A crane health assessment method and system

Abstract

The application provides a crane health assessment method and system, the method comprising collecting data of a working state of the crane; performing health assessment based on the collected data and a health assessment model; obtaining quantitative data based on a result of the assessment and performing maintenance guidance based on the quantitative data. The application can monitor the use of the crane in real time, analyze and assess the state of main structural parts and key components of the crane, assess the health of the crane, provide quantitative data guidance for equipment maintenance, and realize early prediction of the state of the equipment by the production and maintenance departments, so as to arrange maintenance or component plans in a targeted manner.

Description

Technical Field

[0001] This invention relates to the field of special equipment operation and maintenance, and in particular to a method and system for assessing the health of cranes. Background Technology

[0002] Cranes are widely used in the shipbuilding industry and are one of the essential key pieces of equipment. Due to the increasing variety of ship types and the extended shipbuilding cycle, cranes are often operating at full load or even with malfunctions, making them highly susceptible to failure and causing safety accidents that seriously threaten the safety of production and the lives and property of the people.

[0003] In the current technology, 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 the frequent occurrence of various accident hazards.

[0004] Therefore, a solution is needed that can assess the health status of cranes in real time. Summary of the Invention

[0005] One objective of this application is to provide a method and system for assessing the health of cranes, thereby addressing the problem that it is difficult to perform real-time health assessments of cranes under existing technologies.

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

[0007] Step 1. Collect data on the crane's operating status;

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

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

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

[0011] Furthermore, the data collected in step 1 includes: operating condition data, which refers to information data related to the working status of the crane; vibration data, which refers to data on the dynamic changes of the crane's mechanism in real time; and strain data, which refers to data on the strain changes of the crane's structural components under different operating conditions collected in real time, used to analyze the actual stress and deformation state of the structural components.

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

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

[0014] Remaining life of the whole machine: The life of the whole machine refers to the service life of the crane as a whole, including all structural components, mechanisms and parts; the remaining life of the whole machine depends on the overall condition of each key component and structural component, and the life of the whole machine will not exceed the minimum remaining life of each key component and structural component.

[0015] Remaining life of critical structural components: Critical structural components bear the main load, and their lifespan is one of the key factors in the overall lifespan of the crane; the remaining lifespan of the critical structural components is the lower limit of the entire crane's lifespan.

[0016] Second, the relationship between crane components and the health assessment of the mechanism:

[0017] Remaining life of critical components: Critical components are the core components of a mechanism, and the health status of the critical 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 critical components.

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

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

[0020] The component health assessment module includes:

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

[0022] ②Calculation layer operation

[0023] Calculate the stress spectrum coefficient: Based on the working condition data of the part, perform stress analysis to obtain the stress spectrum coefficient of the part. The stress spectrum coefficient reflects the actual stress condition of the part 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 the part: Calculate the working level of the part based on the stress spectrum coefficient, and compare it with the design standard to determine whether preventive maintenance or replacement is required;

[0026] ③ Verification layer operation

[0027] Health assessment model based on component stress variation trend: Combines the stress variation trend of the component with historical working condition data to assess the health status of the component; the model analyzes the stress spectrum coefficient of the component and its variation trend to predict possible failure risks.

[0028] The institutional health assessment module includes:

[0029] ① Data Input

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

[0031] ②Calculation layer operation

[0032] Calculate the load spectrum coefficients: Calculate the load spectrum coefficients of the mechanism based on the operating condition data;

[0033] Verify the load spectrum coefficients: Compare the calculated load spectrum coefficients 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] ③ Verification layer operation

[0036] A health assessment model based on changes in operating conditions and maintenance parameters: combining load spectrum coefficients, operating levels, historical operating conditions, and maintenance records, the model analyzes the health status of the mechanism; the model determines whether the mechanism has any abnormalities based on the vibration data and load fluctuations.

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

[0038] The structural component health assessment module includes:

[0039] ① Data Input

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

[0041] ②Calculation layer operation

[0042] Calculate the load spectrum coefficient: Based on the lifting load, speed and height in the working condition data, calculate the load spectrum coefficient of the structural component. 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 on the structure is within a reasonable range;

[0044] Calculate and verify the working level: Based on the load spectrum coefficient, calculate the working level of the structural component; compare the working level with the design standard to ensure that the structural component is within the safe working range;

[0045] ③ Verification layer operation

[0046] Structural component stress variation trend health assessment model: Based on the strain data and load spectrum coefficients, the stress variation trend of the structural component is analyzed to assess its health status; if the stress variation is significant or there is a long-term high load, it indicates that the health status needs attention.

[0047] The overall machine health assessment module includes:

[0048] ① Data Input

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

[0050] ②Calculation layer operation

[0051] Calculate the stress spectrum coefficient: Based on the overall machine operating data and the results of the structural component health assessment, calculate the stress spectrum coefficient of the whole machine. The stress spectrum coefficient can quantify the average stress level of the whole machine under different working conditions.

