Method and system for predicting remaining life of quay crane steel wire rope based on multi-dimensional attenuation coupling

Abstract

The present application relates to the technical field of port hoisting machinery safety monitoring and predictive maintenance, and discloses a shore-to-ship crane steel wire rope residual life prediction method and system based on multi-dimensional attenuation coupling. In view of the problems that the existing life judgment relies on fixed cycle, cumulative box quantity or single inspection, and it is difficult to take into account the force difference of mechanism, multi-source data sampling asynchronization and multi-type damage coupling accelerated degradation, the present application obtains steel wire rope basic attributes, mechanism type, historical operation, inspection damage, detection performance and historical scrap data; time alignment and dimensionless are carried out according to a unified prediction time, the operation equivalent attenuation, the inspection damage attenuation, the detection performance attenuation are obtained, and the damage coupling acceleration is generated when the two coexist, the comprehensive life retention rate and the residual equivalent life are calculated by inputting the multi-dimensional attenuation model, and the parameters are corrected by the historical scrap results. The scheme can represent operation consumption, apparent damage and performance degradation, and improve the reliability of life prediction and early warning.

Description

Technical Field

[0001] This invention relates to the field of port crane safety monitoring and predictive maintenance technology, and in particular to a method and system for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling. Background Technology

[0002] With the continuous improvement of port container transport scale and quay crane automation level, the operating frequency and continuous operation intensity of quay crane lifting mechanisms, pitching mechanisms, bracket mechanisms, and front and rear traction mechanisms are constantly increasing. As a key load-bearing component in the load transfer, traction, and motion control processes of quay cranes, the service condition of wire ropes directly affects the safety and reliability of quay crane operations. During long-term service, wire ropes are repeatedly subjected to combined loads such as tension, bending, compression, friction, and start-stop impacts. They may also experience degradation phenomena such as wire breakage, wear, corrosion, deformation, flattening, diameter reduction, and decreased mechanical properties due to factors such as lubrication conditions, rope groove fit, environmental corrosion, and abnormal operations. Different quay crane mechanisms have significantly different stress states, operating modes, and operating frequencies, and wire ropes of different specifications, structural forms, and strength grades also have different lifespan benchmarks. Therefore, replacing wire ropes based solely on a fixed service life or a single test result can easily lead to premature replacement or exceeding the service life, making it difficult to simultaneously meet the requirements of safe quay crane operation and rational allocation of maintenance resources. How to dynamically and differentially predict the remaining life of wire ropes by combining actual operating load, on-site inspection damage, and performance changes has become an important technical problem in condition-based maintenance of quay crane equipment.

[0003] In existing technologies, those skilled in the art typically assess the service status of wire ropes using methods such as periodic manual inspections, non-destructive testing, cumulative operation counts, or fatigue life calculations. Manual inspections primarily determine whether a wire rope has reached the end-of-life stage based on superficial characteristics such as the number of broken wires, wear, corrosion, deformation, and changes in rope diameter. Magnetic detection, visual inspection, or other non-destructive testing methods are mainly used to identify local defects in the wire rope and reflect its current damage state through detection signals or performance indicators. Life calculation methods based on operational data typically estimate the cumulative usage of the wire rope based on lifting loads, number of operations, number of bends, or operating time. Furthermore, some solutions utilize statistical models, fatigue analysis models, or data-driven models to predict the health status or remaining operating time of the wire rope based on historical inspection samples. While these technologies can reflect the operational consumption or damage status of wire ropes from different perspectives, the collection frequency, recording time, and units of operational data, inspection data, and testing data are usually different. Existing methods often process different types of data separately, lacking time alignment and attenuation quantification mechanisms based on a unified prediction time. Therefore, they cannot fully reflect the process of accumulation, attenuation, and interaction of various degradation factors over time.

[0004] For example, the Chinese invention patent CN109684784B, entitled "Method for Monitoring the Life of Crane Wire Ropes," discloses a method for calculating the service life and remaining life of crane wire ropes by classifying weight levels according to the weight of the hoisted goods, counting the number of hoisting operations corresponding to each weight level, and combining the load factor and the number of bending cycles of the wire rope. While this method can incorporate the actual hoisting load into the wire rope life calculation, its life assessment is primarily based on load level, number of hoisting operations, and preset bending life. It does not incorporate inspection damage information such as broken wires, wear, corrosion, and deformation, nor does it include detection performance information such as breaking strength, tensile strength, and diameter changes into a unified life model. Furthermore, it does not address the time correction of data collected at different times or the accelerated coupling relationship between inspection damage and abnormal detection performance. Therefore, it is difficult to adapt to the complex working conditions of multi-factor degradation of quay crane wire ropes.

[0005] Chinese invention patent CN115983838B, entitled "Method, Device, Equipment and Storage Medium for Health Assessment of Wire Rope in Hoisting Mechanism of Crane," discloses a technical solution for calculating the working static tension and rope diameter of the wire rope based on parameters such as maximum cargo weight and pulley transmission efficiency, selecting the wire rope model, and monitoring working parameters, conducting health assessments, and calculating the remaining operating time after the wire rope is put into use. This solution considers both wire rope selection and post-use health monitoring; however, it primarily focuses on the selection and health status assessment of the hoisting mechanism wire rope. It does not establish differentiated benchmark lifespans for different mechanisms such as hoisting, pitching, bracketing, and traction of quay cranes, nor does it establish a unified dimensionless attenuation expression for operational wear, inspection damage, and performance degradation. Therefore, it is difficult to fully describe the cumulative patterns and nonlinear effects of different types of degradation information over time.

