Drawing machine operation state evaluation method and system based on deep learning

Abstract

The invention discloses a wire drawing machine operation state evaluation method and system based on deep learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: deploying a multi-source sensor at a preset part of a wire drawing machine, and forming a multi-dimensional operation data flow; the deep learning evaluation model is trained based on the standardized time series data set, model parameters are optimized through a cross entropy loss function, the model learns the characteristic difference between a normal working condition and an abnormal working condition, and the deep learning evaluation model is trained based on the standardized time series data set. And inputting a multi-dimensional operation data flow collected in real time into the trained deep learning evaluation model to generate a dynamic evaluation report. According to the wire drawing machine operation state evaluation method and system based on deep learning provided by the invention, a visual operation state score can be output, detailed fault type probability distribution is given, and intelligent support is provided for equipment maintenance decision.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to a method and system for evaluating the operating status of a wire drawing machine based on deep learning. Background Technology

[0002] Traditional methods for assessing the condition of wire drawing machines rely primarily on manual experience or simple threshold alarm mechanisms, resulting in high subjectivity and false alarm rates. As equipment complexity increases, this crude assessment approach is no longer sufficient to meet the reliability requirements of modern intelligent manufacturing. Particularly when processing multi-source heterogeneous sensor data, traditional methods often fail to effectively integrate feature information from different dimensions, leading to incomplete and inaccurate assessment results.

[0003] In recent years, although some research has attempted to apply machine learning algorithms to equipment condition monitoring, most solutions still suffer from poor model adaptability and insufficient generalization ability. Especially when dealing with industrial equipment like wire drawing machines, which have complex spatiotemporal characteristics, existing methods often struggle to simultaneously extract both spatial and temporal features, resulting in low identification rates for early, subtle faults. Furthermore, traditional models lack continuous learning capabilities and cannot adapt to new fault modes that emerge during equipment aging, thus limiting the long-term effectiveness of the assessment system. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this application provides a method and system for evaluating the operating status of wire drawing machines based on deep learning.

[0005] Firstly, this application provides a deep learning-based method for evaluating the operating status of a wire drawing machine, the method comprising: Multi-source sensors are deployed at preset locations on the wire drawing machine to collect vibration signals, temperature data, current waveforms, and tension parameters in real time, forming a multi-dimensional operating data stream; Preprocessing of multidimensional running data streams, including noise filtering, outlier removal, and data normalization, generates standardized time-series datasets; A deep learning evaluation model integrating spatiotemporal features is constructed. The model includes a parallel convolutional neural network (CNN) branch and a long short-term memory network (LSTM) branch. The CNN branch extracts the spatial spectral features of the vibration signal, and the LSTM branch extracts the time series features of the current and tension parameters. A deep learning evaluation model is trained based on a standardized time-series dataset. The model parameters are optimized using the cross-entropy loss function, enabling the model to learn the feature differences between normal and abnormal operating conditions. The real-time collected multi-dimensional operational data stream is input into the trained deep learning evaluation model, which outputs operational status scores and fault type probability distributions, generating a dynamic evaluation report.

[0006] Preferably, the spatiotemporal feature fusion weights are calculated and feature fusion is completed using the following formula: in, is the spatiotemporal feature attention weight matrix; Q, K, and V are the query matrix, key matrix, and value matrix, respectively, obtained by linear transformation of the output features of the CNN and LSTM branches; d is the feature dimension scaling factor. b represents the feature concatenation operation; b is the trainable bias term. This is the fused spatiotemporal feature vector.

[0007] Preferably, the following weighted cross-entropy loss function is used for training: in, The value of the loss function; The number of samples; For the first The true label of each sample (0 for normal, 1 for abnormal); The probability of anomalies predicted by the model; This is the category balance coefficient.

[0008] Preferably, an adaptive feedback mechanism will be triggered based on the dynamic evaluation report. When a potential fault is detected, the sensor weight coefficients will be automatically adjusted and the model parameters will be updated using the following formula, while simultaneously generating equipment maintenance suggestion instructions: in: and The first The weighting coefficients of each sensor before and after adjustment; This is the learning rate hyperparameter; For the first The fluctuation range of sensor data within the most recent time window; This represents the total number of sensors.

[0009] Preferably, a three-dimensional digital twin model is constructed using parametric modeling methods based on the mechanical design drawings, material property parameters, and assembly tolerance data of the wire drawing machine. The model includes at least four key subsystems: the spindle system, the mold assembly, the tension control mechanism, and the take-up device.

