machine:

[0110] When designing a crane, the crane's use level and load state level are selected, that is, the total number of crane working cycles and the crane's load spectrum coefficient are specified. However, the actual use situation is different from the design, and the crane's load spectrum coefficient and the number of used working cycles need to be estimated based on the actual working conditions, so as to further estimate the crane's service life and unused service life.

[0111] In some embodiments, the crane load spectrum factor is calculated as follows:

[0112]

[0113] Where: K p ——Crane load spectrum coefficient; C i ——The number of working cycles corresponding to each representative lifting load of the crane; C T ——Total number of working cycles of the crane; P Qi ——representative lifting loads that can characterize the working tasks of the crane during its expected life; P Qmax ——rated load of the crane; m——power exponent, take m=3.

[0114] The data collected by the crane is continuous data, and the real-time working condition data of the crane can be obtained. Therefore, it is not necessary to select a representative working cycle for calculation, but to calculate the data of each working cycle, that is, C i =1, P Qi It is the maximum lifting load in each working cycle.

[0115] ② Remaining service life of the whole machine

[0116] According to the cumulative damage principle, each operation of the crane will cause varying degrees of damage to the entire machine. Therefore, the life of the crane cannot be directly calculated according to the number of working cycles specified during the design. Instead, the design life of the crane is used as a reference value, and the actual number of working cycles and actual load spectrum coefficient are considered to estimate the actual service life of the crane. The estimation method is as follows:

[0117]

[0118] Where: N Qy ——Number of remaining working cycles of the crane (remaining life); K P0 ——Crane design load spectrum factor; K P1 ——actual load spectrum coefficient of the crane; N Q0 ——Design life of crane; N Qz ——Number of working cycles the crane has been used (service life).

[0119] The life of a crane refers to the total number of working cycles from the beginning of its use to its final scrapping. For cranes that are still in service and whose working cycle information has not been counted since the beginning of use, it is impossible to obtain the accurate working history of the crane throughout its life cycle. The only way to estimate the used life is through the collected data. The calculation of a small data sample cannot accurately describe the life of the crane, but by increasing the amount of sample data, the estimated result will be closer to the real result, the accuracy will continue to improve, and finally the prediction of the remaining service life of the crane will be realized. The specific technical route is as follows: Figure 3 shown.

[0120] 3) Stress spectrum and life estimation of drum shaft

[0121] ① Dangerous section stress of drum shaft

[0122] The drum shaft is a component used for winding wire ropes in the hoisting mechanism and traction mechanism of a crane. The drum shaft in the hoisting mechanism is selected as the analysis object. The analysis of the forces on the drum shaft shows that the drum shaft is mainly subject to gravity, rope tension, and normal force of the pinion gear. Among them, gravity includes the deadweight of the drum shaft and the weight of the wire rope wound on the drum shaft. The weight of the wire rope is related to the lifting height; the rope tension is related to the lifting load, and the position of action is related to the lifting height; the pinion force is synchronized with the rope tension, that is, it is also related to the lifting load and the lifting height. The stress at the dangerous section of the drum shaft is formed by the superposition of three groups of forces.

[0123] ② Drum shaft stress spectrum coefficient

[0124] The crane also specifies the use level and stress state level of key parts during design, that is, the total number of stress cycles and stress spectrum coefficient of the parts. However, the actual use situation is definitely different from the design, so it is necessary to estimate the stress spectrum coefficient and stress cycle number of the parts according to the actual working conditions, so as to further estimate the service life and unserviceable life of the parts.

[0125] The calculation method of the stress spectrum coefficient of the part is as follows:

[0126] Where: K S ——Stress spectrum coefficient of mechanical parts; n i ——Number of stress cycles corresponding to different stresses occurring in mechanical parts; n T ——Total stress cycle number of mechanical parts; σ i ——Different stresses occurring in mechanical parts during working hours; σ max ——σ i The maximum stress in the part; C – power exponent, determined by the tensile strength of the material and the fatigue limit of the part.

[0127] Taking the drum shaft of the lifting mechanism as an example, according to the real-time working condition data of the crane lifting mechanism, the stress data of the drum shaft under each working cycle of the lifting mechanism is statistically analyzed, and the calculation is also performed according to the actual situation, that is, n i is 1, σ i Calculate the total stress on the cross section of the drum shaft during a single rising or lowering action of the lifting mechanism.

