Equipment state monitoring and maintenance system

Abstract

The invention relates to the technical field of equipment state monitoring, in particular to an equipment state monitoring and maintenance system, and provides the following scheme: acquiring three-dimensional stress energy density distribution data of a roller bearing seat, predicting a roller surface microcrack expansion trend, and generating a dynamic residual life index; rolling force compensation is carried out by combining the ellipticity data of the section of the pipe; meanwhile, by synchronously collecting Barkhausen noise signals and cooling medium flow data, the cooling intensity is adjusted in real time; in order to ensure the roller health, a roller health weight is generated based on the dynamic residual life index, a rolling force compensation model is constructed, rolling force distribution is optimized through a particle swarm optimization algorithm, and the uniformity of the wall thickness of the pipe is ensured; the method effectively prevents roller damage and pipe wall thickness mutation, improves the production efficiency and the product quality, and prolongs the service life of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment status monitoring, and in particular to an equipment status monitoring and maintenance system. Background Art

[0002] During the continuous rolling of steel pipes, the dynamic contact between the rolls and the tube billet leads to energy efficiency losses. This is particularly true in high-frequency welded pipe mills, where the coupled failure of hidden roll surface damage and sudden changes in pipe wall thickness during high-speed rolling of thin-walled pipes is becoming increasingly prominent. Traditional rolling force control methods typically rely on simple empirical adjustments or preset rules, which are difficult to handle under complex rolling conditions and cannot monitor and compensate for roll damage and pipe geometry changes in real time. This lack of precise monitoring and adjustment often leads to uneven pipe wall thickness, fluctuating product quality, and easily causes roll damage, shortening equipment life. Furthermore, existing cooling control technologies fail to effectively address the problems of roll surface overheating or uneven cooling, and lack dynamic adjustment, further exacerbating equipment wear and product quality issues during the rolling process. Therefore, accurately predicting and compensating for roll damage to ensure quality and equipment health during the rolling process is crucial to improving steel pipe production efficiency and reducing costs.

[0003] The problem raised by this background technology is that during the continuous rolling of steel pipes, there is an irreversible accumulation problem of energy efficiency loss due to dynamic contact between the roller and the tube blank. In order to solve the above problem, this application designs an equipment status monitoring and maintenance system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide an equipment status monitoring and maintenance system. By collecting the three-dimensional stress energy density distribution data of the roller bearing seat, the micro-crack expansion trend on the roller surface is predicted, a dynamic remaining life index is generated, and rolling force compensation is performed in combination with the pipe cross-section ovality data. At the same time, the cooling intensity is adjusted in real time by synchronously collecting the Barkhausen noise signal and the cooling medium flow data. To ensure the health of the roller, the roller health weight is generated based on the dynamic remaining life index, and a rolling force compensation model is constructed. The rolling force distribution is optimized by the particle swarm optimization algorithm to ensure the uniformity of the pipe wall thickness. This method effectively prevents roller damage and sudden changes in pipe wall thickness, improves production efficiency and product quality, and extends the service life of the equipment.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An equipment status monitoring and maintenance system is applied to a steel pipe continuous rolling production line. The system includes a data acquisition module, a data analysis module, and an equipment maintenance module, wherein:

[0007] The data acquisition module is used to collect three-dimensional stress energy density distribution data, Barkhausen noise signal and cooling medium flow data of the roller bearing seat;

[0008] The data analysis module establishes an energy dissipation rate model based on the three-dimensional stress energy density distribution data, predicts the growth trend of microcracks on the roll surface and generates a dynamic remaining life index. At the same time, based on the amplitude and frequency domain characteristics of the Barkhausen noise signal, it establishes a mapping relationship between the local magnetic domain changes of the roll and the abnormal phase change characteristics. It also determines the effectiveness of the current cooling mode based on the cooling medium flow data and identifies the cooling abnormality area.

[0009] The equipment maintenance module compensates for the roll reduction amount according to the output of the data analysis module and adjusts the opening strategy of the cooling nozzle combination.

[0010] The data analysis module includes:

[0011] The crack prediction unit builds an energy dissipation rate model based on three-dimensional stress energy density distribution data, dynamically corrects time-varying parameters through a residual-driven algorithm, and generates a thermal map of the crack initiation location and a time-varying growth rate curve;

[0012] The remaining life analysis unit is used to couple crack prediction results with production condition data, generate the remaining life probability distribution interval through Monte Carlo simulation, and generate a dynamic remaining life index based on order requirements;

[0013] The cooling intensity analysis unit is used to analyze the magnetic domain-phase change mapping relationship of the Barkhausen noise signal and identify abnormal phase change areas and cooling failure areas based on the cooling medium flow distribution characteristics.

[0014] The crack prediction unit is configured with prediction logic, which includes:

[0015] Calculating local energy density distribution based on the three-dimensional stress energy density distribution data;

[0016] Extracting the energy dissipation rate peak and the spatial distribution entropy value based on the local energy density distribution, and establishing a mapping relationship between the energy threshold and the crack initiation position in combination with the fatigue crack growth rate test data of the roll material;

[0017] Comparing the mapping relationship with the predicted relationship of the offline calibrated finite element simulation model, and correcting the time-varying parameters of the energy dissipation rate model through a residual-driven algorithm, wherein the time-varying parameters characterize the crack growth coefficient, exponential factor, and crack growth activation energy of the damage accumulation characteristics of the roll material;

[0018] A crack growth trend prediction is generated based on the modified energy dissipation rate model, wherein the crack growth trend prediction includes a probability distribution heat map of the crack initiation position and a time-varying growth rate curve of the crack length.

