Method and system for predicting fatigue life of crane structure

Abstract

The invention relates to the technical field of port cranes and mechanical engineering, in particular to a crane structure fatigue life prediction method and system. The invention provides a crane structure fatigue life prediction method. The crane structure fatigue life prediction method comprises the steps that a finite element model of a crane structure is established, and a stress-load mapping relation matrix of crane structure nodes is generated through dimension reduction processing; collecting actual working data of the crane structure; performing data processing on the actual working data, and constructing an actual working load spectrum; based on the stress-load mapping relation matrix and the actual operation load spectrum, generating a stress-time history used for calculating the fatigue life structure node of the crane; and calculating the fatigue damage of the crane structure and predicting the residual fatigue life by combining a stress-life curve and an accumulative damage theory of a crane structure material. According to the method provided by the invention, online, real-time and accurate prediction and visualization of the fatigue life of all structures of the crane are realized, and a basis is provided for formulating an accurate operation and maintenance plan.

Description

Technical Field

[0001] This invention relates to the field of port cranes and mechanical engineering technology, and more specifically, to a method and system for predicting the fatigue life of crane structures. Background Technology

[0002] Port cranes are the core loading and unloading equipment in modern container ports, belonging to large, complex, and expensive special equipment. Throughout their entire life cycle, they are subjected to high-intensity, repetitive operations, with large load fluctuations and harsh working environments. As the main load-bearing components, the welded steel structures are inevitably prone to fatigue cracking, seriously threatening the safety of equipment operation.

[0003] To ensure safety and reliability, fatigue calculations are typically performed during the structural design phase of port machinery based on material probabilistic statistical models using the damage tolerance design method. Equipment health management relies on periodic maintenance at fixed intervals to ensure the reliability of this probabilistic model. However, such maintenance requires downtime and the detection of weld fatigue cracks through visual inspection and magnetic particle testing. The fixed maintenance intervals preset during the design phase are inherently arbitrary and outdated, affecting both operational efficiency and economic viability.

[0004] Therefore, for cranes in service, fatigue life assessment and predictive maintenance through data acquisition methods such as condition monitoring are crucial for assisting managers in scientifically formulating maintenance plans. However, conventional fatigue life prediction methods based on condition monitoring have significant limitations. For example, this method requires the deployment of strain gauges, sensors, and other monitoring devices at a limited number of theoretically highest stress "hot spots" to calculate cumulative fatigue damage by monitoring structural stress changes under typical working conditions. Although it has high accuracy in monitoring local key locations, it is difficult to cover the complex structural system of the entire machine. At the same time, on-site data acquisition is time-consuming and costly, difficult to implement in harsh working environments, and sensors are prone to drift and damage.

[0005] In summary, there is an urgent need for a more efficient and reliable technical solution to address the aforementioned challenges in fatigue life assessment and predictive maintenance of port cranes. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for predicting the fatigue life of crane structures, which solves the problems of incomplete monitoring, high cost, and poor timeliness caused by the reliance on limited measurement points in traditional fatigue life prediction methods.

[0007] To achieve the above objectives, the present invention provides a method for predicting the fatigue life of a crane structure, comprising the following steps:

[0008] A finite element model of the crane structure is established, and the stress-load mapping relationship matrix of the crane structure nodes is generated by dimensionality reduction processing based on the finite element model.

[0009] Collect actual working data of the crane structure, including at least the trolley position, the lifting position of the spreader, the lifting load, the spreader load, the spreader opening and closing signals, and the running speed;

[0010] The actual work data is processed and the work cycle is identified to construct the actual work load spectrum;

[0011] Based on the stress-load mapping matrix and the actual operating load spectrum, a stress-time history of the structural nodes used to calculate the fatigue life of the crane is generated.

[0012] Based on the stress-time history, combined with the stress-life curve of the crane structure material and the cumulative damage theory, the fatigue damage of the crane structure is calculated and the remaining fatigue life is predicted.

[0013] In one embodiment, generating the stress-load mapping matrix of the crane structure nodes based on the finite element model further includes:

[0014] The stress response of the crane structural nodes under a unit moving load is calculated by using the unit load method for dimensionality reduction. A stress-load mapping matrix of the crane structural nodes is constructed to show the coefficient relationship between the overall machine stress and the working load.

[0015] In one embodiment, the data processing and job cycle identification technology process for the actual work data, and the construction of the actual work load spectrum, further includes:

[0016] The actual working data is preprocessed to construct a spatiotemporal synchronization dataset;

[0017] Based on the spatiotemporal synchronization dataset, the normal operating cycle and operating range of the crane structure are identified;

[0018] Feature extraction is performed on the load data during the normal operating cycle of the crane structure to construct the actual operating load spectrum.

[0019] In one embodiment, the preprocessing of the actual working data to construct a spatiotemporal synchronization dataset further includes:

[0020] The actual working data is cleaned, timestamp aligned, and multi-source data fused to generate consistent time-series data and construct a spatiotemporal synchronized dataset.