[0128] ③Remaining service life of the reel shaft

[0129] In one working cycle of the hoisting mechanism, the drum shaft may be subjected to multiple stress cycles. Similar to the remaining life estimation of the crane, the drum shaft stress caused by the load will cause different degrees of damage to the drum shaft. Similarly, the life of the drum shaft cannot be directly calculated according to the total number of stress cycles specified during the design. Instead, the actual service life of the drum shaft is estimated by taking the design life of the drum shaft as a reference value, considering the actual number of stress cycles and the actual stress spectrum coefficient. The estimation method is as follows:

[0130]

[0131] Where: N Gy ——Number of residual stress cycles of the drum shaft (remaining life); K S0 ——Design stress spectrum coefficient of drum shaft; K S1 ——actual stress spectrum coefficient of drum shaft; N G0 ——Design life of reel shaft; N Gz ——Number of stress cycles (service life) of the reel shaft.

[0132] The life of a reel shaft refers to the total number of stress cycles from the beginning of use to the final scrapping. For reels that are still in service and have not been statistically analyzed for stress cycles since the beginning of use, it is impossible to obtain the accurate working history of the reel shaft throughout its life cycle. The only way to estimate the used life is through the collected data. The estimation of a small data sample cannot accurately describe the life of the reel shaft, but by increasing the amount of sample data, the estimated result will approach the real result, the accuracy will continue to improve, and ultimately the prediction of the remaining service life of the reel shaft will be achieved.

[0133] Further Figure 1 As shown, the four health assessment modules are described in detail below.

[0134] (1) Parts health assessment - taking the reel shaft as an example

[0135] 1) Data input

[0136] Working condition data: also includes lifting load, lifting speed, and lifting height, providing necessary data support for the force analysis of the drum shaft.

[0137] 2) Inference layer operation

[0138] Calculate stress spectrum coefficient: According to the working condition data of the drum shaft, perform force analysis to obtain the stress spectrum coefficient of the drum shaft. The stress spectrum coefficient reflects the actual stress condition of the drum shaft during operation.

[0139] Verify stress spectrum coefficient: Compare the calculated stress spectrum coefficient with the design standard or the safety range provided by the manufacturer to verify whether the stress spectrum coefficient of the drum shaft is within a reasonable range.

[0140] Calculate and verify the working level of parts: Calculate the working level of the drum shaft based on the stress spectrum coefficient and compare it with the design standard to determine whether preventive maintenance or replacement is needed.

[0141] 3) Verification layer operation

[0142] Health assessment model based on part stress change trend: Combine the stress change trend of the drum shaft and historical working condition data to assess the health of the part. The model analyzes the stress spectrum coefficient of the drum shaft and its change trend to predict possible failure risks.

[0143] Health assessment conclusion: Through the trend analysis of the stress change of the drum shaft, the health status and remaining life prediction of the drum shaft are obtained. When the stress change is obvious or the health index is lower than expected, it is recommended to replace or repair it in time.

[0144] (2) Mechanism health assessment - taking the lifting mechanism as an example

[0145] 1) Data input

[0146] Vibration data: used to monitor the dynamic changes of the lifting mechanism in real time.

[0147] Working condition data: including lifting load, lifting speed, lifting height, providing detailed information on equipment load and operating conditions.

[0148] 2) Inference layer operation

[0149] Calculate the load spectrum coefficient: Calculate the load spectrum coefficient of the lifting mechanism based on the lifting load, speed and height in the working condition data. The load spectrum coefficient is an indicator for evaluating the average load and load fluctuation of the equipment under different working conditions.

[0150] Verify the load spectrum coefficient: Compare the calculated load spectrum coefficient with the design or standard requirements to verify whether the actual working load of the lifting mechanism meets the health requirements.

[0151] Calculate and verify the working level: Based on the load spectrum coefficient, further calculate the working level of the lifting mechanism, and compare the calculated working level with the design standard to determine whether the mechanism is within a reasonable working range.

[0152] 3) Verification layer operation

[0153] Health assessment model based on changes in operating conditions and maintenance parameters: Combines load spectrum coefficients, working levels, historical operating conditions and maintenance records to analyze the health status of the lifting mechanism. The model determines whether the mechanism has abnormal conditions based on vibration signals, load fluctuations, etc.