[0006] Chinese invention patent application CN119804626A, entitled "A Method for Predicting the Remaining Life of Wire Ropes in a Four-Rope Grab Crane," discloses a technical solution that magnetizes the wire rope, collects the magnetic field change signal around the wire rope using a Hall sensor, cleans and extracts the signal, and establishes a magnetization prediction model to predict the remaining life of the wire rope. This solution primarily focuses on high-frequency life prediction using magnetic field detection signals. Its prediction results largely depend on sensor detection signals and sample models, failing to consider long-term operational history such as lifting capacity, number of runs, running time, load status, and start-stop frequency. It also does not integrate manual inspection damage records or various detection performance indicators. Therefore, when the data acquisition interval is long, sensor signals are affected by the on-site environment, or the wire rope damage has not yet formed significant magnetic field characteristics, this solution struggles to supplement the life status with other historical information, resulting in insufficient information utilization.

[0007] Furthermore, the Chinese invention patent CN111695214B, entitled "A Method for Determining Fatigue Damage of a Quay Crane Based on a Statistical Model," discloses a technical solution that statistically analyzes stress amplitude and mean stress according to the quay crane's operating conditions, and calculates fatigue damage of the quay crane structure and estimates its remaining service life based on stress-life curves. Although this solution targets quay crane equipment and uses a statistical model for fatigue life analysis, its evaluation object is mainly the metal structure of the quay crane. The stress cycle statistics and stress-life curves used cannot directly characterize the complex degradation process of wire ropes caused by wire breakage, wear, corrosion, flattening, and performance degradation, nor does it establish a life coupling link between wire rope operational consumption, inspection damage, and testing performance.

[0008] Therefore, existing wire rope life assessment methods typically focus on operational load statistics, single inspection judgments, or single-source detection signal analysis. These data are fragmented, making it difficult to uniformly map data from different time scales, dimensions, and reliability periods to comparable life degradation amounts. Furthermore, apparent damage and mechanical property degradation in wire ropes are not independent. When wire breakage, diameter reduction, or strength decline reaches an abnormal state, the degradation rate of the wire rope may accelerate nonlinearly. Existing technologies generally lack quantitative mechanisms for this type of damage coupling effect. In addition, the life benchmarks and model parameters used in existing schemes are mostly pre-set, making it difficult to differentiate and continuously correct based on different quay crane structures, wire rope properties, and actual replacement or scrapping results. This leads to insufficient model generalization ability and long-term predictive stability.

[0009] In summary, overcoming the problems existing in the prior art, such as inconsistent time references of multi-source heterogeneous data, insufficient utilization of operational consumption and damage performance information, difficulty in quantifying the coupling acceleration effect between different degradation factors, and lack of differentiated correction of life references and model parameters, and proposing a more accurate, stable, and adaptable method for predicting the remaining life of wire ropes under complex quay crane working conditions, has become a technical challenge that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0010] To address the challenges of unifying and integrating multi-source heterogeneous data, quantifying the coupling relationships between different degradation factors, and insufficient accuracy in life prediction methods for quay crane wire ropes, this paper proposes a multi-dimensional time-series decay coupling model. This model is constructed by performing time alignment and dimensionless processing on historical operational data, inspection damage data, and testing performance data. The model is then combined with historical replacement or scrapping data for parameter correction, thus resolving the issue of inaccurate and dynamic prediction of the remaining life of quay crane wire ropes.

[0011] This invention provides a method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling, specifically including the following steps: S1: Obtain the basic attribute data of the target wire rope, the type of quay crane to which it belongs, historical operation data, inspection damage data, testing performance data, and historical replacement or scrapping data; S2: Based on the current predicted time To standardize prediction timing, historical operation data, inspection damage data, and detection performance data are time-aligned, and all types of data are converted into dimensionless decay indices. S3: Determine the differentiated baseline life of the target wire rope based on basic attribute data and the type of quay crane organization to which it belongs. ; S4: Calculate the equivalent job attenuation based on the historical job data processed in step S2. ; S5: Calculate the inspection damage attenuation based on the inspection damage data processed in step S2. ; S6: Calculate the detection performance degradation based on the detection performance data processed in step S2. ; S7: Inspection damage attenuation amount obtained from step S5 The detection performance degradation obtained in step S6 And anomaly threshold, calculate damage coupling acceleration. ; S8: The results obtained from steps S4 to S7 , , and Inputting a multidimensional time-series decay coupling model yields the overall lifetime retention rate. ; S9: Differentiated baseline lifetime obtained from step S3 The overall lifetime retention rate obtained in step S8 Determine the target wire rope at the current predicted time. Remaining equivalent lifetime ; S10: Based on the actual remaining equivalent life in historical replacement or scrapping data and the remaining equivalent life obtained in step S9. The deviation between the parameters is used to correct the parameters of the multidimensional time-series decay coupling model, and steps S4 to S9 are re-executed based on the corrected parameters.