[0021] In one embodiment, identifying the normal operating cycle and operating range of the crane structure based on the spatiotemporal synchronization dataset further includes:

[0022] The lifting device locking signal and the sudden increase in lifting device load are used as the start markers of the operation cycle, and the lifting device unlocking signal and the sudden decrease in load are used as the end markers. Abnormal operation cycles are eliminated, and normal operation cycles are obtained.

[0023] In one embodiment, the step of extracting features from the load data during the normal operating cycle of the crane structure to construct the actual operating load spectrum further includes:

[0024] The load data in the normal operating cycle is processed, and the load intervals are divided. The mean of the high load subset is extracted as the average load under normal operating conditions.

[0025] In one embodiment, generating the stress-time history of structural nodes for calculating the fatigue life of the crane based on the stress-load mapping matrix and the actual operating load spectrum further includes:

[0026] Using the load data from the actual operational load spectrum as input, the stress values ​​of each node at different time points are calculated through the transfer equation of the stress-load mapping matrix.

[0027] In one embodiment, the transfer equation corresponds to the following expression:

[0028] , ( );

[0029] in, For stress, SR c Let F be the stress-load mapping matrix, where F is the load, c is the node number, and n is the total number of nodes.

[0030] In one embodiment, the step of calculating the fatigue damage of the crane structure and predicting the remaining fatigue life based on the stress-time history, combined with the stress-life curve of the crane structural material and the cumulative damage theory, further includes:

[0031] Based on the stress-life curve, query the limit number of cycles that cause the crane structural material to fail at each stress amplitude;

[0032] Based on the cumulative damage theory, the fatigue damage caused by each stress amplitude level is calculated and linearly accumulated to obtain the total cumulative damage.

[0033] When the total accumulated damage reaches a critical value, the structure is determined to have experienced fatigue failure, and the remaining fatigue life is predicted based on the current rate of damage accumulation.

[0034] In one embodiment, the formula for calculating the cumulative total damage D is:

[0035] ;

[0036] ;

[0037] in, The fatigue damage is calculated for the i-th stress level during the work cycle, where m is a material constant and C is a material constant related to fatigue strength. The stress amplitude, Let be the number of cycles under the i-th stress level of the structure, and k be the number of operation cycle levels for different stress levels.

[0038] The prediction of remaining fatigue life further includes:

[0039] Based on the cumulative total damage D, the remaining fatigue life is calculated using the following formula:

[0040] ;

[0041] in, Indicates the remaining life expectancy. This indicates the number of years the current load spectrum device has been in use.

[0042] In one embodiment, the multi-source data includes at least the gantry crane trolley / crane positioning data, the hoisting mechanism positioning data, and the spreader status signal.

[0043] To achieve the above objectives, the present invention provides a crane structure fatigue life prediction system, comprising:

[0044] The data acquisition module is used to collect actual working data of the crane structure;

[0045] The load information processing module is used to perform data processing and job cycle identification technology on the actual working data to construct the actual working load spectrum.

[0046] The physical model information module is used to establish and store the finite element model of the crane structure, and generate the stress-load mapping relationship matrix of the crane structure nodes based on the finite element model.

[0047] The fatigue life prediction module is used to generate a stress-time history for calculating the fatigue life of the structural nodes of the crane based on the stress-load mapping matrix and the actual operating load spectrum.

[0048] Based on the stress-time history, combined with the stress-life curve of the crane structure material and the cumulative damage theory, the fatigue damage of the crane structure is calculated and the remaining fatigue life is predicted.

[0049] The interactive and 3D display module is used to display the device's operating status, load spectrum, and structural life prediction results in real time.

[0050] To achieve the above objectives, the present invention provides a crane structure fatigue life prediction device, comprising:

[0051] Memory is used to store instructions that can be executed by the processor;

[0052] A processor for executing the instructions to implement the method as described in any of the preceding descriptions.

[0053] To achieve the above objectives, the present invention provides a computer-readable medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the method described in any of the preceding claims is performed.

[0054] This invention provides a method and system for predicting the fatigue life of crane structures. By establishing a finite element model of the crane structure to generate a stress-load mapping matrix, collecting and processing actual working data to construct an actual working load spectrum, and then generating the stress-time history of structural nodes, fatigue damage is calculated and the remaining fatigue life is predicted by combining the material stress-life curve and cumulative damage theory. This achieves online, real-time, accurate prediction and visualization of the fatigue life of the entire crane structure, providing a basis for formulating precise operation and maintenance plans. Attached Figure Description

[0055] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, in which the same reference numerals always denote the same features, wherein:

[0056] Figure 1 A step diagram of a method for predicting the fatigue life of a crane structure according to an embodiment of the present invention is disclosed.