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

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

[0054] ③ Verification layer operation

[0055] Comprehensive Factor Health Assessment Model: The health status of the entire machine is assessed by taking into account the health status of structural components and other key parts, changes in operating conditions, and maintenance history.

[0056] Furthermore, the calculation layer operation includes work cycle determination, wherein the work cycle determination process for the part includes: collecting working condition data, which includes the lifting load, lifting height, and lifting speed of the lifting mechanism; removing redundant information from the collected data and processing missing and outlier values; data integration: integrating crane working condition data, design data, and specification data into a unified database platform to form a comprehensive data view; determining the lifting action based on the lifting speed; splitting the integrated data to extract the working condition data; identifying the work cycle based on the lifting load and lifting height; and calculating the load based on the work cycle, at which point the determination process ends.

[0057] Furthermore, the calculation layer operation includes the crane's overall load spectrum and remaining life estimation:

[0058] ① Overall load spectrum coefficients:

[0059] Based on actual operating data, the load spectrum coefficient and the number of working cycles used by the crane are estimated, thereby further estimating the crane's service life and remaining service life. 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 various representative lifting loads of the crane; the crane continuously collects data to obtain real-time operating data, and calculates the data for each work cycle, i.e., C. i =1; C T —Total number of crane work cycles; P Qi — This represents the maximum lifting load per work cycle; P Qmax —Rated load of the crane; m —Power exponent, take m = 3;

[0062] ② Remaining service life of the whole machine

[0063] Using the crane's design life as a reference, and considering the actual number of working cycles and the actual load spectrum coefficient, estimate the crane's actual remaining life N. Qy The estimation formula is as follows:

[0064]

[0065] Where: K P0 —Crane design load spectrum coefficient; K P1 — Actual load spectrum coefficient of the crane; N Q0 —Crane design life; N Qz —The number of working cycles the crane has used, i.e., its service life.

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

[0067] ① Stress spectrum coefficient of the part

[0068] The stress spectral coefficient and stress cycle number of the part are estimated based on actual working conditions, thereby further estimating the part's service life and remaining service life. The stress spectral coefficient K of the part is... S The calculation formula is as follows:

[0069]

[0070] Where: n i —The number of stress cycles corresponding to different stresses occurring in a mechanical part; n T —The total number of stress cycles for the mechanical parts; σ i —Different stresses occurring in mechanical parts during working time; 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;

[0071] ② Remaining service life of parts

[0072] Using the design life of the part as a reference value, and considering the actual stress cycle number 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 —Stress spectrum coefficient of the part design; K S1 — Actual stress spectrum coefficient of the part; N G0 —Component design life; N Gz —The number of stress cycles a part has undergone, i.e., its service life.

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

[0076] ① Stress spectrum coefficient: It reflects the stress situation of the equipment under different frequency bands and is a direct quantitative indicator of the structural health of the equipment; the higher the stress spectrum coefficient, the more complex the stress situation of the equipment and the worse its health condition.

[0077] ② Fatigue damage cumulative value D: This represents the cumulative fatigue damage of the equipment under the current working conditions. D = 1 is set as the damage limit. When the value of D 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: This is usually a quantitative indicator ranging from 0 to 1, where 0 indicates good health and 1 indicates extremely poor health.

[0079] Near failure;

[0080] ④ Remaining Service Life (RUL): The remaining service life (RUL) of the device is predicted based on the cumulative fatigue damage value and stress spectrum coefficient.

[0081] On the other hand, this 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 crane health assessment method described above.

[0082] Compared with existing technologies, the crane health assessment provided in this application mainly predicts and monitors the fatigue life of the load-bearing parts of the crane's main structural components and key parts. It estimates the crane's remaining service life by establishing a life prediction model based on stress spectrum coefficients. The remaining service life prediction method combines physical-driven and data-driven approaches. First, a physical model based on fatigue damage, based on linear cumulative damage theory, is used to estimate the cumulative fatigue damage of structural or mechanical components under multiple loading cycles and predict their failure life. Then, a data-driven approach is used to train a prediction model (machine learning model, support vector machine) based on historical health data. The two methods are mutually validated to improve the accuracy of the health assessment conclusions. Furthermore, this assessment process analyzes and evaluates the status of the crane's main structural components and key parts based on the crane's real-time usage, assessing their health status and providing quantitative data guidance for equipment maintenance. This allows production and maintenance departments to anticipate equipment conditions in advance, enabling targeted maintenance or parts planning. Attached Figure Description

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

[0084] Figure 2 This is a flowchart illustrating the working cycle determination process of the crane drum shaft according to the present invention.

[0085] Figure 3 This is a flowchart of the crane remaining life assessment method of the present invention. Detailed Implementation

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

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

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

[0089] In this scenario, the crane continuously lifts loads during operation. These loads are typically heavy industrial components. The continuous lifting of loads by the crane has a significant impact on the overall health of the crane, its major structural components, and critical parts, and will continuously reduce the lifespan of the crane as a whole, as well as the lifespan of its major structural components and critical parts.