[0012] As a preferred embodiment of the present invention, in step S2, time alignment includes: accumulating or statistically analyzing historical operation data according to a preset time window to obtain the current prediction time. Corresponding operational statistics; for inspection damage data and detection performance data, based on their collection time and the current prediction time. The time interval between them determines the corresponding time correction factor.

[0013] As a preferred embodiment of the present invention, in step S3, the differentiated benchmark lifetime... The quay crane mechanism is determined based on at least two of the following: quay crane mechanism type, wire rope diameter, strength grade, structural form, and historical average replacement cycle. The quay crane mechanism type includes at least one of the following: hoisting mechanism, pitching mechanism, bracket mechanism, front traction mechanism, and rear traction mechanism.

[0014] As a preferred embodiment of the present invention, in step S4, the equivalent attenuation amount of the operation Calculate as follows: ; in, The first one after transformation in step S2 The operation indicators are dimensionless and include at least two of the following: natural container quantity, standard container quantity, number of runs, running time, load status, and start / stop frequency. Type of quay crane Next The weight of the task impact on the category of task indicators. Type of quay crane The corresponding nonlinear decay exponent.

[0015] As a preferred embodiment of the present invention, in step S5, the damage attenuation amount is inspected. Calculate as follows: ; in, For the first The inspection damage indicators include at least two of the following: number of broken wires, degree of wear, degree of corrosion, degree of deformation, degree of flattening, and skipping records; For the corresponding damage level function, For the corresponding damage weight, This is the time correction factor determined in step S2.

[0016] As a preferred embodiment of the present invention, in step S6, the performance degradation is detected. Calculate as follows: ; in, For the first Performance degradation weights for class detection performance metrics The time correction factor determined in step S2, For the first The percentage decrease in the performance of various detection performance indicators; When the detection performance index is a numerical index ,in For standard or initial values, This is the current detection value; When the performance indicator is a graded indicator Determined by a preset level quantification table.

[0017] As a preferred embodiment of the present invention, in step S7, the damage coupling acceleration amount Calculate as follows: ; in, For coupling weights, This is to amplify the weights abnormally. It is an abnormal state triggering factor; When at least one of the following conditions exceeds the corresponding abnormal threshold: number of broken wires, diameter reduction rate, breaking tensile strength decrease rate, or tensile strength decrease rate, ,otherwise .

[0018] A system for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling includes: The data acquisition module is used to acquire basic attribute data of the target wire rope, the type of quay crane to which it belongs, historical operation data, inspection damage data, testing performance data, and historical replacement or scrapping data. The time alignment module is used to align the current prediction time. To standardize prediction timing, historical operation data, inspection damage data, and detection performance data are time-aligned, and all types of data are converted into dimensionless decay indices. The baseline life determination module is used to determine the differentiated baseline life of the target wire rope based on basic attribute data and the type of quay crane mechanism to which it belongs. ; The attenuation calculation module is used to calculate the equivalent attenuation of operations based on historical operation data, inspection damage data, and detection performance data processed by the time alignment module. Inspection damage attenuation and detection performance degradation ; The coupled acceleration calculation module is used to calculate the damage attenuation during inspection. Detection performance degradation And anomaly threshold, calculate damage coupling acceleration. ; The lifespan prediction module is used to calculate the equivalent degradation amount of the operation. Inspection damage attenuation Detection performance degradation Damage coupling acceleration Input the multi-dimensional decay model to obtain the overall lifetime retention rate. And based on differentiated benchmark lifetime and overall life retention rate Determine the remaining equivalent lifetime ; The parameter correction module is used to compare the actual remaining equivalent life in historical replacement or scrapping data with the remaining equivalent life obtained by the life prediction module. The deviation between them is used to correct the parameters of the multidimensional decay model.

[0019] As a preferred embodiment of the present invention, the parameters corrected by the parameter correction module include the job impact weight. Nonlinear decay index Damage weight Performance degradation weight Coupling weights Abnormally amplified weights and model weight parameters , , , At least one of the following; the lifetime prediction module redetermines the overall lifetime retention rate based on the corrected parameters. and remaining equivalent lifetime .

[0020] Compared with the relevant prior art, the beneficial effects of the present invention are: 1. This invention acquires basic attribute data of the target wire rope, quay crane mechanism type, historical operation data, inspection damage data, detection performance data, and historical replacement or scrapping data. It also performs time alignment and dimensionless processing on data from different sources and collection periods at a unified prediction time. This enables the fusion of multi-source heterogeneous data under the same life evaluation system, which can reduce prediction deviations caused by inconsistent data dimensions and different collection times.

[0021] 2. This invention determines differentiated baseline life based on the type of quay crane mechanism and the inherent properties of the wire rope, enabling the life prediction process to consider the differences in load characteristics, operating frequency, and stress mode of different mechanisms such as hoisting mechanism, pitching mechanism, bracket mechanism, and traction mechanism. This avoids the distortion of evaluation results caused by using a uniform fixed life benchmark and improves the adaptability of remaining life prediction to different quay crane mechanisms and wire ropes of different specifications.