[0019] The method of correcting the time-varying parameters of the energy dissipation rate model by using a residual driving algorithm includes:

[0020] Perform frequency domain decomposition on the residual signal between the mapping relationship and the prediction relationship to extract the energy dominant mode of the characteristic frequency band;

[0021] performing sensitivity analysis on the time-varying parameters in the energy dissipation rate model according to the energy dominant mode, calculating the sensitivity weights of the time-varying parameters through the constitutive equation of the roll material, and generating a priority sequence;

[0022] Performing a staged Bayesian correction on the time-varying parameters according to the priority sequence, wherein the staged Bayesian correction includes material constraints, energy constraints, and set constraints;

[0023] The time-varying parameters are iteratively modified until a predetermined number of iterations is met.

[0024] The remaining life analysis unit is configured with life analysis logic, and the life analysis logic includes:

[0025] Couple the probability distribution heat map and the time-varying expansion rate curve to extract the credibility weight of the crack initiation position and the probability density function of the expansion rate;

[0026] Generating a probability distribution interval of the remaining life through Monte Carlo simulation according to the credibility weight and the probability density function;

[0027] The probability distribution interval is matched with the order rolling mileage requirements in the production plan database, the remaining life confidence level that meets the current production task requirements is calculated, and a dynamic remaining life index is generated.

[0028] The cooling intensity analysis unit is configured with cooling identification logic, which includes:

[0029] The amplitude and frequency domain characteristics of the Barkhausen noise signal are used to identify the local magnetic domain changes of the roller, and a mapping relationship is established between the abnormal phase change characteristics of the roller surface.

[0030] When the abnormal phase change characteristics indicated by the mapping relationship exceed the process allowable range, the position and extent of the abnormal roll area are determined by extracting the multimodal features of the Barkhausen noise signal.

[0031] The cooling identification logic further includes:

[0032] determining the effectiveness of the current cooling mode according to the cooling medium flow data;

[0033] When the cooling medium flow rate data locally deviates from the set value, the local change in the cooling medium flow rate is correlated to determine the spatial region with uneven cooling distribution.

[0034] The equipment maintenance module includes:

[0035] A rolling force compensation unit, configured to receive ovality detection data and a dynamic remaining life index and generate a rolling force compensation parameter set;

[0036] The cooling intensity adjustment unit adjusts the nozzle opening and closing combination and flow distribution according to the abnormal phase change area and cooling failure area.

[0037] The rolling force compensation unit is provided with compensation logic, which includes:

[0038] The ovality data of the pipe section is divided into N detection areas according to the circumferential angle, the wall thickness deviation rate of each area is calculated, and the ovality feature matrix is ​​generated;

[0039] converting the dynamic remaining life index into a roll health weight;

[0040] A rolling force compensation model based on the health status of the equipment is constructed, the ellipticity characteristic matrix and the roll health weight are used as inputs of the rolling force compensation model, optimization is performed through the rolling force compensation model, and a rolling force compensation parameter set is calculated.

[0041] Optimizing the rolling force compensation model to calculate a rolling force compensation parameter set includes:

[0042] generating an initial compensated pressure distribution using the ellipticity characteristic matrix as input;

[0043] Using the health weight of the roll as a health weight constraint condition;

[0044] The Pareto optimal solution with the minimum total rolling force increment is calculated by a particle swarm optimization algorithm, and the Pareto optimal solution is used as a rolling force compensation parameter set.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This method utilizes a variety of information sources, including three-dimensional stress energy density distribution data, dynamic remaining life index, tube ovality data, and Barkhausen noise signals, to accurately predict roll health and crack growth trends. Based on this information, rolling force and cooling intensity are dynamically adjusted. The particle swarm optimization algorithm optimizes the rolling force distribution, ensuring uniformity of tube wall thickness, reducing roll damage and tube quality defects. This allows for precise prediction and compensation of roll damage, ensuring quality and equipment health during the rolling process, and improving steel pipe production efficiency and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 This is a module diagram of an equipment status monitoring and maintenance system according to embodiment 1 of the present invention;

[0049] Figure 2 This is a flow chart of a device status monitoring and maintenance method according to embodiment 2 of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0051] Example 1

[0052] See also Figure 1 The present invention provides an embodiment of an equipment status monitoring and maintenance system, which is applied to a steel pipe continuous rolling production line. The system includes a data acquisition module, a data analysis module, and an equipment maintenance module, wherein:

[0053] The data acquisition module is used to collect three-dimensional stress energy density distribution data, Barkhausen noise signal and cooling medium flow data of the roller bearing seat;

[0054] The data analysis module establishes an energy dissipation rate model based on the three-dimensional stress energy density distribution data, predicts the growth trend of microcracks on the roll surface and generates a dynamic remaining life index. At the same time, based on the amplitude and frequency domain characteristics of the Barkhausen noise signal, it establishes a mapping relationship between the local magnetic domain changes of the roll and the abnormal phase change characteristics. It also determines the effectiveness of the current cooling mode based on the cooling medium flow data and identifies the cooling abnormality area.

[0055] The equipment maintenance module compensates for the roll reduction amount according to the output of the data analysis module and adjusts the opening strategy of the cooling nozzle combination.

[0056] The data analysis module includes:

[0057] The crack prediction unit builds an energy dissipation rate model based on three-dimensional stress energy density distribution data, dynamically corrects time-varying parameters through a residual-driven algorithm, and generates a thermal map of the crack initiation location and a time-varying growth rate curve;

[0058] The remaining life analysis unit is used to couple crack prediction results with production condition data, generate the remaining life probability distribution interval through Monte Carlo simulation, and generate a dynamic remaining life index based on order requirements;

[0059] The cooling intensity analysis unit is used to analyze the magnetic domain-phase change mapping relationship of the Barkhausen noise signal and identify abnormal phase change areas and cooling failure areas based on the cooling medium flow distribution characteristics.