[0090] However, on-site safety management personnel find it difficult to accurately assess the overall health status of the crane, as well as the health status of its major structural components and key parts. Health status assessments are often based on static experience and cannot keep track of the real-time health status of the entire crane, its major structural components, and key parts. This makes it difficult to control potential safety hazards, and remedial measures are often only taken after an accident occurs, resulting in significant problems with real-time safety.

[0091] To address the aforementioned issue of the inability to monitor the health status of the entire crane, its main structural components, and critical parts in real time, some embodiments of this application provide crane health assessments that primarily predict and monitor the fatigue life of the load-bearing parts of the crane's main structural components and critical parts. This process involves collecting relevant data from the crane in real time, analyzing and evaluating the status of the crane's main structural components and critical parts, assessing their health condition, providing quantitative data guidance for equipment maintenance, and enabling production and maintenance departments to anticipate equipment conditions in advance, thereby allowing for targeted maintenance or parts planning.

[0092] In some embodiments, such as Figure 1 As shown, the internal architecture of the crane health assessment system contains the following relationships:

[0093] ① The relationship between crane structural components and overall machine health assessment:

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

[0095] Remaining life of critical structural components: Critical structural components (such as main beams and outriggers) bear the main loads, and their structural life is one of the key factors in the overall life of the crane. The remaining life of critical structural components is often the lower limit of the entire crane's life, because the failure of structural components usually directly affects the crane's safety and operability.

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

[0097] Remaining life of critical components: Critical components (such as motors, wire ropes, bearings, gears, etc.) are the core components of mechanisms (such as hoisting mechanisms, slewing mechanisms, traveling mechanisms, etc.). The health status of these components directly determines the operational status of the mechanism. The remaining life of the mechanism is usually limited by the component with the shortest life among the critical components. Critical components are the functional components of the mechanism. Once a component approaches its limit life, the life of the entire mechanism will be constrained, and it will be unable to continue operating safely. For example, severe wear on the wire rope or reducer gear in the hoisting mechanism will shorten the remaining life of the hoisting mechanism.

[0098] Mechanism Remaining Life: The remaining life of a mechanism refers to the remaining usable time of each mechanism under normal operating conditions. Mechanism life is affected by factors such as usage frequency and load, thus impacting the overall functionality of the machine. While the lifespan of individual mechanisms does not directly cause structural failure, it does affect their functionality and efficiency, ultimately indirectly impacting the overall lifespan of the machine. Replacing key components can extend the service life of a mechanism.

[0099] Some embodiments of this application provide a method for assessing the health of a crane, including the following steps:

[0100] Step 1. Collect data on the crane's working status.

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

[0102] Step 2. Conduct a health assessment based on the collected data and the health assessment model.

[0103] The health assessment of a crane's entire machine, structural components, mechanisms, and parts mainly includes two operational steps: the estimation layer and the verification layer. The estimation layer operation includes:

[0104] 1) Work cycle determination

[0105] Understandably, according to the definition of a work cycle in the "Crane Design Code", a crane's work cycle refers to a complete process from the lifting of one item to the lifting of the next item, including the crane's operation and normal rest.

[0106] For example, the overall working cycle of the crane is defined as follows: Since the main work of the crane is performed by the hoisting mechanism, the working cycle is determined by analyzing the operating parameters of the hoisting mechanism. Considering the changing characteristics of the various operating data of the hoisting mechanism, the hoisting height value without random drift error is selected to determine the crane's operation. This is then combined with the hoisting load data to determine the working cycle of the hoisting mechanism. Finally, combined with the working cycle of the main hoisting mechanism, the overall working cycle of the crane is defined.

[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; therefore, the working cycle calculation of the drum shaft is defined according to the changes in the lifting mechanism's movements. For example... Figure 2 As shown, the drum shaft working cycle determination process includes: collecting working condition data, which includes the lifting load, lifting height, and lifting speed of the hoisting mechanism; removing redundant information from the collected data and handling missing and outlier values ​​to ensure data quality; data integration: integrating crane working condition data, design data, and specification data into a unified database platform to form a comprehensive data view; determining the lifting action based on the lifting speed; splitting the integrated data to extract the working condition data; identifying the working cycle based on the lifting load and lifting height; and statistically analyzing the load based on the working cycle, thus concluding the determination process.

[0108] 2) Overall load spectrum and life estimation of the crane

[0109] ① Overall load spectrum coefficients:

[0110] When designing a crane, its service class and load condition level are selected, which specifies the total number of working cycles and the load spectrum coefficient. However, actual usage conditions differ from the design, requiring estimation of the crane's load spectrum coefficient and the number of working cycles already completed based on actual operating conditions. This, in turn, allows for a further estimation of the crane's service life and remaining service life.

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

[0112]

[0113] Where: K p —Crane load spectrum coefficient; C i —The number of work cycles corresponding to each representative lifting load of the crane; C T —Total number of crane work cycles; P Qi —A representative lifting load that characterizes the crane's working tasks over its expected lifespan; P Qmax — The rated load of the crane; m — the power exponent, take m = 3.