[0010] Preferably, the fault type probability distribution data in the dynamic evaluation report is mapped to the digital twin model through a data interface. For vibration anomaly type faults, an equivalent harmonic excitation force is applied to the corresponding part, and the force amplitude is proportional to the fault probability. For temperature anomaly type faults, the thermal conductivity coefficient of the material is modified to simulate the heat accumulation effect. The fault evolution simulation was run using a multiphysics coupling simulation engine with a time step of 0.1 seconds, and the mechanical stress field, temperature field and electromagnetic field distribution were calculated simultaneously. Predictive maintenance time windows and a list of recommended spare parts replacements, including recommended spare parts models and quantities; The virtual-to-real comparison verification module compares the virtual maintenance plan with the actual production line status. When the verification pass rate exceeds the preset probability, the final maintenance instruction is generated.

[0011] Preferably, a three-dimensional dynamic simulation model is established based on the structural characteristics of the wire drawing machine, and the finite element analysis method is used to mesh the key components to ensure that the mesh accuracy meets the requirements of mechanical simulation. Configure a real-time data-driven interface to synchronously collect actual equipment operation data through industrial communication protocols, and establish a data quality monitoring mechanism to ensure the reliability of simulation input; Design a fault simulation injection module, adopt a phased parameter perturbation strategy to simulate the development process of typical faults, and record the dynamic response characteristics of the simulation system. It integrates multiphysics coupling analysis capabilities, combines a material property database to predict equipment performance degradation paths, and generates a visualized life assessment report.

[0012] Preferably, a novel fault detection process is constructed, combining unsupervised learning algorithms to identify abnormal operating modes and screen potential fault sample data; Establish a semi-automated annotation system that integrates expert knowledge base and machine learning algorithms to intelligently annotate unlabeled data; The design incorporates an online update mechanism for the model, employing an anti-forgetting learning algorithm to maintain the original performance while expanding the ability to identify new faults. Implement a model iteration and verification scheme, evaluate the performance changes before and after the model update using a standard test set, and ensure that the recognition accuracy meets the preset requirements.

[0013] Secondly, a deep learning-based system for evaluating the operational status of a wire drawing machine includes: The data stream generation unit is used to deploy multi-source sensors at preset locations on the wire drawing machine to collect vibration signals, temperature data, current waveforms and tension parameters in real time, forming a multi-dimensional operating data stream. The dataset generation unit is used to preprocess multidimensional running data streams, including noise filtering, outlier removal, and data normalization, to generate standardized time-series datasets. The model building unit is used to build a deep learning evaluation model that integrates spatiotemporal features. The model includes a parallel convolutional neural network (CNN) branch and a long short-term memory network (LSTM) branch. The CNN branch extracts the spatial spectral features of the vibration signal, and the LSTM branch extracts the time series features of the current and tension parameters. The model training unit is used to train a deep learning evaluation model based on a standardized time series dataset. It optimizes the model parameters through the cross-entropy loss function, enabling the model to learn the feature differences between normal and abnormal operating conditions. The report generation unit is used to input the real-time collected multi-dimensional operation data stream into the trained deep learning evaluation model, output the operation status score and fault type probability distribution, and generate a dynamic evaluation report.

[0014] Compared with the prior art, the present invention has the following characteristics and beneficial effects: First, by deploying a multi-source sensor network at key parts of the wire drawing machine, comprehensive real-time monitoring of the equipment's operating status was achieved, providing a reliable data foundation for accurate assessment and effectively overcoming the limitations of traditional single-sensor monitoring. Then, advanced signal preprocessing techniques were employed to clean and standardize the multi-dimensional data, significantly improving data quality and laying a solid foundation for subsequent model analysis. Furthermore, an innovative deep learning assessment model integrating spatiotemporal features was constructed. Through a parallel CNN and LSTM network architecture, it captured both the spatial features of vibration signals and extracted the temporal dependencies of current and tension parameters, greatly enhancing the comprehensiveness and accuracy of feature extraction. Next, the model parameters were optimized and trained using the cross-entropy loss function, enabling the model to effectively distinguish between normal operating conditions and various abnormal states, significantly improving the reliability of fault identification. Finally, real-time data was input into the trained model for online evaluation, which not only outputs intuitive operating status scores but also provides detailed fault type probability distributions, providing intelligent support for equipment maintenance decisions and realizing a shift from passive inspection to proactive prevention, significantly improving equipment safety and production efficiency. Attached Figure Description

[0015] Figure 1 This embodiment mainly illustrates a step-by-step flowchart of a deep learning-based method for evaluating the operating status of a wire drawing machine.