[0154] Health assessment conclusion: The load spectrum coefficient, working level and maintenance data of the mechanism are combined to draw a conclusion on the health status of the mechanism. If the health index decreases or the working level exceeds the design range, it is recommended to maintain the lifting mechanism.

[0155] (3) Structural health assessment - taking the main beam as an example

[0156] 1) Data input

[0157] Strain data: Real-time collection of the strain changes of the main beam under different working conditions to analyze its actual stress and deformation state.

[0158] Working condition data: including lifting load, lifting speed, lifting height, etc., reflecting the load and operating conditions of the main beam during operation.

[0159] 2) Inference layer operation

[0160] Calculate the load spectrum coefficient: Calculate the load spectrum coefficient of the main beam according to the load, speed and height in the working condition data. The load spectrum coefficient reflects the average stress level and stress fluctuation of the main beam under different working conditions.

[0161] Verify the load spectrum coefficient: Compare the load spectrum coefficient with the design standard to verify whether the actual load condition of the main beam is within a reasonable range.

[0162] Calculate and verify the working level: Calculate the working level of the main beam based on the load spectrum coefficient. Compare the working level with the design standard to ensure that the main beam is within the safe working range.

[0163] 3) Verification layer operation

[0164] Structural component stress change trend health assessment model: Based on stress data and load spectrum coefficients, the stress change trend of the main beam is analyzed to assess its health status. If the stress changes significantly or the load is high for a long time, it indicates that the health status needs attention.

[0165] Health assessment conclusion: Comprehensively analyze the stress change trend and working level to obtain the health status or remaining life of the main beam. If the status is abnormal or the life is insufficient, maintenance or replacement is recommended.

[0166] (4) Whole machine health assessment

[0167] 1) Data input

[0168] Overall machine working condition data: including lifting load, lifting speed, displacement, etc. Overall machine working condition data reflects the overall operating status of the crane and can reveal the global load and overall force conditions.

[0169] 2) Inference layer operation

[0170] Calculate stress spectrum coefficient: Calculate the stress spectrum coefficient of the whole machine based on the whole machine working condition data and the main beam health assessment results. The stress spectrum coefficient can quantify the average stress level of the whole machine under different working conditions.

[0171] Verify stress spectrum coefficients: Compare stress spectrum coefficients with the whole machine design standards to ensure that the overall operation meets the equipment health requirements.

[0172] Calculate and verify the whole machine working level: Calculate the whole machine working level based on the stress spectrum coefficient and verify whether it is consistent with the equipment design standard, so as to ensure that the whole machine operates within the specified safe working level.

[0173] 3) Verification layer operation

[0174] Comprehensive factor health assessment model: The health status of the whole machine is assessed through the model by comprehensively considering the health status of the main beam and other key components, changes in operating conditions, maintenance history and other factors.

[0175] Machine health assessment conclusion: The stress spectrum coefficient, working level and main beam health conclusion of the whole machine are combined to obtain the health status and remaining life of the whole machine. If the health index decreases or approaches the threshold, a targeted maintenance plan will be formulated for the whole machine to ensure overall safety and stability.

[0176] Step 3. Based on the results of the evaluation, quantitative data is obtained.

[0177] Based on the above health assessment model, quantitative data is obtained to provide quantitative data guidance for equipment repair and maintenance, so that the production and maintenance departments can predict the equipment status in advance, and thus arrange maintenance or parts plans in a targeted manner.

[0178] According to the health assessment model, the quantitative data obtained include the following key contents:

[0179] 1) Stress spectrum coefficient (SSC): It reflects the stress conditions of the equipment under different frequency bands and is a direct quantitative indicator of the health of the equipment structure. The higher the stress spectrum coefficient, the more complex the stress conditions of the equipment and the worse the health condition may be.

[0180] 2) Fatigue damage accumulation value (D): Indicates the degree of accumulated fatigue damage of the equipment under the current working conditions. The fatigue damage accumulation value obtained by methods such as Miner's Rule reflects the health status of the equipment under multiple cyclic loading. Generally, D=1 is set as the damage limit. When the D value is close to or exceeds 1, it means that the fatigue damage of the equipment is close to the failure threshold.