[0114] The crane collects continuous data, providing real-time operating data. Therefore, it's unnecessary to select a representative work cycle for calculation; instead, data from each work cycle is collected for calculation, i.e., C. i P is 1. Qi This represents the maximum lifting load for each work cycle.

[0115] ② Remaining service life of the whole machine

[0116] Based on the principle of cumulative damage, each operation of a crane causes varying degrees of damage to the entire machine. Therefore, the lifespan of a crane cannot be directly calculated based on the number of working cycles specified in the design. Instead, the actual service life of the crane is estimated by taking into account the actual number of working cycles and the actual load spectrum coefficient, using the crane's design life as a reference. The estimation method is as follows:

[0117]

[0118] Where: N Qy — Remaining number of working cycles (remaining life) of the crane; K P0 —Crane design load spectrum coefficient; K P1 — Actual load spectrum coefficient of the crane; N Q0 —Crane design life; N Qz — The number of working cycles the crane has used (its service life).

[0119] The lifespan of a crane refers to the total number of working cycles from the start of its use to its final scrapping. For cranes still in service, and where working cycle information was not collected from the beginning of use, it's impossible to obtain an accurate working history throughout the crane's entire lifespan. The only way is to estimate the used lifespan based on collected data. Estimations using small data samples cannot accurately describe the crane's lifespan, but as the sample size increases, the estimation results will approach the actual results, continuously improving accuracy, ultimately enabling the prediction of the crane's remaining service life. The specific technical approach is as follows: Figure 3 As shown.

[0120] 3) Drum shaft stress spectrum and life estimation

[0121] ① Stress at the critical section of the drum shaft

[0122] The drum shaft is a component in the hoisting and traction mechanisms of a crane used for winding the wire rope. Taking the drum shaft in the hoisting mechanism as the object of analysis, the forces acting on it reveal that it is primarily subjected to gravity, rope tension, and the normal force exerted by the pinion gear. Gravity includes the drum shaft's own weight and the weight of the wire rope wound around it; the weight of the wire rope is related to the hoisting height. The rope tension is related to the hoisting load, and its position is related to the hoisting height. The force exerted by the pinion gear is synchronized with the rope tension, meaning it is also related to the hoisting load and hoisting height. The stress at the critical section of the drum shaft is formed by the superposition of these three forces.

[0123] ② Stress spectrum coefficient of the drum shaft

[0124] The design of cranes also specifies the service level and stress state level of key components, that is, the total number of stress cycles and stress spectrum coefficient of the component. However, actual usage conditions will certainly differ from the design, so it is necessary to estimate the stress spectrum coefficient and stress cycle number of the component based on the actual working conditions, and then further estimate the component's service life and remaining service life.

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

[0126] Where: K S —Stress spectrum coefficients of mechanical parts; n i —The number of stress cycles corresponding to different stresses occurring in a mechanical part; n T —The total number of stress cycles for the mechanical parts; σ i —Different stresses occurring in mechanical parts during working time; 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 hoisting mechanism drum shaft as an example, based on the real-time operating data of the crane hoisting mechanism, the stress data of the drum shaft under each working cycle of the hoisting mechanism is statistically analyzed, and the calculation is performed according to the actual situation, i.e., n i =1, σ i This represents the total stress occurring on the cross-section of the drum shaft during a single ascending or descending motion of the hoisting mechanism.

[0128] ③ Remaining service life of the drum shaft

[0129] Within one working cycle of the hoisting mechanism, the drum shaft may experience multiple stress cycles. Similar to estimating the remaining life of the crane, the stress on the drum shaft caused by the load will result in varying degrees of damage. Likewise, the life of the drum shaft cannot be directly calculated based on the total number of stress cycles specified in the design. Instead, the actual service life of the drum shaft is estimated by taking into account the actual number of stress cycles and the actual stress spectrum coefficient, using the design life of the drum shaft as a reference. The estimation method is as follows:

[0130]

[0131] Where: N Gy —Residual stress cycle count (remaining life) of the drum shaft; K S0 —Design stress spectrum coefficient of the drum shaft; K S1 — Actual stress spectrum coefficient of the drum shaft; N G0 —Design life of the drum shaft; N Gz — The number of stress cycles used on the drum shaft (its service life).

[0132] The lifespan of a drum shaft refers to the total number of stress cycles from the start of its use to its eventual scrapping. For drum shafts still in service, and where stress cycle data was not collected from the beginning of use, it's impossible to obtain an accurate understanding of their entire lifespan. The only way to estimate their remaining lifespan is through the collected data. While estimations based on small data samples cannot accurately describe the drum shaft's lifespan, increasing the sample size will bring the estimation results closer to the actual results, continuously improving accuracy, and ultimately enabling the prediction of the remaining service life of the drum shaft.

[0133] Further as Figure 1 As shown, the four health assessment modules will be explained in detail below.