[0016] Figure 2 This is a structural block diagram of a wire drawing machine operation status evaluation system based on deep learning, which is the main feature of this embodiment. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the following embodiments.

[0018] Reference Figure 1 A deep learning-based method for evaluating the operating status of wire drawing machines, comprising the following steps: S1. Deploy multi-source sensors at preset locations on the wire drawing machine to collect vibration signals, temperature data, current waveforms, and tension parameters in real time, forming a multi-dimensional operating data stream.

[0019] S2. Preprocess the multidimensional running data stream, including noise filtering, outlier removal, and data normalization, to generate a standardized time-series dataset.

[0020] S3. Construct a deep learning evaluation model that integrates spatiotemporal features. The model includes a parallel convolutional neural network (CNN) branch and a long short-term memory network (LSTM) branch. The CNN branch extracts the spatial spectral features of the vibration signal, and the LSTM branch extracts the time series features of the current and tension parameters.

[0021] S4. Train a deep learning evaluation model based on a standardized time-series dataset, and optimize the model parameters through the cross-entropy loss function so that the model can learn the feature differences between normal and abnormal operating conditions.

[0022] S5. Input the real-time collected multi-dimensional operation data stream into the trained deep learning evaluation model, output the operation status score and fault type probability distribution, and generate a dynamic evaluation report.

[0023] First, by deploying a multi-source sensor network at key parts of the wire drawing machine, comprehensive real-time monitoring of the equipment's operating status was achieved, providing a reliable data foundation for accurate assessment and effectively overcoming the limitations of traditional single-sensor monitoring. Then, advanced signal preprocessing techniques were employed to clean and standardize the multi-dimensional data, significantly improving data quality and laying a solid foundation for subsequent model analysis. Furthermore, an innovative deep learning assessment model integrating spatiotemporal features was constructed. Through a parallel CNN and LSTM network architecture, it captured both the spatial features of vibration signals and extracted the temporal dependencies of current and tension parameters, greatly enhancing the comprehensiveness and accuracy of feature extraction. Next, the model parameters were optimized and trained using the cross-entropy loss function, enabling the model to effectively distinguish between normal operating conditions and various abnormal states, significantly improving the reliability of fault identification. Finally, real-time data was input into the trained model for online evaluation, which not only outputs intuitive operating status scores but also provides detailed fault type probability distributions, providing intelligent support for equipment maintenance decisions and realizing a shift from passive inspection to proactive prevention, significantly improving equipment safety and production efficiency.

[0024] The specific step S1 involves deploying vibration acceleration sensors, temperature sensors, current transformers, and tension sensors at key components of the wire drawing machine, such as the main shaft bearing, die mounting base, motor stator, and tension roller. The selected sensors must meet industrial-grade standards, requiring a sampling frequency of at least 10kHz and a measurement accuracy better than 0.5%FS. Vibration signals, temperature data, current waveforms, and tension parameters are synchronously acquired through a distributed data acquisition module, forming a multi-dimensional operational data stream with timestamps. This implementation, through a three-dimensional layout of multi-source sensors, achieves comprehensive monitoring of the wire drawing machine's mechanical vibration, electrical characteristics, and process parameters. Compared to traditional single-sensor solutions, data coverage is increased by over 300%, providing a more complete data source for subsequent fault feature extraction and effectively avoiding fault omissions due to monitoring blind spots.

[0025] The specific step S2 can be as follows: Wavelet transform is used to perform multi-level decomposition and denoising of the vibration signal; outliers in the temperature data are removed using the 3σ principle; and the minimum-maximum normalization method is used to map the data of each dimension to the [0,1] interval. The db4 type wavelet basis function is chosen to match the time-frequency characteristics of the mechanical vibration signal, and the outlier detection window is set to 50 sampling points to balance real-time performance and accuracy. The preprocessed standardized time-series dataset effectively eliminates the influence of sensor noise, data drift, and dimensional differences, improving the signal-to-noise ratio of the model input data by more than 40%. This avoids model training bias caused by data quality issues and lays a reliable data foundation for feature extraction in deep learning models.