[0022] 3. This invention constructs operational equivalent attenuation, inspection damage attenuation, and detection performance attenuation, respectively, which can comprehensively characterize the degradation state of wire ropes in multiple dimensions, including actual operational consumption, apparent damage, and performance decline. Specifically, by weighting and nonlinearly processing operational indicators such as number of runs, running time, load state, and start-stop frequency, the impact of different operational intensities on wire rope lifespan can be more accurately reflected. By introducing a damage level function and a time correction factor, the severity of inspection damage and the timeliness of inspection results can be considered. By calculating the decline rate of detection performance indicators, changes in the mechanical properties or condition performance of the wire rope can be quantified as lifespan attenuation factors.

[0023] 4. This invention further calculates the damage coupling acceleration based on the inspection damage attenuation, detection performance attenuation, and anomaly threshold. This allows the model to not only consider the independent effects of various degradation factors but also reflect the accelerated degradation effect that occurs when apparent damage and performance degradation occur simultaneously. When the number of broken wires, diameter reduction rate, breaking tensile strength reduction rate, or tensile strength reduction rate exceed the corresponding anomaly threshold, the life attenuation is amplified through an anomaly state triggering factor, thereby improving the ability to identify abnormal degradation states and sudden risks in wire ropes.

[0024] 5. This invention inputs the equivalent attenuation of operation, the attenuation of inspection damage, the attenuation of detection performance, and the damage coupling acceleration into a multidimensional time-series attenuation coupling model, determines the comprehensive life retention rate in the form of exponential attenuation, and calculates the remaining equivalent life in combination with the differentiated benchmark life. It can establish a unified quantitative relationship between the operation process, damage state, and detection performance and the remaining life, so that the prediction results can continuously reflect the trend of wire rope life changing with service time and damage degree, and improve the completeness and stability of remaining life prediction.

[0025] 6. The present invention can also use the actual remaining equivalent life in historical replacement or scrapping data to correct the model parameters, so that the operation impact weight, damage weight, performance degradation weight, coupling weight and model weight parameters can be continuously corrected according to the actual operation results of the quay crane, thereby reducing the deviation between the preset parameters and the actual working conditions and improving the generalization ability of the model in different equipment, different mechanisms and different working environments.

[0026] 7. This invention can determine the risk level of wire rope based on the remaining equivalent life and the damage coupling acceleration, and output replacement suggestions or early warning information when the remaining life is lower than the life threshold or the damage coupling acceleration is higher than the acceleration threshold. This provides a quantitative basis for condition-based maintenance, maintenance plan formulation and replacement timing judgment of wire rope, which helps to reduce the risk of wire rope exceeding its service life and reduce the waste of resources caused by replacing it only according to a fixed cycle. Attached Figure Description

[0027] Figure 1The overall flowchart of the method for predicting the remaining life of quay crane wire rope based on multidimensional attenuation coupling provided in the embodiments of the present invention; Figure 2 A system module structure diagram of the quay crane wire rope remaining life prediction system based on multidimensional attenuation coupling provided in an embodiment of the present invention; Figure 3 This is a multi-source asynchronous data time alignment and attenuation fusion diagram provided in an embodiment of the present invention; Figure 4 Error comparison chart of simulation experiments provided for embodiments of the present invention. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments. However, the present invention can be implemented in many different ways and should not be construed as limited to the embodiments shown; rather, these embodiments provide those skilled in the art with implementation methods that meet applicable legal requirements.

[0029] Example 1: As Figure 1 As shown, this embodiment provides a method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling. This embodiment takes wire rope A-01 of a quay crane hoisting mechanism as the target wire rope, and the current prediction time... The remaining equivalent life is measured at the end of the 20th month after the steel wire rope is installed and put into use, with "remaining standard box quantity" as the unit of measurement.

[0030] S1, Obtain target wire rope data. The basic attribute data of target wire rope A-01 are as follows: the mechanism it belongs to is a hoisting mechanism, the nominal diameter is 34mm, the strength grade is 1770MPa, the structural form is 6×K36WS+IWR, and the benchmark life corresponding to the historical average replacement cycle of the same type of wire rope is 1,200,000 standard boxes.

[0031] The acquired historical operational data includes: a cumulative standard container volume of 820,000 standard containers, a cumulative runtime of 7,300 hours, a cumulative number of runs of 43,200, and an average load factor of 0.75. The acquired inspection damage data includes: 7 broken wires, wear level 2, corrosion level 1, and slight deformation. The acquired performance data includes: current measured diameter of 32.9 mm, current breaking tensile force of 625 kN, current tensile strength of 1625 MPa, torsional performance retention rate of 82%, and bending performance retention rate of 90%.

[0032] S2: Perform time alignment and dimensionless processing on multi-source data. Using the current prediction time... To standardize prediction times, historical operation data is organized from the installation time to the current prediction time. The cumulative window is used for statistics; the inspection damage data and detection performance data are based on the collection time and the current prediction time. The time interval between them determines the time correction factor.

[0033] In this embodiment, the dimensionless representation of the task data is as follows:

[0034] For the inspection damage data, the damage level quantification table yielded quantified values ​​of 0.42, 0.35, 0.20, and 0.10 for the number of broken wires, wear level, corrosion level, and deformation level, respectively. The time correction factor for the inspection data was calculated using the following formula: ; in, The time interval from the inspection time to the current predicted time. This represents the time decay coefficient. In this embodiment, the corresponding time correction factors are 0.94, 0.94, 0.87, and 0.94.