[0181] Stress-damage accumulation model: linear cumulative damage theory (such as Miner's law) is used to calculate the cumulative value of fatigue damage of parts under multiple cycles of stress. For the reel shaft, the cumulative damage value (D) can be estimated by the following formula:

[0182]

[0183] Among them, n i is the number of cycles at this stress level, N i It is the maximum number of cycles under this stress level. When the D value is close to 1, it means that the part is close to failure.

[0184] 3) Health Index (HI): It is usually a quantitative index of 0-1, where 0 indicates good health and 1 indicates extremely poor health and close to failure. This index takes into account stress spectrum coefficient, fatigue damage and remaining life, and can be used as a quantitative assessment of the overall health status of the equipment.

[0185] 4) Remaining useful life (RUL): Predict the remaining useful life of the equipment based on the fatigue damage accumulation model and stress spectrum coefficient. The shorter the RUL, the more the equipment needs to be maintained or key components replaced in advance.

[0186] Step 4: Provide maintenance guidance based on the quantitative data.

[0187] Using the above quantitative data, the maintenance department can implement preventive and targeted maintenance plans based on the following methods:

[0188] 1) Stress spectrum coefficient and component status detection

[0189] Targeted testing: For components with high stress spectrum coefficients (such as main beams and key stress points), special attention should be paid to their health status and targeted testing should be arranged. Ultrasonic testing, visual inspection and other methods can be used to check whether key components have cracks, deformation and other problems.

[0190] Spectrum trend monitoring: If the stress spectrum coefficient has a trend of gradually increasing, it means that the equipment's operating conditions have become heavier or the stress has increased abnormally. At this time, the equipment's load and operation mode should be evaluated, and maintenance should be arranged in advance to avoid sudden damage.

[0191] 2) Fatigue damage value and maintenance plan

[0192] Damage-graded maintenance: Fatigue damage values ​​are divided into different levels. For example, if the D value is below 0.6, daily maintenance can be arranged; between 0.6-0.8, it is recommended to arrange periodic in-depth inspections; when the D value exceeds 0.8, you should prepare to replace parts or carry out major repairs.

[0193] Advance component replacement plan: Based on fatigue damage values, the remaining durability of key components can be predicted, so that the replacement of key components can be arranged in advance to avoid temporary failures affecting production. For high-damage parts, such as main beam support structures, bearings, etc., regular replacement plans are arranged to ensure that the equipment is in the best condition before important production tasks.

[0194] 3) Health index and maintenance frequency

[0195] Maintenance interval adjustment: Dynamically adjust the maintenance frequency of the equipment according to the changing trend of the health index (HI). For example, when the health index is below 0.3, the equipment can maintain the current maintenance frequency; when the index is between 0.3-0.7, the maintenance cycle can be shortened; when the health index is close to 0.7 or above, it is recommended to increase the inspection frequency.

[0196] Phased maintenance plan: When the health index is close to 1, the risk of equipment failure is high. The maintenance department should increase the frequency of equipment status monitoring or perform temporary maintenance during this period to prevent sudden failures.

[0197] 4) Remaining useful life (RUL) and maintenance planning

[0198] Preventive maintenance plan: Based on RUL data, formulate a reasonable preventive maintenance plan. For example, if the equipment is expected to be close to failure in 3 months, the maintenance department can arrange maintenance 2 months in advance to ensure uninterrupted production.

[0199] Dynamic parts reserve: For components with a short lifespan (such as bearings, steel cables, etc.), spare parts reserves are arranged in advance according to their RUL to avoid the situation where there is no spare parts available in case of sudden failure, which affects the use of equipment.

[0200] Lifecycle management: Monitor the health status of the crane throughout its lifecycle and formulate corresponding maintenance strategies based on the RUL at different stages. For example, in the later stages of equipment use, the RUL may be shortened, and a more frequent maintenance strategy can be selected to ensure that the equipment status is adapted to its remaining life.

[0201] In some embodiments, the health assessment quantitative data is not only a reference for the maintenance department, but also provides support for production planning and resource allocation. The following are several methods for predicting equipment conditions in advance and optimizing maintenance based on quantitative data:

[0202] 1) Coordination of production and maintenance

[0203] Through health assessment data, the production department can understand the status of the equipment and make reasonable estimates of the equipment status during important missions. For example, for equipment with a low health index and a short RUL, the production department can avoid arranging high-load operations during critical missions.