[0057] Figure 2 A flowchart of a method for predicting the fatigue life of a crane structure according to an embodiment of the present invention is disclosed;

[0058] Figure 3 A finite element model diagram of a port crane according to an embodiment of the present invention is disclosed;

[0059] Figure 4 A schematic diagram illustrating the division of beam positions according to an embodiment of the present invention is disclosed;

[0060] Figure 5 A schematic diagram of standard operating condition data load according to an embodiment of the present invention is disclosed;

[0061] Figure 6 A load spectrum distribution frequency diagram according to an embodiment of the present invention is disclosed;

[0062] Figure 7A schematic diagram illustrating fatigue damage calculation results according to an embodiment of the present invention is provided.

[0063] Figure 8 A block diagram of a crane structure fatigue life prediction system according to an embodiment of the present invention is disclosed;

[0064] Figure 9 A block diagram illustrating the principle of a crane structure fatigue life prediction device according to an embodiment of the present invention is disclosed.

[0065] The meanings of the labels in the figures are as follows:

[0066] 10. Finite element model;

[0067] 11. Stop position;

[0068] 12. Maximum forward extension position;

[0069] 13. Main beams;

[0070] 200 Crane Structure Fatigue Life Prediction System;

[0071] 201 Data Acquisition Module;

[0072] 202 Load Information Processing Module;

[0073] 203 Physical Model Information Module;

[0074] 204 Fatigue Life Prediction Module;

[0075] 205 Interactive and 3D Display Module;

[0076] 210 Client;

[0077] 220 Server / Database;

[0078] 901 communication bus;

[0079] 902 processor;

[0080] 903 Memory (ROM);

[0081] 904 RAM;

[0082] 905 communication port;

[0083] 906 hard drive. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0085] Figure 1 A step diagram of a method for predicting the fatigue life of a crane structure according to an embodiment of the present invention is disclosed, as follows: Figure 1 As shown, the crane structure fatigue life prediction method proposed in this invention includes the following steps:

[0086] S1. Establish a finite element model of the crane structure, and generate the stress-load mapping relationship matrix of the crane structure nodes by dimensionality reduction processing based on the finite element model;

[0087] S2. Collect actual working data of the crane structure. The actual working data shall include at least the trolley position, the lifting position of the spreader, the lifting load, the spreader load, the spreader opening and closing signal, and the running speed.

[0088] S3. Perform data processing and job cycle identification technology on the actual working data to construct the actual working load spectrum;

[0089] S4. Based on the stress-load mapping matrix and the actual operating load spectrum, generate the stress-time history of the structural nodes used to calculate the fatigue life of the crane;

[0090] S5. Based on the stress-time history, combined with the stress-life curve of the crane structure material and the cumulative damage theory, calculate the fatigue damage of the crane structure and predict the remaining fatigue life.

[0091] In view of the fact that fatigue cracking is inevitable in the structure of port cranes during loading and unloading operations, which seriously affects the safety of equipment operation, this invention proposes a method for predicting the fatigue life of crane structures. By establishing a finite element model of the crane structure to generate a stress-load mapping matrix, collecting and processing actual working data to construct an actual working load spectrum, and then generating the stress-time history of structural nodes, fatigue damage is calculated and the remaining fatigue life is predicted by combining the material stress-life curve and cumulative damage theory.

[0092] The crane structure fatigue life prediction method provided by this invention can, on the one hand, be based on the digital twin method to collect working data in real time through the crane's PLC (Programmable Logic Controller) system or workstation to obtain the load spectrum; on the other hand, it can carry out accurate calculations through the finite element mechanism model to achieve online, real-time, accurate prediction and visualization of the fatigue life of the entire crane structure, providing a basis for the subsequent formulation of precise operation and maintenance plans for cranes.

[0093] Figure 2 A flowchart of a method for predicting the fatigue life of a crane structure according to an embodiment of the present invention is disclosed, as follows: Figure 2 As shown, the following text will combine Figure 1 and Figure 2These steps are described in detail. It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined and related to each other to form preferred technical solutions.

[0094] S1. Establish a finite element model of the crane structure, and generate the stress-load mapping matrix of the crane structure nodes by dimensionality reduction processing based on the finite element model.

[0095] Optionally, based on the design parameters and engineering properties of the port crane, a finite element simulation model of the crane structure is established by integrating structural geometric parameters, material constitutive relations, and connection boundary conditions.

[0096] The geometric parameters include, but are not limited to, track gauge, base distance, height, front and rear extension distances, and component cross-sectional dimensions and arrangement; the material constitutive relations include, but are not limited to, material properties, basic mechanical properties, fatigue properties, and elements; the connection conditions include, but are not limited to, hinged connections, rigid connections, and load conditions.

[0097] In this step, dimensionality reduction is a mathematical modeling process based on mechanical principles. Specifically, it reduces the dimensionality of complex physical structures (high-dimensional finite element models) to generate mathematical matrices (low-dimensional), thereby improving computational efficiency.

[0098] Figure 3 A finite element model diagram of a port crane according to an embodiment of the present invention is disclosed, such as... Figure 3 As shown, when the port crane is working, the trolley transporting goods back and forth will cause fatigue damage to the steel structure. The present invention can establish a finite element model 10 based on the design parameters, material properties, structural connection methods, etc. of the port crane.