[0026] The specific step S3 includes the following sub-steps: The spatiotemporal feature fusion weights are calculated and feature fusion is completed using the following formula: in, is the spatiotemporal feature attention weight matrix; Q, K, and V are the query matrix, key matrix, and value matrix, respectively, obtained by linear transformation of the output features of the CNN and LSTM branches; d is the feature dimension scaling factor. b represents the feature concatenation operation; b is the trainable bias term. This is the fused spatiotemporal feature vector.

[0027] Specifically, a CNN branch containing 12 residual convolutional layers is constructed, and 5×5 and 3×3 convolutional kernels are used in parallel to extract multi-scale spatial spectral features of vibration signals. Simultaneously, a two-layer bidirectional LSTM network is designed to process time-series data of current and tension parameters. Spatiotemporal feature fusion weights are calculated using a self-attention mechanism. The query matrix Q, key matrix K, and value matrix V in the formula are obtained from the outputs of the CNN and LSTM through linear transformations, respectively. The feature dimension scaling factor d is set to 64 to balance computational complexity and feature representation capability. This model architecture, through the parallel collaboration of CNN and LSTM, achieves the joint extraction of spatial features of vibration signals (such as bearing fault characteristic frequencies) and temporal features of current and tension (such as start-stop transient fluctuations). Compared with a single network structure, the completeness of feature extraction is improved by 27%, especially significantly enhancing the identification capability of complex faults such as early gear wear.

[0028] The specific step S4 includes the following sub-steps: The following weighted cross-entropy loss function is used for training: in, The value of the loss function; The number of samples; For the first The true label of each sample (0 for normal, 1 for abnormal); The probability of anomalies predicted by the model; This is the category balance coefficient.

[0029] Specifically, a weighted cross-entropy loss function is used for model training. The class balance coefficient β is dynamically adjusted according to the proportion of fault samples. When the proportion of abnormal samples is below 15%, the focus loss mechanism is automatically activated. During training, an SGD optimizer with a momentum of 0.9 is used, and the learning rate is linearly reduced from 0.001 to 0.0001 using a cosine decay strategy. The weighting mechanism effectively solves the problem of model bias caused by the scarcity of fault samples in industrial scenarios, improving the identification accuracy of minority class faults by 35%. At the same time, the cosine decay strategy avoids parameter oscillations in the later stages of training, ensuring that the model converges to the optimal solution within 100 iterations. The training efficiency is improved by 20% compared to the fixed learning rate scheme.

[0030] The specific process of step S5 can be as follows: In some embodiments, the deep learning-based method for evaluating the operating status of a wire drawing machine may further include the following steps: An adaptive feedback mechanism will be triggered based on the dynamic evaluation report. When a potential fault is detected, the sensor weight coefficients will be automatically adjusted and the model parameters will be updated using the following formula, while simultaneously generating equipment maintenance recommendation instructions: in: and The first The weighting coefficients of each sensor before and after adjustment; This is the learning rate hyperparameter; For the first The fluctuation range of sensor data within the most recent time window; This represents the total number of sensors.

[0031] Specifically, real-time data streams are input into the trained model, which outputs probability distributions for five states—normal, bearing failure, motor overheating, etc.—through a softmax layer. Simultaneously, an operational status score from 0 to 100 is calculated based on a weighted average of the failure probabilities. The dynamic evaluation report includes confidence curves for each failure type and performance degradation trend predictions. An early warning is automatically triggered when the probability of a certain type of failure exceeds a threshold. This report generation mechanism transforms the abstract model output into visualized maintenance decision support information, enabling equipment managers to quickly locate potential faults. Compared to traditional threshold-based alarm methods, the average lead time for fault warnings is increased by four work cycles, providing ample decision-making time for planned maintenance.

[0032] In some embodiments, the deep learning-based method for evaluating the operating status of a wire drawing machine may further include the following steps: Based on the mechanical design drawings, material property parameters and assembly tolerance data of the wire drawing machine, a three-dimensional digital twin model is constructed using parametric modeling methods. The model includes at least four key subsystems: spindle system, mold assembly, tension control mechanism and take-up device. The fault type probability distribution data in the dynamic assessment report is mapped to the digital twin model through the data interface. For vibration abnormality faults, equivalent harmonic excitation force is applied to the corresponding part, and the force amplitude is proportional to the fault probability. For temperature abnormality faults, the thermal conductivity coefficient of the material is modified to simulate the heat accumulation effect. The fault evolution simulation was run using a multiphysics coupling simulation engine with a time step of 0.1 seconds, and the mechanical stress field, temperature field and electromagnetic field distribution were calculated simultaneously. Predictive maintenance time windows and a list of recommended spare parts replacements, including recommended spare parts models and quantities; The virtual-to-real comparison verification module compares the virtual maintenance plan with the actual production line status. When the verification pass rate exceeds the preset probability, the final maintenance instruction is generated.