[0134] (1) Part health assessment - taking the drum shaft as an example

[0135] 1) Data Input

[0136] Operating data: This also includes lifting load, lifting speed, and lifting height, providing necessary data support for the stress analysis of the drum shaft.

[0137] 2) Calculation layer operation

[0138] Calculation of stress spectrum coefficients: Based on the operating data of the drum shaft, stress analysis is performed to obtain the stress spectrum coefficients of the drum shaft. The stress spectrum coefficients reflect the actual stress conditions experienced by the drum shaft during operation.

[0139] 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 drum shaft is within a reasonable range.

[0140] Calculate and verify the working level of the 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 required.

[0141] 3) Verification layer operation

[0142] A health assessment model based on component stress variation trends: This model assesses the health status of components by combining the stress variation trends of the drum shaft with historical operating data. It analyzes the stress spectrum coefficients of the drum shaft and their variation trends to predict potential failure risks.

[0143] Health assessment conclusion: By analyzing the trend of stress changes on the drum shaft, the health status and remaining service life of the drum shaft are predicted. When stress changes are significant or the health index is lower than expected, timely replacement or repair is recommended.

[0144] (2) Institutional health assessment – ​​taking lifting institutions as an example

[0145] 1) Data Input

[0146] Vibration data: used for real-time monitoring of the dynamic changes of the hoisting mechanism.

[0147] Operating data includes lifting load, lifting speed, and lifting height, providing detailed information on equipment load and operating conditions.

[0148] 2) Calculation layer operation

[0149] Calculate the load spectrum coefficient: Based on the lifting load, speed, and height data from the operating conditions, calculate the load spectrum coefficient of the hoisting mechanism. The load spectrum coefficient is an indicator for evaluating the average load and load fluctuation of the equipment under different operating conditions.

[0150] Verify the load spectrum coefficients: Compare the calculated load spectrum coefficients with the design or standard requirements to verify whether the actual working load of the hoisting 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 hoisting 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] A health assessment model based on changes in operating conditions and maintenance parameters analyzes the health status of the hoisting mechanism by combining load spectrum coefficients, operating levels, historical operating conditions, and maintenance records. The model determines whether there are any abnormalities in the mechanism based on vibration signals, load fluctuations, and other factors.

[0154] Health assessment conclusion: Based on the load spectrum coefficient, operating level, and maintenance data of the mechanism, a conclusion on the mechanism's health status is drawn. If the health index decreases or the operating level exceeds the design range, maintenance of the hoisting mechanism is recommended.

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

[0156] 1) Data Input

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

[0158] Operating data includes lifting load, lifting speed, lifting height, etc., reflecting the load and operating conditions of the main beam during operation.

[0159] 2) Calculation layer operation

[0160] Calculate the load spectrum coefficients: Based on the load, speed, and height data from the operating conditions, calculate the load spectrum coefficients of the main beam. The load spectrum coefficients reflect the average stress level and stress fluctuations of the main beam under different operating conditions.

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

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

[0163] 3) Verification layer operation

[0164] Structural component stress variation trend health assessment model: Based on stress data and load spectrum coefficients, the stress variation trend of the main beam is analyzed to assess its health status. Significant stress changes or prolonged high loads indicate that the health status needs attention.

[0165] Health assessment conclusion: A comprehensive analysis of stress variation trends and working level will determine the health status or remaining life of the main beam. If the condition is abnormal or the lifespan is insufficient, maintenance or replacement is recommended.

[0166] (4) Overall health assessment

[0167] 1) Data Input

[0168] Overall machine operating data includes lifting load, lifting speed, displacement, etc. This data reflects the crane's overall operating status and reveals the global load and overall stress conditions.

[0169] 2) Calculation layer operation

[0170] Calculation of stress spectrum coefficients: Based on the overall machine operating data and the main beam health assessment results, the stress spectrum coefficients of the entire machine are calculated. The stress spectrum coefficients quantify the average stress level of the entire machine under different operating conditions.

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

[0172] Calculate and verify the overall machine operating level: Calculate the overall machine operating level based on the stress spectrum coefficient and verify whether it is consistent with the equipment design standards, thereby ensuring that the whole machine operates within the specified safe operating level.

[0173] 3) Verification layer operation

[0174] Comprehensive factor health assessment model: The model assesses the overall health status of the machine by taking into account factors such as the health status of the main beam and other key components, changes in operating conditions, and maintenance history.

[0175] Overall machine health assessment conclusion: By combining the stress spectrum coefficient, working level, and main beam health conclusions, the overall health status and remaining life of the machine are determined. If the health index decreases or approaches the threshold, a targeted maintenance plan is developed for the entire machine to ensure overall safety and stability.

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

[0177] Based on the above health assessment model, quantitative data is obtained, providing quantitative data guidance for equipment maintenance and repair. This enables production and maintenance departments to anticipate equipment conditions in advance, thereby arranging maintenance or spare parts plans in a targeted manner.