[0099] Finite element model 10 shows the visualized form of a port crane (such as beams, legs, tie rods, etc.) after being modeled using the finite element method. Based on finite element model 10, stress is calculated using the "unit load method" to generate a "structural node stress-unit load mapping matrix", which provides the basis for stress calculation for subsequent fatigue life analysis of the crane structure.

[0100] In one embodiment, the trolley, loaded from the stop position to the beam position with the maximum forward extension, is discretized into multiple points for analysis and calculation, thereby studying the influence of structural stress changes on fatigue damage.

[0101] Figure 4 A schematic diagram illustrating the division of beam positions according to an embodiment of the present invention is shown, as follows: Figure 4As shown, the process of the trolley running with load in the normal working cycle of the crane is discretized into multiple positions for finite element analysis calculation. That is, the position of the main beam from the stop position 11 to the maximum forward distance position 12 is divided into n points, including points P0, P1, P2, P3, P4, P5... P(n-1), Pn.

[0102] In one embodiment, the stress response of the crane structural nodes under a unit moving load is calculated using the unit load method for dimensionality reduction. A stress-load mapping matrix for the crane structural nodes is constructed, representing the coefficient relationship between the overall machine stress and the working load. That is, the stress situation of the crane under a unit moving load is independently calculated in the finite element model using the unit load method at each point in the divided beam 13, and the stress-load mapping matrix of the crane structural nodes is generated. The results are shown in Table 1.

[0103] Table 1 Stress-Load Mapping Matrix of the Main Beam

[0104]

[0105] S2. Collect actual working data of the crane structure. The actual working data includes at least the trolley position, the lifting position of the spreader, the lifting load, the spreader load, the spreader opening and closing signal, and the running speed.

[0106] In this embodiment, real-time data acquisition is performed by the PLC system or workstation on the port crane. Based on actual calculations and design requirements, the operating data of the equipment over time, i.e., the actual working data, is obtained and transmitted to the next processing module (load information processing module, which will be introduced later). The actual working data includes, but is not limited to, trolley position (TP), hoisting position (HP), lifting load (LW), lock / lock signal (Lock), running speed (TS), etc.

[0107] S3. Perform data processing and job cycle identification technology on the actual working data to construct the actual working load spectrum.

[0108] This step employs a multi-level analysis architecture to achieve intelligent identification and condition classification of port crane operation cycles, and establishes a real-time load spectrum for crane operations.

[0109] In one embodiment, the data processing and job cycle identification technology process for the actual work data to construct the actual work load spectrum further includes:

[0110] The actual working data is preprocessed to construct a spatiotemporal synchronization dataset;

[0111] Based on the spatiotemporal synchronization dataset, the normal operating cycle and operating range of the crane structure are identified;

[0112] Feature extraction is performed on the load data during the normal operating cycle of the crane structure to construct the actual operating load spectrum.

[0113] In this embodiment, based on the identification of each complete operating cycle of the port crane, the load data within that cycle needs to be processed to extract characteristic loads for fatigue assessment.

[0114] In one embodiment, the step of constructing the actual work load spectrum by performing data processing and job cycle identification on the actual work data further includes:

[0115] The actual working data is preprocessed to construct a spatiotemporal synchronized dataset. The actual working data is then cleaned, timestamp aligned, and multi-source data fused to generate consistent time-series data to construct the spatiotemporal synchronized dataset.

[0116] In this embodiment, the actual working data is preprocessed, including but not limited to removing invalid data segments such as sensor breakpoints and signal loss. A time stamp alignment mechanism is established based on the crane operation log to construct a spatiotemporal synchronized dataset. Multi-dimensional data sources such as gantry crane positioning data, hoisting mechanism positioning data, and spreader status signals are integrated to establish a spatiotemporal synchronized dataset.

[0117] Based on the spatiotemporal synchronized dataset, the steps of identifying the normal operating cycle and operating range of the crane structure further include: using the spreader locking signal and the spreader load surge as the start marker of the operating cycle, and using the spreader unlocking signal and the load drop as the end marker, excluding abnormal operating cycles, and obtaining the normal operating cycle.

[0118] Specifically, a judgment model is established based on engineering experience rules, and the loading and unloading operation cycle of the port crane under each normal working condition is judged by equipment data such as trolley position, lifting position, trolley load and spreader opening and closing signals.

[0119] The start and end conditions for the job cycle are as follows:

[0120] Work cycle start indicators: spreader locking signal and sudden increase in load impact;

[0121] Operation cycle termination indicators: spreader unlocking signal and sudden load impact drop.

[0122] Furthermore, after eliminating abnormal and test conditions, a complete work cycle under normal operating conditions is obtained, which satisfies the continuity in time. Under normal operating conditions, the work process includes "grabbing the box - transporting - placing the box - resetting".

[0123] In one embodiment, the step of extracting features from load data during the normal operating cycle of the crane structure to construct the actual operating load spectrum further includes:

[0124] The load data in the normal operating cycle is processed, and the load intervals are divided. The mean of the high load subset is extracted as the average load under normal operating conditions.