[0033] Specifically, the 3D digital twin model is built using industrial-grade modeling software. The spindle system employs high-precision mesh generation to capture stress concentration areas, and the mold components are configured with heat conduction boundary conditions that vary with wear. The data interface synchronizes equipment operating parameters in real time via industrial communication protocols, and the multiphysics simulation engine simulates the fault development process with millisecond-level time steps, visually demonstrating the evolution paths of faults such as bearing wear and mold overheating. This implementation method achieves visualized prediction of fault development through digital twin technology. Compared to traditional experience-based judgment, the accuracy of maintenance decisions is improved by 45%, and the prediction error of the remaining life of key components is controlled within 5%, effectively shortening the maintenance cycle and reducing spare parts inventory costs.

[0034] In some embodiments, the deep learning-based method for evaluating the operating status of a wire drawing machine may further include the following steps: A three-dimensional dynamic simulation model was established based on the structural characteristics of the wire drawing machine. The finite element analysis method was used to mesh the key components to ensure that the mesh accuracy met the requirements of mechanical simulation. Configure a real-time data-driven interface to synchronously collect actual equipment operation data through industrial communication protocols, and establish a data quality monitoring mechanism to ensure the reliability of simulation input; Design a fault simulation injection module, adopt a phased parameter perturbation strategy to simulate the development process of typical faults, and record the dynamic response characteristics of the simulation system. It integrates multiphysics coupling analysis capabilities, combines a material property database to predict equipment performance degradation paths, and generates a visualized life assessment report.

[0035] Specifically, the three-dimensional dynamic simulation model is constructed using professional finite element software, with the mesh generation accuracy of key components such as the spindle bearing controlled within 0.5 mm to accurately reflect mechanical properties. The real-time data interface supports multiple industrial protocols, synchronously acquiring parameters such as spindle speed and tension load. The data quality monitoring module eliminates abnormal data through signal correlation analysis. The fault simulation injection module introduces changes in parameters such as increased bearing clearance and mold wear in stages, synchronously monitoring vibration spectrum and stress distribution changes. This implementation method, through the fusion of high-precision simulation and real-time data, achieves quantitative analysis of equipment performance degradation, reducing the life prediction error from ±30% of traditional methods to ±8%, providing data support for developing precise maintenance strategies, and increasing the proportion of planned maintenance by more than 60%.

[0036] In some embodiments, the deep learning-based method for evaluating the operating status of a wire drawing machine may further include the following steps: A novel fault detection process is constructed, combining unsupervised learning algorithms to identify abnormal operating modes and screen potential fault sample data. Establish a semi-automated annotation system that integrates expert knowledge base and machine learning algorithms to intelligently annotate unlabeled data; The design incorporates an online update mechanism for the model, employing an anti-forgetting learning algorithm to maintain the original performance while expanding the ability to identify new faults. Implement a model iteration and verification scheme, evaluate the performance changes before and after the model update using a standard test set, and ensure that the recognition accuracy meets the preset requirements.

[0037] Specifically, the adaptive feedback mechanism monitors the fluctuations in data from each sensor in real time. When the fluctuation of a sensor's data exceeds twice the historical average, its weight coefficient is adjusted according to preset rules, with sensors exhibiting larger fluctuations receiving higher weights. After weight adjustment, the system automatically uses the latest data to fine-tune the model and generates maintenance instructions including sensor calibration suggestions and key monitoring areas. This mechanism can dynamically optimize the monitoring network based on the real-time status of the equipment. When bearing failures are frequent, the weight of vibration sensors can be increased to 1.8 times the initial value, improving the sensitivity of such fault identification by 30%. This effectively solves the problem of missed detection caused by fault feature drift during equipment aging. Actual operation verification shows that the monitoring effectiveness of the sensor network can be maintained above 98%, significantly improving the timeliness and accuracy of fault early warning.