[0060] The crack prediction unit is configured with prediction logic, which includes:

[0061] Calculating local energy density distribution based on the three-dimensional stress energy density distribution data;

[0062] Extracting the energy dissipation rate peak and the spatial distribution entropy value based on the local energy density distribution, and establishing a mapping relationship between the energy threshold and the crack initiation position in combination with the fatigue crack growth rate test data of the roll material;

[0063] Comparing the mapping relationship with the predicted relationship of the offline calibrated finite element simulation model, and correcting the time-varying parameters of the energy dissipation rate model through a residual-driven algorithm, wherein the time-varying parameters characterize the crack growth coefficient, exponential factor, and crack growth activation energy of the damage accumulation characteristics of the roll material;

[0064] A crack growth trend prediction is generated based on the modified energy dissipation rate model, wherein the crack growth trend prediction includes a probability distribution heat map of the crack initiation position and a time-varying growth rate curve of the crack length.

[0065] The method of correcting the time-varying parameters of the energy dissipation rate model by using a residual driving algorithm includes:

[0066] Perform frequency domain decomposition on the residual signal between the mapping relationship and the prediction relationship to extract the energy dominant mode of the characteristic frequency band;

[0067] performing sensitivity analysis on the time-varying parameters in the energy dissipation rate model according to the energy dominant mode, calculating the sensitivity weights of the time-varying parameters through the constitutive equation of the roll material, and generating a priority sequence;

[0068] Performing a staged Bayesian correction on the time-varying parameters according to the priority sequence, wherein the staged Bayesian correction includes material constraints, energy constraints, and set constraints;

[0069] The time-varying parameters are iteratively modified until a predetermined number of iterations is met.

[0070] The remaining life analysis unit is configured with life analysis logic, and the life analysis logic includes:

[0071] Couple the probability distribution heat map and the time-varying expansion rate curve to extract the credibility weight of the crack initiation position and the probability density function of the expansion rate;

[0072] Generating a probability distribution interval of the remaining life through Monte Carlo simulation according to the credibility weight and the probability density function;

[0073] The probability distribution interval is matched with the order rolling mileage requirements in the production plan database, the remaining life confidence level that meets the current production task requirements is calculated, and a dynamic remaining life index is generated.

[0074] The cooling intensity analysis unit is configured with cooling identification logic, which includes:

[0075] The amplitude and frequency domain characteristics of the Barkhausen noise signal are used to identify the local magnetic domain changes of the roller, and a mapping relationship is established between the abnormal phase change characteristics of the roller surface.

[0076] When the abnormal phase change characteristics indicated by the mapping relationship exceed the process allowable range, the position and extent of the abnormal roll area are determined by extracting the multimodal features of the Barkhausen noise signal.

[0077] The cooling identification logic further includes:

[0078] determining the effectiveness of the current cooling mode according to the cooling medium flow data;

[0079] When the cooling medium flow rate data locally deviates from the set value, the local change in the cooling medium flow rate is correlated to determine the spatial region with uneven cooling distribution.

[0080] The equipment maintenance module includes:

[0081] A rolling force compensation unit, configured to receive ovality detection data and a dynamic remaining life index and generate a rolling force compensation parameter set;

[0082] The cooling intensity adjustment unit adjusts the nozzle opening and closing combination and flow distribution according to the abnormal phase change area and cooling failure area.

[0083] The rolling force compensation unit is provided with compensation logic, which includes:

[0084] The ovality data of the pipe section is divided into N detection areas according to the circumferential angle, the wall thickness deviation rate of each area is calculated, and the ovality feature matrix is ​​generated;

[0085] converting the dynamic remaining life index into a roll health weight;

[0086] A rolling force compensation model based on the health status of the equipment is constructed, the ellipticity characteristic matrix and the roll health weight are used as inputs of the rolling force compensation model, optimization is performed through the rolling force compensation model, and a rolling force compensation parameter set is calculated.

[0087] Optimizing the rolling force compensation model to calculate a rolling force compensation parameter set includes:

[0088] generating an initial compensated pressure distribution using the ellipticity characteristic matrix as input;

[0089] Using the health weight of the roll as a health weight constraint condition;

[0090] The Pareto optimal solution with the minimum total rolling force increment is calculated by a particle swarm optimization algorithm, and the Pareto optimal solution is used as a rolling force compensation parameter set.

[0091] Example 2

[0092] See also Figure 2 The present invention provides an embodiment: a method for monitoring and maintaining equipment status, which is applied to a steel pipe continuous rolling production line. The specific steps of the method are as follows:

[0093] S1: Collect the three-dimensional stress energy density distribution data of the roller bearing seat and predict the micro-crack growth trend of the roller surface based on the energy dissipation rate model;

[0094] In this embodiment, a deformation energy sensor array is used to collect real-time three-dimensional stress energy density distribution data on the roller bearing seat. Using an energy dissipation rate model and combining it with the roller's stress concentration areas to predict microcrack growth trends, the potential crack initiation locations on the roller surface can be accurately identified. Real-time monitoring of energy loss provides early warning of early fatigue cracks in the roller, enabling cracks to be addressed before they reach a certain size, thereby extending equipment life and reducing production interruptions caused by roller damage. This solves the problem of failure to promptly identify roller surface damage during traditional rolling processes and improves equipment maintenance efficiency.

[0095] S2: Generate dynamic remaining life index;

[0096] In this embodiment, a dynamic remaining life index (RMLI) is generated based on the crack growth trend data predicted in S1 and the actual operating conditions of the rolls. This index not only reflects the damage state of the rolls but also incorporates the properties of the roll material to accurately estimate their remaining service life under the current load. This allows real-time monitoring of the health of the rolls, providing timely data support for maintenance and avoiding emergency shutdowns and unnecessary repairs caused by unclear equipment status. This provides predictable equipment maintenance cycles for steel pipe production lines, effectively reducing unplanned downtime and improving production continuity and stability.