[0178] Based on the health assessment model, the obtained quantitative data includes the following key components:

[0179] 1) Stress Spectrum Coefficient (SSC): Reflects the stress conditions of equipment at different frequency bands and is a direct quantitative indicator of the structural health of the equipment. The higher the stress spectrum coefficient, the more complex the stress conditions of the equipment, and the worse its health condition may be.

[0180] 2) Cumulative Fatigue Damage Value (D): This represents the cumulative degree of fatigue damage to the equipment under current operating conditions. The cumulative fatigue damage value obtained through 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 equipment fatigue damage is approaching the failure threshold.

[0181] Stress-damage accumulation model: This model uses linear cumulative damage theory (such as Miner's rule) to calculate the cumulative fatigue damage of a part under multiple cycles of stress. For a drum shaft, the cumulative damage value (D) can be estimated using the following formula:

[0182]

[0183] Where, n i N represents the number of cycles at this stress level. i This represents the maximum number of cycles that the component can withstand at this stress level. When the D value is close to 1, it indicates that the component is nearing failure.

[0184] 3) Health Index (HI): This is typically a quantitative indicator ranging from 0 to 1, where 0 indicates good health and 1 indicates extremely poor health, nearing failure. This index comprehensively considers stress spectrum coefficients, fatigue damage, and remaining life, and can serve as a quantitative assessment of the overall health status of the equipment.

[0185] 4) Remaining Service Life (RUL): Predicts the remaining usable time of the equipment based on a fatigue damage accumulation model and stress spectrum coefficients. The shorter the RUL, the sooner the equipment needs maintenance or replacement of critical components.

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

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

[0188] 1) Stress spectrum coefficient and component condition inspection

[0189] Targeted testing: For components with high stress spectrum coefficients (such as main beams and critical load-bearing points), their health status should be closely monitored, and targeted testing should be arranged. Ultrasonic testing, visual inspection, and other methods can be used to check for cracks, deformation, and other problems in critical components.

[0190] Spectrum trend monitoring: If the stress spectrum coefficient shows a gradual increasing trend, it indicates that the equipment's operating conditions are becoming heavier or the stress is abnormally increasing. In this case, the equipment's operating load and mode should be assessed, and maintenance should be arranged in advance to avoid sudden damage.

[0191] 2) Fatigue damage values ​​and maintenance plan

[0192] Damage grading and maintenance: Fatigue damage values ​​are divided into different grades. For example, if the D value is below 0.6, routine maintenance can be arranged; if it is between 0.6 and 0.8, periodic in-depth inspections are recommended; when the D value exceeds 0.8, replacement of parts or major overhaul should be prepared.

[0193] Advance component replacement planning: Based on fatigue damage values, the remaining durability of critical components can be predicted, allowing for advance replacement of these components and preventing temporary failures from impacting production. For high-damage parts, such as main beam support structures and bearings, regular replacement plans are implemented to ensure equipment is in optimal condition before critical production tasks.

[0194] 3) Health index and maintenance frequency

[0195] Maintenance Interval Adjustment: The maintenance frequency of the equipment is dynamically adjusted based on the 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 and 0.7, the maintenance cycle can be shortened; when the Health Index is close to or above 0.7, 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. During this period, the maintenance department should increase the frequency of equipment status monitoring or perform temporary maintenance to prevent sudden failures.

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

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

[0199] Dynamic spare parts inventory: For components with short lifespans (such as bearings, steel cables, etc.), spare parts are stockpiled in advance according to their RUL (Rating Limits) to avoid the lack of spare parts available in the event of a sudden failure, which would affect the use of the equipment.

[0200] Lifecycle Management: Monitor the health status of the crane throughout its entire lifecycle and develop corresponding maintenance strategies based on the Recovery After Life (RUL) at different stages. For example, in the later stages of equipment use, the RUL may shorten, at which point a more frequent maintenance strategy can be selected to ensure that the equipment is in a condition suitable for its remaining lifespan.

[0201] In some embodiments, quantitative health assessment data not only serves as a reference for maintenance departments but also supports 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] By using health assessment data, the production department can understand the status of equipment and make reasonable predictions about equipment conditions during critical mission periods. For example, for equipment with low health indices and short recovery periods (RUL), the production department can avoid scheduling high-load operations during critical mission periods.

[0204] For equipment approaching the damage threshold, the maintenance department can arrange for shutdown and repair in advance, and the production department can reasonably arrange alternative equipment or adjust the production plan to avoid sudden shutdowns affecting production.

[0205] 2) Optimized allocation of maintenance resources

[0206] Quantitative data provides a basis for the allocation of maintenance resources. For example, for equipment with a high D-value, spare parts and necessary tools can be prepared in advance, and a suitable maintenance team can be arranged for inspection and repair, avoiding maintenance delays due to insufficient resources.

[0207] By utilizing quantitative data from health assessments, we can rationally allocate the configuration of maintenance personnel, spare parts, and testing equipment, thereby reducing waste of maintenance resources and improving equipment utilization.