[0035] For the detection performance data, the percentage of performance degradation is calculated using the following formula: ; in, For standard or initial values, This is the current detection value.

[0036] The calculations show that the diameter decreased by 3.24%, the breaking strength decreased by 11.97%, the tensile strength decreased by 8.19%, the torsional properties decreased by 18%, and the bending properties decreased by 10%. The time correction factor for the performance data was set to 0.90.

[0037] S3: Determine the differentiated baseline lifetime Based on the type of mechanism to which the target wire rope belongs, its nominal diameter, strength grade, structural form, and the historical average replacement cycle of similar wire ropes, determine the differentiated benchmark life of the wire rope for this hoisting mechanism: .

[0038] S4: Calculate the equivalent attenuation of the task. In this embodiment, the operational impact weight corresponding to the hoisting mechanism is: ; Calculate according to the following formula: ; in, These are the dimensionless operational indicators corresponding to the cumulative standard container quantity, cumulative runtime, cumulative number of runs, and average load factor, respectively. Substituting the data, we get: .

[0039] S5: Calculate the damage attenuation during inspection In this embodiment, the inspection damage weight is: Calculate according to the following formula: ; in, These are the damage quantification values ​​corresponding to wire breakage, wear, corrosion, and deformation. This is the corresponding time correction factor. Substituting the data, we get: .

[0040] S6: Calculate the performance degradation of the detection. In this embodiment, the detection performance degradation weight is: Calculate according to the following formula: ; in, These represent the percentage decrease in diameter, breaking strength, tensile strength, torsional properties, and bending properties, respectively. This is the time correction factor corresponding to the performance data. Substituting the data, we get: .

[0041] S7: Calculate the damage coupling acceleration In this embodiment, the abnormal thresholds are set as follows: the threshold for the number of broken wires is 6, the threshold for the diameter reduction rate is 3%, the threshold for the decrease in breaking tensile strength is 10%, and the threshold for the decrease in tensile strength is 8%.

[0042] Since the target wire rope has 7 broken wires, a diameter reduction rate of 3.24%, a breaking tensile strength reduction rate of 11.97%, and a tensile strength reduction rate of 8.19%, all of which meet or exceed the corresponding abnormal thresholds, the abnormal state trigger factor is set to: The damage coupling acceleration is calculated according to the following formula: ; in, , After substituting the data, we get: .

[0043] S8: Calculate overall lifetime retention rate In this embodiment, the model weight parameters are set to: ; ; ; ; The overall life retention rate is calculated using the following formula: ; Substituting the above data, we get: .

[0044] S9: Determine the remaining equivalent lifetime The target wire rope at the current predicted time is calculated using the following formula. Remaining equivalent lifetime: ; Substitution Standard container ,get: .

[0045] Therefore, at the current prediction time The predicted remaining equivalent life of target wire rope A-01 is approximately 417,208 standard boxes.

[0046] The risk level can also be determined based on the remaining equivalent lifetime and the amount of damage coupling acceleration. For example, when Less than 40% of the baseline lifespan, or A value higher than 0.12 is classified as a Level 2 warning. In this embodiment, ,and Therefore, a level-two warning is issued, and suggestions are generated to shorten the inspection cycle or arrange planned replacements.

[0047] S10: Correct the model parameters based on historical replacement or scrapping data. When the target wire rope or a similar wire rope is subsequently replaced or scrapped, obtain its actual remaining equivalent life and compare it with the predicted remaining equivalent life obtained in step S9.

[0048] For example, if the actual remaining equivalent life of target wire rope A-01 is 410,000 standard boxes, then the prediction error is: Error = .

[0049] Based on this error, the model weight parameters are recursively corrected. This can be done in the following way: ; in, These are the corrected model weight parameters. To learn step length, To predict the normalized error between the remaining equivalent lifetime and the actual remaining equivalent lifetime, This corresponds to the attenuation amount. The corrected parameters are used for predicting the lifespan of steel wire ropes for similar quay cranes in the future.

[0050] Through the above steps, this embodiment achieves a comprehensive evaluation of the consumption of steel wire ropes during operation, inspection damage, decline in detection performance, and accelerated abnormal coupling of the quay bridge, and can perform closed-loop correction of model parameters based on historical replacement or scrapping results.

[0051] Example 2: Figure 2 and Figure 3As shown, this embodiment provides a system for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling. The system includes an external data interface layer, a data storage layer, a model calculation layer, a result output layer, and a parameter feedback layer. Data is linked between these layers using the unique wire rope number, quay crane number, and mechanism type.

[0052] The external data interface layer connects the quay crane control system, the terminal production operation system, the inspection and maintenance system, the inspection report entry system, and the equipment maintenance management system. The quay crane control system provides this system with the number of operations, runtime, start / stop frequency, and load status; the terminal production operation system provides natural container volume and standard container volume; the inspection and maintenance system provides records of wire breakage, wear, corrosion, deformation, flattening, and skipping; the inspection report entry system provides data on diameter, breaking strength, tensile strength, torsional performance, and bending performance; and the equipment maintenance management system provides replacement, scrapping, and repair records.