[0125] In this embodiment, key information for each normal working cycle is first extracted from the operating data of the port crane. Specifically, this includes the starting and ending positions of the trolley running on the main beam, the load weight for that cycle, and the lifting position of the spreader. Preferably, a filter is used to smooth the load data within each cycle to eliminate data noise.

[0126] Specifically, from each identified normal working cycle, raw load data, including the starting and ending positions of the trolley on the main beam, the load weight, and the lifting position of the spreader, can be extracted.

[0127] A filter is used to smooth the raw load data within the cycle to suppress noise interference, resulting in filtered data. Subsequently, based on the typical load abrupt change characteristics caused by the "grabbing" and "releasing" actions, a complete work cycle load-time data is intelligently divided into several different "lifting load intervals," each interval corresponding to one effective cargo handling process.

[0128] Figure 5 A schematic diagram of the standard operating condition data load according to an embodiment of the present invention is disclosed, such as... Figure 5 As shown, based on the characteristics of load mutation caused by the container handling actions of port cranes, the original load data of each group of cycles are divided into different container lifting load ranges.

[0129] like Figure 5 As shown, the data within each container load range typically exhibit a bimodal distribution, reflecting the impact transition process from no load to full load.

[0130] To eliminate interference from low-load impacts, the interval data is divided into two subsets: high load and low load. The mean of the high load subset data is calculated and used as the representative final average load for subsequent fatigue calculations under this working condition.

[0131] After extracting the characteristic loads of each work cycle, the actual work load spectrum for fatigue life assessment is constructed through data statistics and aggregation, as shown in Table 2.

[0132] Table 2 Actual Operational Load Spectrum

[0133]

[0134] in, For position coordinates, The average load during lifting. For the load of the trolley, For the load of the lifting device.

[0135] Key parameters for each job cycle (Cycle i) in both the "Forward" and "Backward" running directions:

[0136] One is spatial path information, including the starting and ending positions of the vehicle's movement;

[0137] The other is the load magnitude information, namely the actual operating load F, which consists of three parts: This represents the average weight of the containers being transported. This refers to the weight of the cart itself; This refers to the weight of the lifting device itself.

[0138] The actual operating load spectrum aggregates data from multiple consecutive operating cycles to form a complete dataset that accurately reflects the specific working history of the crane and includes information on load magnitude and spatial distribution. This provides a direct input for subsequently mapping the actual load to structural stress.

[0139] Correspondingly, Figure 6 A load spectrum distribution frequency diagram according to an embodiment of the present invention is disclosed, such as Figure 6 The diagram shows the statistical patterns of loads experienced by port cranes during actual operation. Specifically, it identifies the most frequent loads and the most occasional load ranges, representing the loads the equipment most frequently bears. The trolley experiences significantly higher operating cycle frequencies around coordinates approximately 4000, 6000, 8000, and 10000, indicating these are high-frequency, critical locations for loading and unloading ships. Conversely, the operating cycle frequencies are very low around coordinates 0-3000 and beyond 12000, reflecting the varying frequency of trolley operations at different locations. These high-frequency locations represent areas where the structure experiences more concentrated load cycles, providing the most direct and crucial input for accurate fatigue life assessment and structural health management.

[0140] In this embodiment, by filtering, dividing, and extracting features (such as extracting the mean of high load subsets) of the load data, noise interference caused by vibration, impact, etc. is effectively eliminated. The extracted "actual operating load spectrum" is more representative of stable load conditions that contribute substantially to structural fatigue damage, thus improving the accuracy and reliability of subsequent life prediction.

[0141] Furthermore, the operating load spectrum of each working cycle of the equipment is sorted into graded load spectra of multiple stress levels, which are used as coefficient matrices to input the stress-load transfer equation of the structural nodes to calculate the stress spectrum, including different stress amplitudes and corresponding cycle numbers of the stress history.

[0142] S4. Based on the stress-load mapping matrix and the actual operating load spectrum, generate the stress-time history of the structural nodes used to calculate the fatigue life of the crane.

[0143] Furthermore, by taking the load data from the actual operational load spectrum as input, the stress values ​​of each node at different time points are calculated through the transfer equation of the stress-load mapping relationship matrix.

[0144] In this step, the actual operating load is first mapped to structural stress. Specifically, based on the stress-load mapping matrix of the crane structural nodes established in step S1, a stress-actual operating load transfer equation is established. Since the finite element model is a linear model within the elastic range, the stress response and the actual load satisfy a linear relationship. The transfer equation is as follows: , ( );

[0145] in, For structural nodal stress, SR c Let F be the stress-load mapping matrix, where F is the actual working load, c is the node number, and n is the total number of nodes.

[0146] Each load range in the actual operational load spectrum is aggregated to the nearest predefined load step point in the finite element model. For example, this involves determining the start and end positions of the load run in the forward direction using Cyclei. , The points defined in the finite element model satisfy the following:

[0147] .