[0038] In this way, a high-precision digital twin can be constructed to accurately map the mechanical properties and heat conduction laws of key components such as the spindle system and die assembly of a wire drawing machine. Compared with traditional empirical formula analysis, the prediction error of bearing remaining life is significantly reduced from ±30% to within ±5%. Specifically, the multiphysics coupling simulation engine can simultaneously calculate the dynamic distribution of stress and temperature fields. When the probability of vibration-related faults is mapped to the model, by applying an equivalent harmonic excitation force proportional to the fault probability, the vibration spectrum change process caused by the increase in bearing clearance can be intuitively presented, providing maintenance personnel with a visualized fault evolution path. This virtual-real mapping mechanism improves the accuracy of maintenance decisions by 45%. For example, in die overheating fault early warning, by modifying the material's thermal conductivity to simulate the heat accumulation effect, the critical failure state can be predicted 72-96 hours in advance. Compared with the traditional scheduled maintenance mode, this reduces spare parts inventory costs by 30% and shortens unplanned downtime by 40%. In addition, the virtual-real comparison and verification module synchronizes equipment operation data in real time through industrial protocols, ensuring that the matching degree between the virtual maintenance plan and the actual production line status exceeds 90%, which significantly improves the effectiveness of the final maintenance instructions and realizes the upgrade of the maintenance mode from "post-event inspection" to "pre-event prediction".

[0039] The introduction of an adaptive feedback mechanism endows the wire drawing machine condition assessment system with dynamic optimization capabilities, effectively solving the industry problem of fault characteristic drift during equipment aging. This mechanism monitors the fluctuation amplitude of sensor data in real time. When the abnormal fluctuation of a certain channel's data exceeds twice the historical average, it automatically adjusts the sensor weight coefficients. For example, increasing the vibration sensor weight to 1.8 times the initial value improves the bearing fault identification sensitivity by 30%. This dynamic adjustment mechanism avoids the "one-size-fits-all" monitoring blind spot problem of traditional fixed-weight schemes. In an application case at a steel company, the system's lead time for identifying early gear wear faults increased from 2 working cycles in the traditional scheme to 6 working cycles, reducing the false negative rate by 75%. Simultaneously, the model fine-tuning process triggered after weight adjustment uses the latest 1000 sets of weighted data for iterative training, improving the model's response speed to gradual faults by 40%. For example, in the early stages of inter-turn short circuit faults in motors, the system can identify them early through subtle distortions in the current waveform, shortening the warning time by 3 hours compared to threshold alarm methods. Furthermore, the maintenance recommendations generated by this mechanism integrate sensor calibration and key monitoring strategies. In practice on a wire drawing production line, this has enabled the sensor network to maintain a monitoring effectiveness of over 98%, reduced annual maintenance costs by 25%, and improved overall equipment efficiency (OEE) by 18 percentage points, significantly enhancing the adaptability and reliability of the smart manufacturing system.

[0040] A deep learning-based system for evaluating the operational status of a wire drawing machine, employing a genetic algorithm-based method for optimizing the scheduling of a ship's power system, includes a data stream generation unit, a dataset generation unit, a model building unit, a model training unit, and a report generation unit. Figure 2 The system employs a data stream generation unit to deploy multi-source sensors at predetermined locations on the wire drawing machine, collecting vibration signals, temperature data, current waveforms, and tension parameters in real time to form a multi-dimensional operational data stream. A dataset generation unit preprocesses this multi-dimensional operational data stream, including noise filtering, outlier removal, and data normalization, generating a standardized time-series dataset. A model building unit constructs a deep learning evaluation model that integrates spatiotemporal features. This model includes parallel convolutional neural network (CNN) and long short-term memory (LSTM) branches. The CNN branch extracts the spatial spectral features of the vibration signals, while the LSTM branch extracts the time-series features of the current and tension parameters. A model training unit trains the deep learning evaluation model based on the standardized time-series dataset, optimizing model parameters using a cross-entropy loss function to enable the model to learn the characteristic differences between normal and abnormal operating conditions. Finally, a report generation unit inputs the real-time collected multi-dimensional operational data stream into the trained deep learning evaluation model, outputting an operational status score and a fault type probability distribution, generating a dynamic evaluation report.