[0208] 3) Parts replacement and spare parts plan

[0209] Based on fatigue damage values ​​and RUL (Rapid Utilization Limit) prediction data, spare parts planning and replacement should be carried out in advance for critical components with short service life. Components requiring special attention should be included in a "high-risk" spare parts list, and procurement should be arranged in advance with suppliers.

[0210] Develop a cycle plan for parts replacement to ensure that replacements are carried out regularly when the equipment is in good condition and the parts have sufficient lifespan, thus avoiding unnecessary emergency procurement.

[0211] Quantitative data-driven health assessments not only reflect the current health status of equipment but also provide scientific guidance for equipment maintenance. By comprehensively judging data such as stress spectrum coefficients, fatigue damage values, health indices, and remaining service life, the system alerts equipment managers to schedule maintenance and eliminate potential hazards when data triggers warning thresholds. This provides quantitative data guidance for equipment maintenance, enabling production and maintenance departments to anticipate equipment conditions in advance, thereby allowing for targeted maintenance or spare parts planning. This ensures equipment is in optimal condition during production periods, improving equipment utilization efficiency and production reliability.

[0212] Some embodiments of this 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, this application provides a method and system for assessing the health of cranes. It primarily predicts and monitors the fatigue life of load-bearing components of major structural parts and key components of a crane based on a health assessment model. This model combines physical-driven and data-driven methods. First, it estimates the cumulative fatigue damage of structural or mechanical components under multiple loading cycles and predicts their failure life. Then, it uses a data-driven method to train the prediction model based on historical health data. The two methods are mutually validated to improve the accuracy of the health assessment conclusions. By assessing the health status, it provides quantitative data guidance for equipment maintenance and repair, enabling production and maintenance departments to anticipate equipment conditions in advance.

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

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

[0216] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0217] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0218] Furthermore, a portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. The program instructions invoking the methods of this application may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal carrying medium, and / or stored in the working memory of a computer device operating according to the program instructions. Here, one embodiment of this application includes a device 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 device is triggered to run methods and / or technical solutions based on the foregoing embodiments of this application.

[0219] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware.

Claims

1. A method for assessing the health of a crane, characterized in that, Includes the following steps: Step 1. Collect data on the crane's operating status; Step 2. Conduct a health assessment based on the collected data and the health assessment model; Step 3. Based on the evaluation results, obtain quantitative data; Step 4. Provide maintenance guidance based on the quantitative data; The data collected in step 1 includes: operating condition data, which refers to information data related to the working status of the crane; vibration data, which refers to data on the dynamic changes of the crane's mechanism in real time; and strain data, which refers to data on the strain changes of the crane's structural components under different operating conditions, used to analyze the actual stress and deformation state of the structural components. The health assessment model in step 2 includes the following two layers of relationships: First, the relationship between the structural components of the crane and the overall machine health assessment: Remaining life of the whole machine: The life of the whole machine refers to the service life of the crane as a whole, including all structural components, mechanisms and parts; the remaining life of the whole machine depends on the overall condition of each key component and structural component, and the life of the whole machine will not exceed the minimum remaining life of each key component and structural component. Remaining life of critical structural components: Critical structural components bear the main load, and their lifespan is one of the key factors in the overall lifespan of the crane; the remaining lifespan of the critical structural components is the lower limit of the entire crane's lifespan. Second, the relationship between crane components and the health assessment of the mechanism: Remaining life of critical components: Critical components are the core components of a mechanism, and the health status of the critical 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 critical components. Remaining lifespan of the mechanism: The remaining lifespan of the mechanism refers to the remaining usable time of each mechanism under normal operating conditions; the lifespan of the mechanism affects the functionality and efficiency of the whole machine. The health assessment model in step 2 includes a component health assessment module and a mechanism health assessment module; The component health assessment module includes: ① Data input: The input data includes operating condition data, including lifting load, lifting speed, and lifting height; ②Calculation layer operation Calculate the stress spectrum coefficient: Based on the working condition data of the part, perform stress analysis to obtain the stress spectrum coefficient of the part. The stress spectrum coefficient reflects the actual stress condition of the part 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 the part: Calculate the working level of the part based on the stress spectrum coefficient, and compare it with the design standard to determine whether preventive maintenance or replacement is required; ③ Verification layer operation Health assessment model based on component stress variation trend: Combines the stress variation trend of the component with historical working condition data to assess the health status of the component; the model analyzes the stress spectrum coefficient of the component and its variation trend to predict possible failure risks. The institutional health assessment module includes: ① Data Input The input data includes vibration data and operating condition data; ②Calculation layer operation Calculate the load spectrum coefficients: Calculate the load spectrum coefficients of the mechanism based on the operating condition data; Verify the load spectrum coefficients: Compare the calculated load spectrum coefficients 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; ③ Verification layer operation A health assessment model based on changes in operating conditions and maintenance parameters: This model analyzes the health status of the mechanism by combining load spectrum coefficients, operating levels, historical operating conditions, and maintenance records; the model determines whether the mechanism has any abnormalities based on the vibration data and load fluctuations; the quantification data in step 3 includes: ① Stress spectrum coefficient: It reflects the stress situation of the equipment under different frequency bands and is a direct quantitative indicator of the structural health of the equipment; the higher the stress spectrum coefficient, the more complex the stress situation of the equipment and the worse its health condition. ② Fatigue damage cumulative value D: This represents the cumulative fatigue damage 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 equipment fatigue damage is close to the failure threshold. ③ Health Index: Usually a quantitative indicator of 0-1, where 0 indicates good health and 1 indicates extremely poor health, close to failure; ④ Remaining Service Life (RUL): The remaining service life (RUL) of the device is predicted based on the cumulative fatigue damage value and stress spectrum coefficient.