[0204] For equipment that is close to the damage threshold, the maintenance department can arrange shutdown and maintenance in advance, and the production department can reasonably arrange replacement equipment or adjust the production plan to avoid sudden shutdowns that affect production.

[0205] 2) Optimal allocation of maintenance resources

[0206] Quantitative data provides a basis for the deployment of maintenance resources. For example, for equipment with a high D value, spare parts and required tools can be prepared in advance, and a suitable maintenance team can be arranged to carry out maintenance to avoid maintenance delays due to incomplete resources.

[0207] Utilize the quantitative data of health assessment to rationally arrange the configuration of maintenance personnel, spare parts and testing equipment, reduce the waste of maintenance resources and improve equipment utilization.

[0208] 3) Parts replacement and spare parts planning

[0209] According to fatigue damage value and RUL prediction data, spare parts planning and replacement are carried out in advance for key parts with short service life. Parts that need special attention are included in the "high-risk" spare parts list, and procurement is arranged in advance through consultation with suppliers.

[0210] Develop a cyclical plan for parts replacement to ensure regular replacement when equipment is in good condition and parts have sufficient life, and avoid unnecessary emergency purchases.

[0211] Health assessment based on quantitative data can not only reflect the current health status of the equipment, but also provide scientific guidance for equipment maintenance. Through comprehensive judgment of data such as stress spectrum coefficient, fatigue damage value, health index, and remaining service life, when the data triggers the warning value, the system reminds the equipment management personnel to arrange maintenance and eliminate hidden dangers. Provide quantitative data guidance for equipment maintenance, so that the production and maintenance departments can predict the equipment status in advance, so as to arrange maintenance or parts plans in a targeted manner, ensure that the equipment is in the best condition during the production task period, and improve equipment utilization efficiency and production reliability.

[0212] Some embodiments of the present application also provide a crane health assessment system, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the system is triggered to execute the aforementioned crane health assessment method.

[0213] In summary, the present application provides a crane health assessment method and system, which mainly predicts and monitors the fatigue life of the load-bearing parts of the main structural parts and key components of the crane based on a health assessment model. The model combines physical drive methods with data drive methods. First, the cumulative fatigue damage of the structure or mechanical parts in multiple loading cycles is estimated, and its failure life is predicted. Then, a data-driven method is used to train the prediction model based on historical health data. The two methods verify each other to improve the accuracy of the health assessment conclusions. By evaluating its health status, quantitative data guidance is provided for equipment maintenance, so that the production and maintenance departments can predict the equipment status in advance.

[0214] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0215] In a typical configuration of the present application, the terminal and the network device each include one or more processors (CPU), input / output interface, network interface and memory.

[0216] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0217] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0218] In addition, a part of the present application may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. The program instruction for calling the method of the present application may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in a working memory of a computer device that runs according to the program instruction. Here, according to an embodiment of the present application, a device is included, the device including a memory for storing computer program instructions and a processor for executing program instructions, wherein, when the computer program instruction is executed by the processor, the device is triggered to run the method and / or technical solution based on the aforementioned multiple embodiments according to the present application.

[0219] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware.

Claims

1. A crane health assessment method, characterized in that: The following steps are involved: Step 1. Collect data when the crane is in working state; Step 2. Performing a health assessment based on the collected data and the health assessment model; Step 3. Based on the results of the evaluation, obtaining quantitative data; Step 4: Provide maintenance guidance based on the quantitative data.

2. The method according to claim 1, characterized in that: The data collected in step 1 include: working condition data, which refers to information data related to the working condition of the crane; vibration data, which refers to data for real-time monitoring of dynamic changes in the crane's mechanism; strain data, which refers to data for real-time collection of strain changes in the crane's structural parts under different working conditions, which is used to analyze the actual stress and deformation state of the structural parts.