[0148] The load operating range is , ( The load size is .

[0149] Based on the magnitude of the operational load, the input coefficient matrix is ​​substituted into the transfer equation. Combined with the stress-time history data of the nodes constructed within the operating interval, the stress-load transfer equations corresponding to each node within the interval are substituted sequentially. , ( ), = The actual stress variation curves within the work cycle were obtained, as shown in Table 3.

[0150] Table 3 Actual stress variation curves

[0151]

[0152] S5. Based on the stress-time history, combined with the stress-life curve of the crane structure material and the cumulative damage theory, calculate the fatigue damage of the crane structure and predict the remaining fatigue life.

[0153] Furthermore, based on the stress-life curve, the limit number of cycles that cause failure of the crane structural material is queried at each stress amplitude;

[0154] Based on the cumulative damage theory, the fatigue damage caused by each stress amplitude level is calculated and linearly accumulated to obtain the total cumulative damage.

[0155] When the total accumulated damage reaches a critical value, the structure is determined to have experienced fatigue failure, and the remaining fatigue life is predicted based on the current rate of damage accumulation.

[0156] After obtaining the stress-time history, this step calculates the fatigue damage of the structure and predicts its remaining life by using material fatigue characteristics and a cumulative damage model.

[0157] Stress value refers to the magnitude of the internal force per unit area within a structure at a specific moment. Stress amplitude represents the range of stress change over a complete stress cycle.

[0158] The stress-time history obtained in step S4 above is statistically analyzed by cycle counting to obtain a series of stress amplitudes S of different magnitudes and their corresponding cycle numbers N. The fatigue characteristics of the crane structural material are described by the PSN curve (stress-life curve), and its formula is as follows: This curve provides the fatigue limit of the material under different stress amplitude levels.

[0159] Where S is the stress amplitude (MPa), P is the reliability of the curve probability model, N is the number of cycles leading to failure under the corresponding stress amplitude, and m and C are material constants determined experimentally.

[0160] This step uses Miner's linear cumulative damage rule for damage calculation. It assumes that fatigue damage under different stress levels can be linearly accumulated. When the total damage D reaches 1, the structure experiences fatigue failure. The fatigue damage Di caused by the i-th stress level is:

[0161] ;

[0162] ;

[0163] in, The fatigue damage is calculated for the i-th stress level during the work cycle, where m is a material constant and C is a material constant related to fatigue strength. The stress amplitude, Let be the number of cycles under the i-th stress level of the structure, and k be the number of operation cycle levels for different stress levels.

[0164] It is important to note that 'a' refers to the number of stress cycles the structure undergoes at a given stress level. When 'a' is 1, it represents the damage caused by each working cycle (the i-th cycle). It is based on "one cycle", that is, the "unit damage" of each cycle is calculated separately.

[0165] Figure 7 A schematic diagram illustrating fatigue damage calculation results according to an embodiment of the present invention is shown, as follows. Figure 7 As shown, the first row (ELEMENT) represents the unit number, the second row (Node point) represents the stress point, the third row (Detail Class) represents the fatigue details, the fourth row (Event No) represents the work cycle number and the damage calculation for the current cycle level (only one cycle is shown here, subsequent cycles are not shown), and the last part (Total) is the sum of the calculated fatigue damage. It can be seen that the cumulative fatigue damage of the corresponding position of the port crane structural unit in the current work cycle is calculated by stress spectrum, and then fatigue life is predicted by combining various standard coefficients and evaluation criteria in the built-in knowledge base. The final result can be visualized on the display interface for staff to view as needed.

[0166] In one embodiment, the prediction of remaining fatigue life further includes:

[0167] Based on the cumulative total damage D, the remaining fatigue life is calculated using the following formula:

[0168] ;

[0169] in, Remaining lifespan The current load spectrum device has been in use for many years.

[0170] D represents the cumulative damage from all currently recorded operating cycles. For example, suppose a port crane, based on operating log data from the past Y=5 years, has a cumulative damage D=0.25 on its critical structural nodes. Starting from a brand new state, the current load spectrum would exceed its fatigue life limit after repeating 1 / 0.25=4 times. Since the equipment has already completed one cycle of this load spectrum in the past 5 years and consumed its corresponding lifespan, the remaining number of cycles is... Therefore, the remaining fatigue life is [number] years. Year.

[0171] To achieve the above objectives, the present invention provides a fatigue life prediction system for crane structures. Figure 8 A block diagram of a crane structure fatigue life prediction system according to an embodiment of the present invention is disclosed, such as... Figure 8 As shown, the crane structure fatigue life prediction system 200 may include:

[0172] The data acquisition module 201 is used to collect the actual working data of the crane structure and upload the collected raw operating data to the server / database 220 through the data transmission network to provide a data source for subsequent processing.

[0173] Server / Database 220 serves as the system's data center and scheduling hub, responsible for storing massive amounts of historical operational data, model parameters, and calculation results, and coordinating data exchange and communication between various modules.