2. The method according to claim 1, 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; The structural component health assessment module includes: ① Data Input The input data includes strain data and operating condition data; ②Calculation layer operation Calculate the load spectrum coefficient: Based on the lifting load, speed and height in the working condition data, calculate the load spectrum coefficient of the structural component. 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 on the structure is within a reasonable range; Calculate and verify the working level: Based on the load spectrum coefficient, calculate the working level of the structural component; compare the working level with the design standard to ensure that the structural component is within the safe working range; ③ Verification layer operation Structural component stress variation trend health assessment model: Based on the strain data and load spectrum coefficients, the stress variation trend of the structural component is analyzed to assess its health status; if the stress variation is significant or there is a long-term high load, it indicates that the health status needs attention. The overall machine health assessment module includes: ① Data Input The input data includes overall machine operating data, which includes lifting load, lifting speed, and displacement; the overall machine operating data reflects the overall operating status of the crane. ②Calculation layer operation Calculate the stress spectrum coefficient: Based on the overall machine operating data and the results of the structural component health assessment, calculate the stress spectrum coefficient of the whole machine. The stress spectrum coefficient can quantify the average stress level of the whole machine under different working conditions. Verify the stress spectrum coefficient: Compare the stress spectrum coefficient with the overall machine design standard to ensure that the overall operation meets the equipment health requirements; Calculate and verify the overall machine operating level: Calculate the overall machine operating level based on the stress spectrum coefficient and verify whether it is consistent with the equipment design standards to ensure that the whole machine operates within the specified safe operating level; ③ Verification layer operation Comprehensive Factor Health Assessment Model: The health status of the entire machine is assessed by taking into account the health status of structural components and other key parts, changes in operating conditions, and maintenance history.

3. The method according to claim 2, characterized in that, The calculation layer operation includes work cycle determination, wherein the work cycle determination process for a part includes: collecting working condition data, which includes the lifting load, lifting height, and lifting speed of the hoisting mechanism; removing redundant information from the collected data and processing missing and outlier values; data integration: integrating crane working condition data, design data, and specification data into a unified database platform to form a comprehensive data view; determining the lifting action based on the lifting speed; splitting the integrated data to extract the working condition data; identifying the work cycle based on the lifting load and lifting height; and calculating the load based on the work cycle, at which point the determination process ends.

4. The method according to claim 3, characterized in that, The calculation layer operation includes the crane's overall load spectrum and remaining life estimation: ① Overall load spectrum coefficients: Based on actual operating data, the load spectrum coefficient and the number of working cycles used by the crane are estimated, thereby further estimating the crane's service life and remaining service life. The load spectrum coefficient of the crane... K p The calculation formula is as follows: ； in: C i —Work cycle data corresponding to various representative lifting loads of the crane; the crane continuously collects data to obtain real-time operating data, and calculates the data for each work cycle. C i =1; C T —Total number of working cycles of the crane; P Qi —The maximum lifting load for each work cycle; P Qmax —The rated load of the crane; m — Power exponent, take m =3; ② Remaining service life of the whole machine Using the crane's design life as a reference, and considering the actual number of working cycles and the actual load spectrum coefficient, the crane's actual remaining life is estimated. N Qy The estimation formula is as follows: ； in: K P0 —Crane design load spectrum coefficient; K P1 — Actual load spectrum coefficient of the crane; N Q0 —Crane design life; N Qz —The number of working cycles the crane has used, i.e., its service life.

5. The method according to claim 4, characterized in that, The calculation layer operation includes stress spectrum and life estimation of crane components: ① Stress spectrum coefficient of the part The stress spectral coefficient and stress cycle number of the component are estimated based on actual working conditions, thereby further estimating the component's service life and remaining service life. The stress spectral coefficient of the component... 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 —The total number of stress cycles for the mechanical parts; σ i —Different stresses occurring in mechanical parts during working time; σ max ——σ i The maximum stress in; C — The power exponent is determined by the tensile strength of the material and the fatigue limit of the part; ② Remaining service life of parts Using the design life of the part as a reference value, and taking into account the actual stress cycle number 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 —Stress spectrum coefficients for part design; K S1 — Actual stress spectrum coefficients of the part; N G0 —Component design life; N Gz —The number of stress cycles a part has undergone, i.e., its service life.

6. 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 perform the method of any one of claims 1 to 5.

Images