[0097] S3: When the dynamic remaining life index is lower than the preset threshold, rolling force compensation is performed based on the tube cross-section ovality data;

[0098] In this embodiment, real-time monitoring of pipe cross-sectional ovality data identifies uneven wall thickness that may occur during the rolling process. When the ovality deviation exceeds the process tolerance, the hydraulic AGC system automatically activates to dynamically adjust the rolling force to ensure uniform force distribution. This effectively reduces sudden changes in pipe wall thickness caused by roll damage and avoids structural instability caused by uneven pipe wall thickness. This improves product quality during production, reduces production costs, and ensures the reliability of the high-frequency welded pipe unit during high-speed rolling of thin-walled pipes.

[0099] S4: Synchronously collects Barkhausen noise signals and cooling medium flow data, and adjusts the cooling intensity based on a real-time feedback loop;

[0100] In this embodiment, the amplitude and frequency domain characteristics of the Barkhausen noise signal are used to identify areas of abnormal phase change on the roll surface. This is then combined with coolant flow rate data to determine the effectiveness of the current cooling mode. For identified areas of uneven cooling, the coolant flow rate and injection angle are adjusted in real time to optimize the cooling intensity distribution, ensuring uniform cooling of the roll surface and avoiding material phase change issues caused by uneven cooling. This effectively suppresses material deformation on the roll surface caused by overheating or uneven cooling, improving rolling quality and preventing thermal cracking caused by insufficient local cooling.

[0101] During the continuous rolling of steel pipes, especially when thin-walled pipes are rolled at high speeds, conventional technologies still face challenges in addressing the energy efficiency loss caused by dynamic contact between the rolls and the tube blanks. In high-frequency pipe welding units in particular, the coupled failure of hidden roll surface damage and sudden changes in pipe wall thickness not only affects product quality but also increases equipment maintenance costs. Existing technologies primarily address this issue through experience or simple feedback adjustments, but they do not comprehensively analyze and optimize complex factors such as the propagation of microcracks on the roll surface and energy efficiency losses. Therefore, the key to solving this problem is how to reduce roll damage and achieve precise control of pipe wall thickness while ensuring production efficiency.

[0102] In this embodiment, the dynamic state of the rolls is precisely monitored by systematically combining multiple data sources (such as three-dimensional stress energy density distribution, ovality data, Barkhausen noise signals, and coolant flow rate). Intelligent adjustment is achieved through the use of advanced energy dissipation rate models, dynamic remaining life index generation, and rolling force compensation. By providing real-time feedback on the health of the rolls and the pipe, the system promptly identifies potential damage risks and adjusts rolling force and cooling intensity based on actual operating conditions, thus avoiding equipment failure and pipe quality issues caused by long-term neglect of microcrack growth and localized temperature unevenness.

[0103] Specifically, the system first collects the three-dimensional stress energy density distribution data of the roller bearing seat in real time through a multi-directional deformation energy sensor array, and predicts the growth trend of microcracks on the roller surface based on the energy dissipation rate model to generate a dynamic remaining life index. Through this method, the system can accurately grasp the health status of the roller and determine its remaining life, providing a scientific basis for subsequent compensation measures. At the same time, combined with the cross-sectional ovality data of the pipe, when the dynamic remaining life index falls below the preset threshold, the system automatically performs rolling force compensation to ensure that the roller pressure is evenly distributed and avoid local wall thickness mutations. This compensation not only targets the damage to the roller, but also comprehensively considers the geometric changes of the pipe, making the compensation effect more accurate and improving the rolling quality.

[0104] Furthermore, by synchronously collecting Barkhausen noise signals and cooling medium flow data, the system can dynamically adjust the cooling intensity under the action of a real-time feedback loop. This technology can accurately identify abnormal phase changes that may occur on the surface of the roller and judge the effectiveness of the cooling mode based on the flow data of the cooling medium. Through this technology, the unevenness in the cooling process is effectively suppressed, avoiding roller wear or material organization problems caused by local overheating or overcooling. Compared with traditional cooling control methods, the feedback loop of this application optimizes the distribution of cooling intensity, which not only improves the service life of the roller, but also improves the quality of rolled products.

[0105] The specific steps of S1 are as follows:

[0106] S1.1: Calculating local energy density distribution based on the three-dimensional stress energy density distribution data;

[0107] In this embodiment, a multi-directional deformation energy sensor array is first used to collect three-dimensional stress energy density distribution data of the roller bearing seat in real time. This data reflects the stress state and energy distribution in different areas of the roller surface during the rolling process.

[0108] Specifically, the 3D data is mapped to local areas to identify stress concentrations at different locations on the roll surface. Based on the 3D stress energy density data, spatial meshing technology is used to divide the entire roll surface into multiple small regions. The local stress energy density within each region is calculated to determine the local energy density distribution. Detailed modeling of the stress distribution at different locations on the roll surface provides basic data for predicting subsequent crack growth trends. High-risk areas of stress concentration are identified, providing precise spatial information for subsequent fatigue crack prediction, enabling more targeted maintenance work.

[0109] S1.2: Extracting the peak energy dissipation rate and spatial distribution entropy value based on the local energy density distribution, and establishing a mapping relationship between the energy threshold and the crack initiation location in combination with the fatigue crack growth rate test data of the roll material;

[0110] In this example, these two indicators reflect the concentration and spatial heterogeneity of energy consumption in a local area of ​​the roll surface during rolling, respectively. The peak energy dissipation rate reveals the risk of crack growth in stress-concentrated areas, while the spatial distribution entropy reflects the local energy distribution characteristics, providing important clues to the likelihood of crack growth.

[0111] Specifically, by conducting experiments on fatigue crack growth rates of different roll materials under varying stress conditions and combining them with energy data from actual operating conditions, the crack growth threshold and corresponding crack initiation location were calculated. The experimental data can be accurately mapped to the material's characteristics and actual operating conditions, ensuring the reliability of crack initiation predictions and providing more accurate input for the prediction model.