3. The method according to claim 1, characterized in that The health assessment model in step 2 includes the following two relationships: First, the relationship between crane structural parts and the health assessment of the whole machine: Remaining life of the whole machine: The life of the whole machine refers to the service life of the entire crane, including all structural parts, mechanisms and components; the remaining life of the whole machine depends on the comprehensive status of each key component and structural part, and the life of the whole machine will not exceed the minimum remaining life of each key component and structural part; Remaining life of important structural parts: important structural parts bear the main loads, and the life of the structural parts is one of the key factors for the overall life of the crane; the remaining life of the important structural parts is the lower limit of the life of the whole machine; The second is the relationship between crane parts and mechanism health assessment: Remaining life of key components: Key components are the core components of the mechanism. The health status of the key components directly determines the operating status of the mechanism. The remaining life of the mechanism is limited by the component with the shortest life among the key components. Remaining life of the mechanism: The remaining life of the mechanism refers to the remaining usable time of each mechanism under normal working conditions; the life of the mechanism affects the functionality and efficiency of the whole machine.

4. The method according to claim 1, characterized in that The health assessment model in step 2 includes a parts health assessment module and a mechanism health assessment module; Wherein, the parts health assessment module includes: ① Data input: the input data includes working condition data, including lifting load, lifting speed and lifting height; ②Calculation layer operation Calculating stress spectrum coefficients: performing force analysis according to the working condition data of the parts to obtain stress spectrum coefficients of the parts, wherein the stress spectrum coefficients reflect the actual stress conditions of the parts during operation; Verify the stress spectrum coefficient: compare the calculated stress spectrum coefficient with the design standard or the safety range provided by the manufacturer to verify whether the stress spectrum coefficient of the part is within a reasonable range; Calculate and verify the working level of parts: Calculate the working level of the parts based on the stress spectrum coefficients, and compare it with the design standards to determine whether preventive maintenance or replacement is required; ③Verification layer operation Health assessment model based on part stress change trend: Combine the stress change trend and historical working condition data of the part to evaluate the health of the part; the model analyzes the stress spectrum coefficient of the part and its change trend to predict possible failure risks; The Institutional Health Assessment Module includes: ①Data input The input data includes vibration data and working condition data; ②Calculation layer operation Calculating load spectrum coefficients: calculating load spectrum coefficients of the mechanism according to the working condition data; Verify the load spectrum coefficient: compare the calculated load spectrum coefficient with the design or standard requirements to verify whether the actual working load of the mechanism meets the health requirements; Calculate and verify the working level: Based on the load spectrum coefficient, further calculate the working level of the mechanism, and compare the calculated working level with the design standard to determine whether the mechanism is within a reasonable working range; ③Verification layer operation Health assessment model based on changes in operating conditions and maintenance parameters: The health status of the mechanism is analyzed by combining load spectrum coefficients, working levels, historical operating conditions and maintenance records; the model determines whether the mechanism has abnormal conditions based on the vibration data and load fluctuations.

5. The method according to claim 4, characterized in that The health assessment model in step 2 also includes a structural component health assessment module and a whole machine health assessment module; Wherein, the structural component health assessment module includes: ①Data input The input data includes strain data and working condition data; ②Calculation layer operation Calculating the load spectrum coefficient: calculating the load spectrum coefficient of the structural component according to the lifting load, speed and height in the working condition data, wherein the load spectrum coefficient reflects the average stress level and stress fluctuation of the structural component under different working conditions; Verify the load spectrum coefficient: compare the load spectrum coefficient with the design standard to verify whether the actual load condition of the structural member is within a reasonable range; Calculate and verify the working level: Calculate the working level of the structural parts based on the load spectrum coefficient; compare the working level with the design standard to ensure that the structural parts are within the safe working range; ③Verification layer operation Structural component stress change trend health assessment model: Based on the strain data and load spectrum coefficient, the stress change trend of the structural component is analyzed to assess its health status; if the stress changes significantly or the load is high for a long time, it indicates that the health status needs attention; The whole machine health assessment module includes: ①Data input The input data includes whole machine working condition data, which includes lifting load, lifting speed and displacement; the whole machine working condition data reflects the overall operating status of the crane; ②Calculation layer operation Calculating stress spectrum coefficients: Calculating stress spectrum coefficients of the whole machine according to the whole machine working condition data and the results of the structural component health assessment, wherein the stress spectrum coefficients can quantify the average stress level of the whole machine under different working conditions; Verify stress spectrum coefficients: Compare stress spectrum coefficients with the whole machine design standards to ensure that the overall operation meets the equipment health requirements; Calculate and verify the whole machine working level: Calculate the whole machine working level based on the stress spectrum coefficient, and verify whether it is consistent with the equipment design standard to ensure that the whole machine operates within the specified safe working level; ③Verification layer operation Comprehensive factor health assessment model: The health status, operating condition changes, and maintenance history of structural parts and other key components are comprehensively considered to evaluate the health status of the entire machine through the model.