[0053] The data storage layer includes a wire rope basic property database, an operational database, an inspection damage database, a testing performance database, a historical replacement or scrap database, and a model parameter database. The wire rope basic property database uses the wire rope number as the primary index and records information such as the associated quay crane, the affiliated organization, the installation location, specifications, installation time, and baseline life. The operational database, inspection damage database, testing performance database, and historical replacement or scrap database are all linked to the basic property database through the wire rope number, enabling data from different sources to converge on the same target wire rope.

[0054] The model calculation layer includes a data acquisition module, a time alignment module, a baseline lifetime determination module, a decay calculation module, a coupled accelerated calculation module, and a lifetime prediction module. The data acquisition module receives the target wire rope number selected by the user, reads the relevant data from various databases, and sends the results to the time alignment module and the baseline lifetime determination module.

[0055] After receiving operation data, inspection data, and detection data, the time alignment module processes them based on the current prediction time. For continuous or high-frequency operation data, the time alignment module generates cumulative operation indicators according to a preset statistical window. For periodic inspection and detection data, the time alignment module determines their validity based on the data acquisition time and outputs standardized data for subsequent calculations. The output of the time alignment module is sent to the operation attenuation submodule, inspection damage attenuation submodule, and detection performance attenuation submodule within the attenuation calculation module.

[0056] The baseline life determination module receives the basic attribute data and mechanism type of the target wire rope, reads the corresponding baseline life configuration from the model parameter library, and determines the differentiated baseline life of the target wire rope by combining it with the historical average replacement cycle. This differentiated baseline life is then sent to the life prediction module.

[0057] The attenuation calculation module comprises three sub-modules. The operational attenuation sub-module outputs operational attenuation results based on standard box quantity, runtime, number of runs, load status, and start / stop frequency. The inspection damage attenuation sub-module outputs inspection damage attenuation results based on records of broken wires, wear, corrosion, deformation, flattening, and skipping. The inspection performance attenuation sub-module outputs inspection performance attenuation results based on diameter, breaking tensile force, tensile strength, torsional properties, and bending properties. The outputs of all three sub-modules are sent to the life prediction module, with the inspection damage attenuation results and inspection performance attenuation results also simultaneously sent to the coupled accelerated calculation module.

[0058] The coupling acceleration calculation module receives the inspection damage attenuation results, the detection performance attenuation results, and the abnormal threshold configuration. When the number of broken wires, the diameter reduction rate, the breaking tensile force reduction rate, or the tensile strength reduction rate exceed the corresponding threshold, or when the inspection damage and the detection performance degradation reach a high level simultaneously, the coupling acceleration calculation module generates the damage coupling acceleration result and sends the result to the life prediction module and the risk warning module.

[0059] The life prediction module receives differentiated baseline life, operational attenuation results, inspection damage attenuation results, detection performance attenuation results, and damage coupling acceleration results, and generates the overall life retention rate and remaining equivalent life of the target wire rope. The life prediction module writes the prediction results into the prediction result database and sends them to the risk warning module.

[0060] The output layer includes a risk warning module and a display module. The risk warning module determines the risk level based on the remaining equivalent life, damage coupling acceleration results, and preset risk thresholds. Risk levels include normal, attention level, warning level, and severe warning level. The display module shows the predicted results according to the quay crane number, mechanism type, and wire rope number, and outputs replacement suggestions, re-inspection suggestions, or suggestions to shorten the inspection cycle. Warning results can also be sent to the equipment maintenance management system to generate maintenance work orders or planned replacement tasks.

[0061] The parameter feedback layer includes a parameter correction module and a model parameter library. The parameter correction module is activated upon actual replacement, scrapping, or manual confirmation of life deviation. It reads the actual results from the historical replacement or scrapping database, and simultaneously reads the predicted results for the corresponding wire rope before replacement or scrapping from the prediction result library, comparing the deviation between the predicted and actual results. If the deviation exceeds a preset allowable range, the parameter correction module corrects the parameters for the corresponding mechanism type, wire rope specifications, and operating conditions, and writes the corrected parameters into the model parameter library. When subsequent prediction tasks are initiated, the baseline life determination module, attenuation calculation module, coupled acceleration calculation module, and life prediction module all call the updated parameters.

[0062] In this embodiment, the system data flow path is as follows: the external data source first enters the data storage layer through the external data interface layer; the data acquisition module reads data from the data storage layer according to the wire rope number; the time alignment module performs unified processing on the multi-source data; the attenuation calculation module and the coupled acceleration calculation module generate the current attenuation status; the lifetime prediction module outputs the remaining equivalent lifetime; the risk warning module outputs warning information and maintenance suggestions; the parameter correction module updates the model parameters according to the actual replacement or scrapping results; the updated model parameters are returned to the model parameter library and are called back in subsequent predictions. This forms a closed-loop system from data acquisition, lifetime prediction, risk warning to parameter correction.

[0063] Example 3: As Figure 4 As shown, this embodiment is used to verify the effectiveness of the methods and systems described in Embodiments 1 and 2 in predicting the remaining life of quay crane wire ropes. This embodiment uses a historical sample playback method for the experiment, that is, selecting quay crane wire ropes that have been replaced or scrapped as experimental objects, and calling up the available operational data, inspection damage data, and detection performance data at a certain historical prediction point before their replacement or scrapping, calculating the predicted remaining equivalent life, and comparing it with the subsequent actual remaining equivalent life.