[0112] S1.3: Comparing the mapping relationship with the predicted relationship of the offline calibrated finite element simulation model, and correcting the time-varying parameters of the energy dissipation rate model using a residual-driven algorithm, wherein the time-varying parameters represent the crack growth coefficient, exponential factor, and crack growth activation energy of the damage accumulation characteristics of the roll material;

[0113] In this example, the finite element simulation model provides theoretical predictions of roll surface stress and crack propagation. Combined with the material's constitutive relationships, it can simulate the deformation and damage processes of the roll material under different operating conditions. However, due to numerous uncertainties in actual operation (such as variations in rolling force and temperature), the finite element simulation model's predictions may be biased. To further improve prediction accuracy, the model needs to be modified using a residual-driven algorithm.

[0114] Specifically, the residual-driven algorithm analyzes the error between the mapping relationship and the simulation model's predictions to correct the time-varying parameters in the energy dissipation rate model. These parameters, including the crack growth coefficient, exponential factor, and crack growth activation energy, characterize the damage accumulation characteristics of the roll material. This allows the model to more accurately reflect the damage evolution of the roll during actual production, improving the accuracy of crack growth trend predictions and effectively avoiding equipment failures and production downtime caused by excessive damage.

[0115] S1.4: Generate a crack growth trend prediction based on the modified energy dissipation rate model, wherein the crack growth trend prediction includes a probability distribution heat map of the crack initiation location and a time-varying growth rate curve of the crack length;

[0116] Specifically, the revised model first predicts a heat map of the probability distribution of crack initiation locations. This heat map shows the likelihood and risk of crack initiation at different locations. This heat map accurately identifies high-risk areas on the roll surface where crack propagation is most likely, providing data support for subsequent repairs and prevention. Secondly, a time-varying crack length propagation rate curve is generated, showing the crack's propagation rate over different time periods. Using this data, production line managers can monitor crack propagation in real time and take appropriate control measures based on the predicted results, effectively extending equipment life and reducing equipment downtime and production losses caused by crack propagation. These predictions allow managers to better monitor the health of the rolls, ensuring the stability of the rolling process and product quality.

[0117] The specific steps of S1.3 are as follows:

[0118] S1.3.1: Perform frequency domain decomposition on the residual signal between the mapping relationship and the prediction relationship to extract the energy dominant mode of the characteristic frequency band;

[0119] In this embodiment, the mapping relationship is the mapping between the energy threshold and the crack initiation location obtained in the previous steps, while the prediction relationship is the crack growth prediction result provided by the finite element simulation model. There may be errors between these two relationships, and these errors may not be easy to directly observe in the time domain. Therefore, frequency domain analysis is used to more clearly identify and separate these residual signals.

[0120] Specifically, frequency domain decomposition techniques such as Fast Fourier Transform (FFT) are used to convert the residual signal in the time domain into a frequency domain signal, and the energy distribution in different frequency bands is analyzed. In the frequency domain, the energy-dominant mode refers to the pattern in which the energy of the signal is concentrated within a specific frequency range. These modes are usually closely related to the damage progression of the roll. The purpose of extracting the energy-dominant mode of the characteristic frequency band is to identify the signal features that are most relevant to the crack propagation process. These features can help identify the dynamic process of crack propagation and provide accurate data support for subsequent correction steps.

[0121] S1.3.2: Perform sensitivity analysis on the time-varying parameters in the energy dissipation rate model based on the energy dominant mode, calculate the sensitivity weights of the time-varying parameters using the roll material constitutive equation, and generate a priority sequence;

[0122] In this example, after obtaining the energy-dominant mode, the next step is to perform a sensitivity analysis on the time-varying parameters in the energy dissipation rate model. Time-varying parameters such as the crack growth coefficient, exponential factor, and crack growth activation energy are key variables in characterizing the roll material damage process.

[0123] Specifically, the constitutive equation of the roll material is used to describe its mechanical behavior, and the influence of time-varying parameters on damage propagation is derived through factors such as the material's stress-strain relationship, temperature changes, and strain rate. The purpose of sensitivity analysis is to quantify the impact of these time-varying parameters on crack propagation prediction, and to determine which parameters have a significant impact on the crack propagation trend and which parameters may contribute less to the final prediction results. It can clarify which time-varying parameters should be adjusted first during the correction process, thereby improving the efficiency of the correction process. The generated priority sequence provides a reference for subsequent correction steps, ensuring that the optimization process can focus on the most influential parameters to achieve the best correction effect.

[0124] S1.3.3: Performing a staged Bayesian correction on the time-varying parameters according to the priority sequence, wherein the staged Bayesian correction includes material constraints, energy constraints, and set constraints;

[0125] In this embodiment, after obtaining the priority sequence, the next step is to perform a phased Bayesian correction of the time-varying parameters. Based on Bayes' theorem, the Bayesian correction method gradually updates the model parameters by combining prior knowledge with observed data. The core of this phased correction is that, at different stages of the correction, more data and constraints are gradually introduced to gradually approach the final corrected value.

[0126] Specifically, for time-varying parameters, material constraints are first used to limit the correction range of the parameters. These constraints come from the inherent properties of the material, such as elastic modulus, yield strength, etc., to ensure that the physical properties of the material itself will not be violated during the correction process. Secondly, energy constraints ensure that the energy dissipation rate does not change abnormally during the correction process, ensuring the physical rationality of the entire process. Finally, the introduction of set constraints is to coordinate the interactions between different time-varying parameters, ensuring that these parameters can remain consistent during the correction process, and avoiding unreasonable correction results. Through this staged Bayesian correction method, time-varying parameters can be accurately optimized, the prediction accuracy of the model can be improved, and the uncertainty of the model can be reduced, thereby obtaining a more accurate prediction of crack propagation trends.