6. The method according to claim 5, characterized in that The inference layer operation includes working cycle determination, wherein the working cycle determination process of the parts includes: collecting working condition data, the working condition data includes the lifting load, lifting height and lifting speed of the lifting mechanism; removing redundant information in the collected data, and processing missing values ​​and abnormal values; data integration: integrating the crane working condition data, design data and specification data on a unified database platform to form a comprehensive data view; judging the lifting action based on the lifting speed; splitting the integrated data to separate the working condition data; finding the working cycle based on the lifting load and lifting height; and statistically analyzing the load based on the working cycle, and the determination process ends.

7. The method according to claim 5, characterized in that The calculation layer operation includes the load spectrum and remaining life estimation of the crane: ① Load spectrum coefficient of the whole machine: The load spectrum coefficient and the number of used working cycles of the crane are estimated based on the actual working condition data, so as to further estimate the service life and unserviceable service life of the crane. K p The calculation formula is as follows: ； in: C i ——Work cycle data corresponding to each representative lifting load of the crane; the crane continuously collects data to obtain the real-time working condition data of the crane, and calculates the data of each work cycle, that is, C i is 1; C T ——Total number of crane working cycles; P Qi ——The maximum lifting load in each working cycle; P Qmax - rated load of the crane; m ——Power index, take m =3; ② Remaining service life of the whole machine Taking the design life of the crane as a reference value, considering the actual number of working cycles and the actual load spectrum coefficient, the actual remaining life of the crane is estimated. N Qy , the estimation formula is as follows: ； in: K P0 ——Crane design load spectrum factor; K P1 ——actual load spectrum coefficient of the crane; N Q0 - Design life of the crane; N Qz ——The number of working cycles the crane has been used, that is, its service life.

8. The method according to claim 5, characterized in that The calculation layer operations include crane parts stress spectrum and life estimation: ① Part stress spectrum coefficient The stress spectrum coefficient and stress cycle number of the parts are estimated according to the actual working conditions, so as to further estimate the service life and unserviceable service life of the parts. K S The calculation formula is as follows: ； in: n i - the number of stress cycles corresponding to different stresses occurring in mechanical parts; n T ——Total stress cycle number of mechanical parts; σ i ——Different stresses occurring in mechanical parts during working hours; σ max ——σ i The maximum stress in C - power index, determined by the tensile strength of the material and the fatigue limit of the component; ② Remaining service life of parts Taking the design life of the part as a reference value, considering the actual number of stress cycles and the actual stress spectrum coefficient, the actual remaining life of the part is estimated. N Gy , the estimation formula is as follows: ； in: K S0 ——Part design stress spectrum coefficient; K S1 ——actual stress spectrum coefficient of parts; N G0 - design life of parts; N Gz ——The number of stress cycles the part has been used, that is, its service life.

9. The method according to claim 1, characterized in that: The quantitative data in step 3 include: ① Stress spectrum coefficient: reflects the stress conditions of the equipment under different frequency bands and is a direct quantitative indicator of the health of the equipment structure. The higher the stress spectrum coefficient, the more complex the stress conditions of the equipment and the worse the health condition. ② Fatigue damage accumulation value D: indicates the accumulated fatigue damage degree of the equipment under the current working conditions. D=1 is set as the damage limit. When the D value is close to or exceeds 1, it means that the fatigue damage of the equipment is close to the failure threshold. ③ Health index: usually a quantitative index of 0-1, 0 means good health, 1 means extremely poor health, close to failure; ④ Remaining useful life RUL: predict the remaining useful life RUL of the equipment based on the fatigue damage accumulation value and stress spectrum coefficient.

10. A crane health assessment system, the system comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein: When the computer program instructions are executed by the processor, the system is triggered to execute the method according to any one of claims 1 to 9.

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