[0174] The load information processing module 202 receives raw data collected by the data acquisition module 201, performs data processing and operation cycle identification technology on the actual working data, constructs the actual working load spectrum, and sends the processed load spectrum to the fatigue life prediction module 204.

[0175] The physical model information module 203 stores a digital model representing the structural characteristics of the crane, which is used to establish and store the finite element model of the crane structure, and to generate the stress-load mapping relationship matrix of the crane structure nodes based on the finite element model.

[0176] The physical model information module 203 constructs a simulation mechanism model of the crane structure based on engineering information such as design geometric parameters, part connection relationships, material properties, and weld settings. It calculates the stress model of the crane under unit load and provides stress mapping relationship data to the fatigue life prediction module 204 to convert the load spectrum into stress.

[0177] The fatigue life prediction module 204 is used to generate a stress-time history of structural nodes for calculating the fatigue life of the crane based on the stress-load mapping matrix and the actual operating load spectrum.

[0178] Based on the stress-time history, combined with the stress-life curve of the crane structure material and the cumulative damage theory, the fatigue damage of the crane structure is calculated and the remaining fatigue life is predicted.

[0179] The fatigue life prediction module 204 contains normative coefficients and algorithms for fatigue and stability. It calculates fatigue damage and predicts fatigue life by using the input work load spectrum information and crane stress model. The calculated fatigue damage distribution and remaining life results are then sent to the interactive and 3D display module 205.

[0180] The interactive and 3D display module 205 is the interface between the system and the user. It is usually deployed on the client 210 (such as a computer or mobile terminal) and is responsible for the visualization of the results. It is used to display the device's working status, load spectrum, and structural life prediction results in real time.

[0181] The crane structure fatigue life prediction system provided by this invention continuously acquires on-site data and stores it in a database through the data acquisition module 201. The load information processing module 202 and the fatigue life prediction module 204 are triggered to calculate as needed or at regular intervals, driving the entire prediction process. Finally, the prediction results are pushed to the client's interactive and three-dimensional display module 205 to inform the equipment management personnel in an intuitive way, thereby realizing online, real-time, visual prediction and precise management of the fatigue life of port cranes.

[0182] The present invention provides a method and system for predicting the fatigue life of a crane structure, which can not only improve the accuracy of predicting the fatigue life of a port crane structure, but also predict the overall life status of the port crane, not limited to single-point assessment, and can update the port crane's operating status and fatigue life prediction in real time, making it easier to formulate accurate and economical operation and maintenance plans.

[0183] Figure 9 This diagram illustrates a crane structure fatigue life prediction system according to an embodiment of the present invention. The crane structure fatigue life prediction device 900 may include an internal communication bus 901, a processor 902, a read-only memory (ROM) 903, a random access memory (RAM) 904, a communication port 905, and a hard disk 906. The internal communication bus 901 enables data communication between components of the crane structure fatigue life prediction device. The processor 902 can perform judgments and issue prompts. In some embodiments, the processor 902 may consist of one or more processors.

[0184] The communication port 905 enables data transmission and communication between the crane structure fatigue life prediction device 900 and external input / output devices. In some embodiments, the crane structure fatigue life prediction device 900 can send and receive information and data from a network via the communication port 905. In some embodiments, the crane structure fatigue life prediction device can transmit data and communicate with external input / output devices via a wired connection through the input / output port.

[0185] The crane structure fatigue life prediction device 900 may also include different types of program storage units and data storage units, such as a hard disk 906, a read-only memory (ROM) 903, and a random access memory (RAM) 904, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 902. The processor 902 executes these instructions to implement the main part of the method. The results processed by the processor 902 are transmitted to an external output device via a communication port 905 and displayed on the user interface of the output device.

[0186] For example, the implementation process document of the crane structure fatigue life prediction device 900 described above can be a computer program, stored in the hard disk 906, and can be loaded into the processor 902 for execution to implement the method of this application.

[0187] When the implementation process document of the crane structure fatigue life prediction method is a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, computer-readable storage media can include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media used for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0188] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0189] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0190] Those skilled in the art will understand that information, signals, and data can be represented using any of a variety of different techniques and skills. For example, the data, instructions, commands, information, signals, bits, symbols, and chips described throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0191] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0192] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0193] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0194] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0195] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the inventive concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A method for predicting fatigue life of a crane structure, characterized by, The method comprises the following steps: establishing a finite element model of the crane structure, and generating a stress-load mapping relationship matrix of nodes of the crane structure according to the finite element model; collecting actual working data of the crane structure, the actual working data at least including trolley position, lifting tool lifting position, lifting load, lifting tool load, lifting tool locking and unlocking signal and running speed; performing data processing and operation cycle identification on the actual working data, and constructing an actual operation load spectrum; generating a stress-time history of nodes for calculating fatigue life of the crane based on the stress-load mapping relationship matrix and the actual operation load spectrum; calculating fatigue damage of the crane structure and predicting remaining fatigue life of the crane structure according to the stress-time history, in combination with a stress-life curve of a material of the crane structure and a cumulative damage theory.