[0127] S1.3.4: Iteratively modify the time-varying parameters until the number of iterations is satisfied;

[0128] Specifically, the iterative correction process is to optimize the model by continuously updating the parameter values ​​and gradually reducing the residual between the model prediction value and the actual observation value. In each iteration, the time-varying parameters will be adjusted according to the sensitivity weights generated in the previous steps and the priority of the Bayesian correction, so that each step of the correction can be closer to the actual damage state. As the number of iterations increases, the prediction accuracy of the model continues to improve, and eventually a stable parameter solution can be obtained. This solution fully considers the actual evolution process of the roll damage and can accurately predict the crack propagation trend based on the current rolling conditions. Through this iterative process, it is ensured that the time-varying parameters can be continuously optimized in a dynamically changing environment, ultimately providing reliable crack propagation prediction results.

[0129] The specific steps of S2 are as follows:

[0130] S2.1: performing a coupled analysis on the probability distribution heat map and the time-varying growth rate curve to extract the credibility weight of the crack initiation location and the probability density function of the growth rate;

[0131] In this example, the probability distribution heatmap reflects the probability distribution of crack initiation at different locations, while the time-varying growth rate curve shows the crack growth rate over different time periods. By combining these two data sources, a more detailed and accurate analysis of the crack growth process can be achieved.

[0132] Specifically, the system identifies areas with high crack initiation probability in the thermal map and, based on the locations of these areas, extracts the corresponding crack growth velocity data from the time-varying growth rate curve. In other words, when a crack at a specific location is marked as a high-risk area, the system extracts the crack growth velocity data at that location from the growth rate curve. These two data sources are combined through the corresponding relationship between position and time to derive a new crack growth prediction model. By dynamically combining the crack initiation probability at the crack location with the crack growth rate, a more detailed modeling of the crack evolution process can be achieved, rather than simply analyzing crack occurrence or growth. Therefore, the results obtained from this coupled analysis provide the probability density function of the crack initiation location and the crack growth rate at that location in a specific area, which is crucial for further remaining life prediction. This allows accurate capture of the initial crack growth trend during actual rolling and dynamic prediction of crack growth behavior in future time periods, providing a scientific basis for subsequent maintenance decisions.

[0133] S2.2: Generate a probability distribution interval of the remaining useful life through Monte Carlo simulation based on the credibility weight and the probability density function;

[0134] In this embodiment, Monte Carlo simulation is used to generate a probability distribution interval of the remaining life. This process involves generating multiple possible remaining life scenarios through multiple random sampling and iterative calculations, and deriving an overall probability distribution interval from them.

[0135] Specifically, the system performs multiple sampling based on the confidence weights of the crack initiation locations and the probability density function of the crack growth rate. The confidence weights reflect the probability of crack initiation at different locations, while the probability density function of the growth rate describes the distribution of crack growth rates. In the Monte Carlo simulation, multiple possible crack initiation locations are first randomly sampled from the probability distribution of crack initiation locations. Then, samples are taken based on the crack growth rate probability density function for each location. Each sampling generates a combination of a specific location and a corresponding rate, representing the crack growth at that specific location.

[0136] Furthermore, in each simulation, these sampled results are combined to construct a complete crack propagation path, including the crack's growth process over time. These paths are then used to calculate the remaining life of the crack. Specifically, each path is simulated over time, until the crack reaches a critical size or the roll surface reaches a preset fatigue threshold, thereby determining the remaining life of the crack. The result of each simulation represents a possible crack growth scenario, and ultimately all simulation results form a set of remaining life values.

[0137] Furthermore, through multiple random sampling and simulation rounds, the system generates a set of remaining life distribution data, from which a probability distribution interval can be calculated. This probability distribution interval reflects the remaining useful life of the roll under different production conditions and supports subsequent decision-making. For example, these simulation results can be used to generate a confidence interval for the remaining life, indicating the remaining life of the roll within a certain probability range under given production conditions.

[0138] Furthermore, by using Monte Carlo simulation to simulate this process, various uncertainties in actual production (such as stress fluctuations, temperature changes, rolling force differences, etc.) can be taken into account, thereby providing a more comprehensive and accurate remaining life prediction.

[0139] S2.3: Match the probability distribution interval with the order rolling mileage requirements in the production plan database, calculate the remaining life confidence level that meets the current production task requirements, and generate a dynamic remaining life index;

[0140] In this embodiment, the production planning database contains specific production task requirements, such as the rolling mileage and time required for each order. By matching the probability distribution interval of the remaining life with these production requirements, the confidence level of the current roll's remaining life when completing a specific production task can be determined.

[0141] Specifically, based on the rolling mileage required for the current production task and the distribution of remaining life, the system calculates whether the rolls will meet the requirements when completing the task, thereby assessing whether the rolls need maintenance or replacement. The core principle is to provide real-time decision support for the production process by comparing the probability distribution of remaining life with the actual production task requirements. This not only improves production line efficiency but also effectively reduces the costs and risks associated with premature or delayed equipment maintenance. Furthermore, the generated dynamic remaining life index can be provided as a real-time indicator for production management personnel.

[0142] The specific steps for S3 are as follows:

[0143] S3.1: Divide the tube cross-section ovality data into N detection areas according to the circumferential angle, calculate the wall thickness deviation rate of each area, and generate the ovality feature matrix;

[0144] In this step, it is first necessary to divide the ovality data of the pipe cross section according to the circumferential angle, and divide the cross section of the entire pipe into several small areas. The number of these small areas is N. The specific setting of N can be determined by a technician in this field through a large number of repeated experiments. Each area covers a certain angle range to ensure that the ovality changes of each part of the pipe can be reflected in detail. Ovality refers to the deviation of the pipe cross section caused by uneven rolling force or other factors during the rolling process, which is usually manifested as the non-circular degree of the pipe cross-section shape. By dividing these areas, the ovality changes of each area can be accurately monitored. Next, for each divided area, the wall thickness deviation rate of each area is calculated by measuring the deviation between the actual value of the pipe wall thickness and the standard wall thickness value. This process can reflect the uneven wall thickness or deformation that may occur in the pipe during the rolling process, and provide accurate data support for subsequent rolling force compensation. By generating an ovality characteristic matrix, the comprehensive situation of the wall thickness deviation and ovality of each area can be obtained. This matrix becomes the key data source for subsequent rolling force adjustment and optimization. By accurately monitoring the geometric deformation of the pipe, the uniformity of the wall thickness during the rolling process is ensured, the occurrence of pipe quality defects is avoided, and production efficiency and product quality are improved.