[0064] In this embodiment, the remaining equivalent life is represented by the remaining standard container quantity. The experimental sample includes 12 quay crane wire ropes, respectively from the hoisting mechanism, pitching mechanism, front traction mechanism, and rear traction mechanism. Each sample includes basic attribute data, historical operation data, inspection damage data, testing performance data, and actual replacement or scrapping data.

[0065] The experiment was designed with three comparison methods: The first method is the traditional container quantity method, which estimates the remaining life based solely on the differentiated baseline life and the current cumulative standard container quantity. The second method is the uncoupled multidimensional attenuation method, which uses operational data, inspection damage data and detection performance data for comprehensive evaluation, but does not introduce time correction and damage coupling acceleration. The third method is the method of this invention, which simultaneously employs multi-source data time alignment, operation attenuation, inspection damage attenuation, detection performance attenuation, damage coupling acceleration, and historical sample parameter correction.

[0066] The following evaluation metrics are used in this embodiment: Absolute error = |Predicted remaining equivalent lifetime - Actual remaining equivalent lifetime|; Relative error = Absolute error / Actual remaining equivalent lifetime × 100%; Mean Absolute Error (MAE) = The average of the absolute errors of each sample; Mean relative error (MAPE) = the average of the relative errors of each sample; Root Mean Square Error (RMSE) = the square root of the mean squared errors of each sample. exist Figure 4 In region (a), the simulation input data is shown using sample A-01 as an example. Sample A-01 is a hoisting mechanism wire rope with a nominal diameter of 34mm, a strength grade of 1770MPa, a structural form of 6×K36WS+IWR, and a differentiated baseline life of 1,200,000 standard boxes. At the prediction time, the cumulative number of standard boxes for sample A-01 is 820,000 standard boxes, the cumulative running time is 7,300h, the cumulative number of runs is 43,200, and the average load factor is 0.75. The inspection record shows 7 broken wires, wear level 2, corrosion level 1, and slight deformation. The test record shows a measured diameter of 32.9mm, a breaking tensile force of 625kN, a tensile strength of 1625MPa, a torsional performance retention rate of 82%, and a bending performance retention rate of 90%.

[0067] exist Figure 4 In region (b), the multidimensional attenuation calculation results for sample A-01 are displayed. After time alignment and dimensionless processing, the equivalent attenuation of the operation is Eop(t) = 0.647, the inspection damage attenuation is Eins(t) = 0.289, and the detection performance attenuation is Etest(t) = 0.085. Since the number of broken wires, diameter reduction rate, breaking tensile force reduction rate, and tensile strength reduction rate of sample A-01 all reach the corresponding abnormal thresholds, the abnormal state triggering factor is set to 1, resulting in damage coupling acceleration Ecouple(t) = 0.141.

[0068] exist Figure 4 In region (c), the lifetime prediction results for sample A-01 are displayed. After inputting the above multidimensional decay results into the multidimensional decay model, the overall lifetime retention rate S(t) = 0.348 is obtained, and the predicted remaining equivalent lifetime RUL(t) = 417208 standard boxes is obtained. The subsequent actual remaining equivalent lifetime of sample A-01 is 410000 standard boxes, therefore the relative error of the method of this invention for sample A-01 is 1.76%.

[0069] exist Figure 4 The results of the multi-sample comparison experiment are shown in region (d). The experimental data for 12 samples are shown in Table 1 below.

[0070] Table 1: Experimental data for 12 samples

[0071] Based on the experimental samples above, the overall error statistics of the three methods are shown in Table 2 below.

[0072] Table 2: Error Results of the Three Methods

[0073] As can be seen from the results in Table 2 above, the traditional box-counting method only reflects the cumulative consumption during operation and cannot reflect damage and performance degradation factors such as wire breakage, wear, diameter reduction, and decreased breaking tensile strength. Therefore, the error is relatively large in some abnormally degraded samples. Although the uncoupled multidimensional attenuation method introduces inspection damage and detection performance data, its prediction error is still significantly higher than that of the method of this invention because it does not consider the differences in data acquisition time and the accelerated degradation effect when multiple types of damage occur simultaneously.

[0074] This invention's method fuses operational data, inspection damage data, and performance data at a unified prediction time. It also introduces accelerated damage coupling when inspection damage and performance degradation occur simultaneously, enabling the model to identify situations where the wire rope transitions from a normal attenuation state to an accelerated degradation state. Experimental results show that the method's average relative error across 12 samples is 1.90%, significantly lower than both the traditional box-counting method and the uncoupled multidimensional attenuation method.

[0075] After the experiment, the system writes the predicted results of each sample and the actual replacement or scrapping results into the historical replacement or scrapping database, and the parameter correction module updates the model parameters for the same institution, specifications, and operating conditions. The updated parameters are written into the model parameter library for subsequent life prediction tasks of quay crane wire ropes. Thus, this embodiment not only verifies the prediction effect of the multi-dimensional attenuation model, but also verifies that the system can form a closed-loop optimization process from historical samples and prediction output to parameter updates.