2. The method of predicting the fatigue life of a crane structure according to claim 1, characterized in that, The generating of the stress-load mapping relationship matrix of the nodes of the crane structure according to the finite element model further comprises: calculating stress response of the nodes of the crane structure under unit moving load by dimension reduction processing of a unit load method, and constructing a crane structure node stress-load mapping relationship matrix of coefficient relationship between whole machine stress and working load.

3. The method of claim 1, wherein, The data processing and operation cycle identification on the actual working data, and the constructing of the actual operation load spectrum further comprise: preprocessing the actual working data to construct a space-time synchronous data set; identifying normal operation cycle and operation range of the crane structure based on the space-time synchronous data set; extracting features of load data in the normal operation cycle of the crane structure to construct the actual operation load spectrum.

4. The method of claim 3, wherein, The preprocessing of the actual working data to construct the space-time synchronous data set further comprises: performing data cleaning, timestamp alignment and multi-source data fusion on the actual working data to generate consistent time series data to construct the space-time synchronous data set.

5. The method of claim 3, wherein the fatigue life prediction of the crane structure is performed by using the stress intensity factor and the stress ratio. The identifying of the normal operation cycle and operation range of the crane structure based on the space-time synchronous data set further comprises: taking the lifting tool locking signal and lifting tool load sudden increase as starting marks of the operation cycle, taking the lifting tool unlocking signal and load sudden decrease as termination marks, excluding abnormal operation cycles, and obtaining the normal operation cycle.

6. The method of claim 3, wherein, The extracting of features of the load data in the normal operation cycle of the crane structure to construct the actual operation load spectrum further comprises: processing the load data in the normal operation cycle, dividing load intervals, and extracting mean values of high load subsets as average loads under normal working conditions.

7. The method of predicting the fatigue life of a crane structure according to claim 1, characterized by, The generating of the stress-time history of nodes for calculating fatigue life of the crane based on the stress-load mapping relationship matrix and the actual operation load spectrum further comprises: taking load data in the actual operation load spectrum as input, and calculating stress values of each node at different time points through a transfer equation of the stress-load mapping relationship matrix.

8. The method of predicting the fatigue life of a crane structure according to claim 7, characterized in that, The transfer equation, and a corresponding expression is: ，( )； wherein, is the stress, SR c is the stress-load mapping matrix, c is the node number, F is the load, and n is the total number of nodes.

9. The method of predicting the fatigue life of a crane structure according to claim 1, characterized by, The calculating of fatigue damage of the crane structure and the predicting of remaining fatigue life of the crane structure according to the stress-time history, in combination with the stress-life curve of the material of the crane structure and the cumulative damage theory further comprises: According to the stress-life curve, the limit cycle number of each stress amplitude leading to failure of the crane structure material is inquired; Based on the cumulative damage theory, the fatigue damage caused by each stress amplitude level is calculated and linearly accumulated to obtain the cumulative total damage; When the cumulative total damage reaches a critical value, it is determined that the structure has fatigue failure, and the remaining fatigue life is predicted according to the current damage accumulation rate.

10. The method of predicting the fatigue life of a crane structure according to claim 9, characterized in that, The calculation formula of the cumulative total damage D is: ； ； wherein, is the fatigue damage calculated for the i-th stress level of the duty cycle, m is a material constant, C is a material constant related to the fatigue strength, is the stress amplitude, is the number of cycles of the structure at the i-th stress level, k is the number of duty cycles at different stress levels.

11. The method of predicting the fatigue life of a crane structure according to claim 10, characterized in that, The prediction of the remaining fatigue life further comprises: According to the cumulative total damage D, the remaining fatigue life is calculated by using the following formula: ； wherein, is the remaining life in years, is the current load spectrum device has been in use for years.

12. The method of claim 4, wherein, The multi-source data at least includes quay crane trolley / small car positioning data, hoisting mechanism positioning data, and sling state signal.

13. A crane structure fatigue life prediction system characterized by, The system comprises: A data acquisition module for acquiring actual working data of the crane structure; A load information processing module for data processing and operation cycle identification technology process on the actual working data, and for constructing an actual operation load spectrum; A physical model information module for establishing and storing a finite element model of the crane structure, and for generating a stress-load mapping relationship matrix of the crane structure nodes according to the finite element model; A fatigue life prediction module for generating a stress-time history of the crane structure nodes for calculating the fatigue life of the crane based on the stress-load mapping relationship matrix and the actual operation load spectrum; According to the stress-time history, the fatigue damage of the crane structure is calculated and the remaining fatigue life is predicted in combination with the stress-life curve of the crane structure material and the cumulative damage theory; An interactive and three-dimensional display module for real-time display of the equipment working state, load spectrum and structure life prediction result.

14. A crane structure fatigue life prediction device, comprising: A memory for storing instructions executable by a processor; A processor for executing the instructions to implement the method of any one of claims 1-12.

15. A computer storage medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the method of any one of claims 1-12 is performed.

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