[0145] S3.2: Convert the dynamic remaining life index into a roll health weight;

[0146] Specifically, the system converts the dynamic remaining life index (DLI) into a roll health weight. The DLI reflects the remaining service life of the roll under its current production conditions and takes into account the growth trend of microcracks on the roll surface and other influencing factors. Analysis of this index yields a quantitative indicator of the roll's health—the roll health weight. The roll health weight represents the current health level of the roll, with higher values ​​indicating a healthier roll and lower values ​​indicating a higher risk of damage. During this process, the system uses a weighting function based on the roll's dynamic remaining life and health status to convert the DLI into a health weight. This allows the actual health of the roll to be used as input for the subsequent calculation of the rolling force compensation model. By quantifying the roll's health, a scientific basis is provided for accurate rolling force compensation, preventing excessive wear or quality defects caused by roll damage during the rolling process.

[0147] S3.3: Construct a rolling force compensation model based on the equipment health status, use the ellipticity characteristic matrix and the roll health weight as inputs of the rolling force compensation model, perform optimization through the rolling force compensation model, and calculate a rolling force compensation parameter set, specifically including:

[0148] generating an initial compensated pressure distribution using the ellipticity characteristic matrix as input;

[0149] Using the health weight of the roll as a health weight constraint condition;

[0150] Calculating the Pareto optimal solution with the minimum total rolling force increment by using a particle swarm optimization algorithm, and using the Pareto optimal solution as a rolling force compensation parameter set;

[0151] In this step, the system will construct a rolling force compensation model based on the ovality characteristic matrix and the roll health weight. The ovality characteristic matrix contains the wall thickness deviation and ovality data of each area of ​​the pipe, which reveals the changes in the geometric shape of the pipe during the rolling process. The roll health weight provides a quantitative assessment of the current health status of the roll. The two together serve as input data into the rolling force compensation model. The core purpose of the rolling force compensation model is to dynamically adjust the distribution of the rolling force according to the geometric changes of the pipe and the health status of the roll, ensuring that the rolling force is evenly distributed throughout the rolling process, thereby achieving precise control of the wall thickness of the pipe. The compensation model optimizes the rolling force and adjusts the pressure applied by each roll to avoid excessive compression or stress concentration in local areas, thereby ensuring the uniformity of the wall thickness of the pipe and the service life of the roll.

[0152] Next, the system uses the particle swarm optimization (PSO) algorithm to optimize the rolling force compensation model and calculate the rolling force compensation parameter set. PSO is an optimization method that simulates the behavior of particle swarms in nature and can effectively search for the optimal solution in a multidimensional parameter space. During this process, the PSO algorithm performs multiple iterative calculations within the rolling force adjustment parameter space based on the input data of the rolling force compensation model (i.e., the ellipticity characteristic matrix and the roll health weight), searching for the optimal solution that minimizes the total rolling force increment. Through this optimization, the system is able to calculate the rolling force compensation parameter set that best suits the current production status. This parameter set will be used to adjust the rolling force distribution in real time, thereby ensuring pipe quality and roll health during the production process.

[0153] Furthermore, the particle swarm optimization algorithm calculates a Pareto optimal solution through multiple iterations of the rolling force compensation model. A Pareto optimal solution refers to a solution in a multi-objective optimization problem that cannot improve one objective without sacrificing other objectives. In this embodiment, the Pareto optimal solution represents the optimal solution that can balance the health of the rolls, the uniformity of the pipe wall thickness, and the minimum increment of the rolling force in the rolling force compensation model. Through this optimized solution, the system can ensure that the pressure applied by each roll can meet production requirements while minimizing the increment of the rolling force, thereby effectively reducing the loss of the rolls and pipes. Ultimately, these calculated parameter sets will be used to adjust the rolling force in real time to ensure the smooth progress of the entire rolling process.

[0154] The adjusting the cooling intensity according to the real-time feedback loop comprises:

[0155] The amplitude and frequency domain characteristics of the Barkhausen noise signal are used to identify the local magnetic domain changes of the roller, and a mapping relationship is established between the abnormal phase change characteristics of the roller surface.

[0156] When the abnormal phase change characteristics indicated by the mapping relationship exceed the process allowable range, the specific location and extent of the abnormal roll area are determined by extracting the multimodal features of the Barkhausen noise signal;

[0157] According to the specific location and extent of the abnormal roll area, the cooling nozzle combination opening strategy is adjusted;

[0158] The adjusting the cooling intensity according to the real-time feedback loop further includes:

[0159] determining the effectiveness of the current cooling mode according to the cooling medium flow data;

[0160] When the cooling medium flow rate data deviates locally from the set value, the local change in the cooling medium flow rate is correlated to determine the spatial area with uneven cooling distribution;

[0161] According to the spatial area where the cooling is unevenly distributed, the cooling nozzle combination opening strategy is adjusted.