[0076] The above embodiments merely illustrate implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling, characterized in that: Includes the following steps: S1: Obtain the basic attribute data of the target wire rope, the type of quay crane to which it belongs, historical operation data, inspection damage data, testing performance data, and historical replacement or scrapping data; S2: Based on the current predicted time To standardize prediction timing, historical operation data, inspection damage data, and detection performance data are time-aligned, and all types of data are converted into dimensionless decay indices. S3: Determine the differentiated baseline life of the target wire rope based on basic attribute data and the type of quay crane organization to which it belongs. ; S4: Calculate the equivalent job attenuation based on the historical job data processed in step S2. ; S5: Calculate the inspection damage attenuation based on the inspection damage data processed in step S2. ; S6: Calculate the detection performance degradation based on the detection performance data processed in step S2. ; S7: Inspection damage attenuation amount obtained from step S5 The detection performance degradation obtained in step S6 And anomaly threshold, calculate damage coupling acceleration. ; S8: The results obtained from steps S4 to S7 , , and Inputting a multidimensional time-series decay coupling model yields the overall lifetime retention rate. ; S9: Differentiated baseline lifetime obtained from step S3 The overall lifetime retention rate obtained in step S8 Determine the target wire rope at the current predicted time. Remaining equivalent lifetime ; S10: Based on the actual remaining equivalent life in historical replacement or scrapping data and the remaining equivalent life obtained in step S9. The deviation between the parameters is used to correct the parameters of the multidimensional time-series decay coupling model, and steps S4 to S9 are re-executed based on the corrected parameters.

2. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S2, time alignment includes: accumulating or statistically analyzing historical job data according to a preset time window to obtain the current prediction time. Corresponding operational statistics; for inspection damage data and detection performance data, based on their collection time and the current prediction time. The time interval between them determines the corresponding time correction factor.

3. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S3, the differentiated benchmark lifetime The quay crane mechanism is determined based on at least two of the following: quay crane mechanism type, wire rope diameter, strength grade, structural form, and historical average replacement cycle. The quay crane mechanism type includes at least one of the following: hoisting mechanism, pitching mechanism, bracket mechanism, front traction mechanism, and rear traction mechanism.

4. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S4, the equivalent attenuation of the operation. Calculate as follows: ； in, The first one after transformation in step S2 The operation indicators are dimensionless and include at least two of the following: natural container quantity, standard container quantity, number of runs, running time, load status, and start / stop frequency. Type of quay crane Next The weight of the task impact on the category of task indicators. Type of quay crane The corresponding nonlinear decay exponent.

5. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S5, the damage attenuation amount is inspected. Calculate as follows: ； in, For the first The inspection damage indicators include at least two of the following: number of broken wires, degree of wear, degree of corrosion, degree of deformation, degree of flattening, and skipping records; For the corresponding damage level function, For the corresponding damage weight, This is the time correction factor determined in step S2.

6. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S6, the amount of performance degradation is detected. Calculate as follows: ； in, For the first Performance degradation weights for class detection performance metrics The time correction factor determined in step S2, For the first The percentage decrease in the performance of various detection performance indicators; When the detection performance index is a numerical index ,in For standard or initial values, This is the current detection value; When the performance indicator is a graded indicator Determined by a preset level quantification table.

7. The method for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling according to claim 1, characterized in that: In step S7, the damage coupling acceleration amount Calculate as follows: ； in, For coupling weights, This is to amplify the weights abnormally. It is an abnormal state triggering factor; When at least one of the following conditions exceeds the corresponding abnormal threshold: number of broken wires, diameter reduction rate, breaking tensile strength decrease rate, or tensile strength decrease rate, ,otherwise .

8. A system for predicting the remaining life of quay crane wire ropes based on multidimensional attenuation coupling, characterized in that: include: The data acquisition module is used to acquire basic attribute data of the target wire rope, the type of quay crane to which it belongs, historical operation data, inspection damage data, testing performance data, and historical replacement or scrapping data. The time alignment module is used to align the current prediction time. To standardize prediction timing, historical operation data, inspection damage data, and detection performance data are time-aligned, and all types of data are converted into dimensionless decay indices. The baseline life determination module is used to determine the differentiated baseline life of the target wire rope based on basic attribute data and the type of quay crane mechanism to which it belongs. ; The attenuation calculation module is used to calculate the equivalent attenuation of operations based on historical operation data, inspection damage data, and detection performance data processed by the time alignment module. Inspection damage attenuation and detection performance degradation ; The coupled acceleration calculation module is used to calculate the damage attenuation during inspection. Detection performance degradation And anomaly threshold, calculate damage coupling acceleration. ; The lifespan prediction module is used to calculate the equivalent degradation amount of the operation. Inspection damage attenuation Detection performance degradation Damage coupling acceleration Input the multi-dimensional decay model to obtain the overall lifetime retention rate. And based on differentiated benchmark lifetime and overall life retention rate Determine the remaining equivalent lifetime ; The parameter correction module is used to compare the actual remaining equivalent life in historical replacement or scrapping data with the remaining equivalent life obtained by the life prediction module. The deviation between them is used to correct the parameters of the multidimensional decay model.

9. The system for predicting the remaining life of quay crane wire ropes according to claim 8, characterized in that: The parameters calibrated by the parameter calibration module include the job impact weight. Nonlinear decay index Damage weight Performance degradation weight Coupling weights Abnormally amplified weights and model weight parameters , , , At least one of the following; the lifetime prediction module redetermines the overall lifetime retention rate based on the corrected parameters. and remaining equivalent lifetime .

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