[0162] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An equipment status monitoring and maintenance system, applied to a steel pipe continuous rolling production line, characterized in that: The system includes a data acquisition module, a data analysis module and an equipment maintenance module, wherein: The data acquisition module is used to collect three-dimensional stress energy density distribution data, Barkhausen noise signal and cooling medium flow data of the roller bearing seat; The data analysis module establishes an energy dissipation rate model based on the three-dimensional stress energy density distribution data, predicts the microcrack growth trend of the roll surface and generates a dynamic remaining life index. At the same time, based on the amplitude and frequency domain characteristics of the Barkhausen noise signal, it establishes a mapping relationship between the local magnetic domain changes of the roll and the abnormal phase change characteristics. It also determines the effectiveness of the current cooling mode based on the cooling medium flow data and identifies the cooling abnormality area. The equipment maintenance module compensates for the roll reduction amount according to the output of the data analysis module and adjusts the opening strategy of the cooling nozzle combination.

2. The equipment status monitoring and maintenance system according to claim 1, characterized in that: The data analysis module includes: The crack prediction unit builds an energy dissipation rate model based on three-dimensional stress energy density distribution data, dynamically corrects time-varying parameters through a residual-driven algorithm, and generates a thermal map of the crack initiation location and a time-varying growth rate curve; The remaining life analysis unit is used to couple crack prediction results with production condition data, generate the remaining life probability distribution interval through Monte Carlo simulation, and generate a dynamic remaining life index based on order requirements; The cooling intensity analysis unit is used to analyze the magnetic domain-phase change mapping relationship of the Barkhausen noise signal and identify abnormal phase change areas and cooling failure areas based on the cooling medium flow distribution characteristics.

3. The equipment status monitoring and maintenance system according to claim 2, characterized in that: The crack prediction unit is configured with prediction logic, which includes: Calculating local energy density distribution based on the three-dimensional stress energy density distribution data; Extracting the energy dissipation rate peak and the spatial distribution entropy value based on the local energy density distribution, and establishing a mapping relationship between the energy threshold and the crack initiation position in combination with the fatigue crack growth rate test data of the roll material; Comparing the mapping relationship with the predicted relationship of the offline calibrated finite element simulation model, and correcting the time-varying parameters of the energy dissipation rate model through a residual-driven algorithm, wherein the time-varying parameters characterize the crack growth coefficient, exponential factor, and crack growth activation energy of the damage accumulation characteristics of the roll material; A crack growth trend prediction is generated based on the modified energy dissipation rate model, wherein the crack growth trend prediction includes a probability distribution heat map of the crack initiation position and a time-varying growth rate curve of the crack length.

4. The equipment status monitoring and maintenance system according to claim 3, characterized in that: The method of correcting the time-varying parameters of the energy dissipation rate model by using a residual driving algorithm includes: Perform frequency domain decomposition on the residual signal between the mapping relationship and the prediction relationship to extract the energy dominant mode of the characteristic frequency band; performing sensitivity analysis on the time-varying parameters in the energy dissipation rate model according to the energy dominant mode, calculating the sensitivity weights of the time-varying parameters through the constitutive equation of the roll material, and generating a priority sequence; Performing a staged Bayesian correction on the time-varying parameters according to the priority sequence, wherein the staged Bayesian correction includes material constraints, energy constraints, and set constraints; The time-varying parameters are iteratively modified until a predetermined number of iterations is met.

5. The equipment status monitoring and maintenance system according to claim 3, characterized in that: The remaining life analysis unit is configured with life analysis logic, and the life analysis logic includes: Couple the probability distribution heat map and the time-varying expansion rate curve to extract the credibility weight of the crack initiation position and the probability density function of the expansion rate; Generating a probability distribution interval of the remaining life through Monte Carlo simulation according to the credibility weight and the probability density function; The probability distribution interval is matched with the order rolling mileage requirements in the production plan database, the remaining life confidence level that meets the current production task requirements is calculated, and a dynamic remaining life index is generated.

6. The equipment status monitoring and maintenance system according to claim 2, characterized in that: The cooling intensity analysis unit is configured with cooling identification logic, which includes: The amplitude and frequency domain characteristics of the Barkhausen noise signal are used to identify the local magnetic domain changes of the roller, and a mapping relationship is established between the abnormal phase change characteristics of the roller surface. When the abnormal phase change characteristics indicated by the mapping relationship exceed the process allowable range, the position and extent of the abnormal roll area are determined by extracting the multimodal features of the Barkhausen noise signal.

7. The equipment status monitoring and maintenance system according to claim 6, characterized in that: The cooling identification logic further includes: determining the effectiveness of the current cooling mode according to the cooling medium flow data; When the cooling medium flow rate data locally deviates from the set value, the local change in the cooling medium flow rate is correlated to determine the spatial region with uneven cooling distribution.

8. The equipment status monitoring and maintenance system according to claim 1, characterized in that: The equipment maintenance module includes: A rolling force compensation unit, configured to receive ovality detection data and a dynamic remaining life index and generate a rolling force compensation parameter set; The cooling intensity adjustment unit adjusts the nozzle opening and closing combination and flow distribution according to the abnormal phase change area and cooling failure area.

9. The equipment status monitoring and maintenance system according to claim 8, characterized in that: The rolling force compensation unit is provided with compensation logic, which includes: The ovality data of the pipe section is divided into N detection areas according to the circumferential angle, the wall thickness deviation rate of each area is calculated, and the ovality feature matrix is ​​generated; converting the dynamic remaining life index into a roll health weight; A rolling force compensation model based on the health status of the equipment is constructed, the ellipticity characteristic matrix and the roll health weight are used as inputs of the rolling force compensation model, optimization is performed through the rolling force compensation model, and a rolling force compensation parameter set is calculated.

10. The equipment status monitoring and maintenance system according to claim 9, characterized in that: Optimizing the rolling force compensation model to calculate a rolling force compensation parameter set includes: generating an initial compensated pressure distribution using the ellipticity characteristic matrix as input; Using the health weight of the roll as a health weight constraint condition; The Pareto optimal solution with the minimum total rolling force increment is calculated by a particle swarm optimization algorithm, and the Pareto optimal solution is used as a rolling force compensation parameter set.

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