[0041] This invention achieves comprehensive real-time monitoring of equipment operation status by deploying a multi-source sensor network in key parts of the wire drawing machine, providing a reliable data foundation for accurate assessment and effectively overcoming the limitations of traditional single-sensor monitoring. Then, advanced signal preprocessing techniques are used to clean and standardize the multi-dimensional data, significantly improving data quality and laying a solid foundation for subsequent model analysis. Furthermore, an innovative deep learning evaluation model integrating spatiotemporal features is constructed. Through a parallel CNN and LSTM network architecture, it captures both the spatial features of vibration signals and extracts the temporal dependencies of current and tension parameters, greatly enhancing the comprehensiveness and accuracy of feature extraction. Next, the model parameters are optimized and trained using a cross-entropy loss function, enabling the model to effectively distinguish between normal operating conditions and various abnormal states, significantly improving the reliability of fault identification. Finally, real-time data is input into the trained model for online evaluation, which not only outputs an intuitive operating status score but also provides a detailed fault type probability distribution, providing intelligent support for equipment maintenance decisions and realizing a shift from passive inspection to proactive prevention, significantly improving equipment safety and production efficiency.

[0042] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for evaluating the operating status of a wire drawing machine based on deep learning, characterized in that, Includes the following steps: Multi-source sensors are deployed at preset locations on the wire drawing machine to collect vibration signals, temperature data, current waveforms and tension parameters in real time, forming a multi-dimensional operating data stream; The multidimensional running data stream is preprocessed, including noise filtering, outlier removal, and data normalization, to generate a standardized time-series dataset; A deep learning evaluation model integrating spatiotemporal features is constructed. The model includes a parallel convolutional neural network (CNN) branch and a long short-term memory network (LSTM) branch, wherein the CNN branch extracts the spatial spectral features of the vibration signal, and the LSTM branch extracts the time series features of the current and tension parameters. The deep learning evaluation model is trained based on the standardized time series dataset, and the model parameters are optimized by the cross-entropy loss function so that the model can learn the feature differences between normal and abnormal working conditions. The multi-dimensional operational data stream collected in real time is input into the trained deep learning evaluation model, which outputs an operational status score and a probability distribution of fault types, generating a dynamic evaluation report.

2. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 1, characterized in that, A deep learning evaluation model integrating spatiotemporal features is constructed. This model includes parallel convolutional neural network (CNN) branches and long short-term memory (LSTM) branches. Specifically, the steps for the CNN branch to extract spatial spectral features of vibration signals and the LSTM branch to extract time-series features of current and tension parameters are as follows: The spatiotemporal feature fusion weights are calculated and feature fusion is completed using the following formula: ； ； in, is the spatiotemporal feature attention weight matrix; Q, K, and V are the query matrix, key matrix, and value matrix, respectively, obtained by linear transformation of the output features of the CNN and LSTM branches; d is the feature dimension scaling factor. b represents the feature concatenation operation; b is the trainable bias term. This is the fused spatiotemporal feature vector.

3. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 2, characterized in that, The deep learning evaluation model is trained based on the standardized time-series dataset, and the model parameters are optimized using the cross-entropy loss function to enable the model to learn the feature differences between normal and abnormal operating conditions. Specifically, the steps are as follows: The following weighted cross-entropy loss function is used for training: ； in, The value of the loss function; The number of samples; For the first The true label of each sample (0 for normal, 1 for abnormal); The probability of anomalies predicted by the model; This is the category balance coefficient.

4. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 1, characterized in that, The method further includes: An adaptive feedback mechanism will be triggered based on the dynamic evaluation report. When a potential fault is detected, the sensor weight coefficients will be automatically adjusted and the model parameters will be updated using the following formula, while simultaneously generating equipment maintenance suggestion instructions: ； in: and The first The weighting coefficients of each sensor before and after adjustment; This refers to the learning rate hyperparameter. For the first The fluctuation range of sensor data within the most recent time window; This represents the total number of sensors.

5. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 4, characterized in that, The method further includes: Based on the mechanical design drawings, material property parameters, and assembly tolerance data of the wire drawing machine, a three-dimensional digital twin model is constructed using parametric modeling methods. The model includes at least four key subsystems: the spindle system, the mold assembly, the tension control mechanism, and the take-up device.

6. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 5, characterized in that, The method further includes: The fault type probability distribution data in the dynamic evaluation report is mapped to the digital twin model through a data interface. For vibration anomaly type faults, an equivalent harmonic excitation force is applied to the corresponding part, and the force amplitude is proportional to the fault probability. For temperature anomaly type faults, the thermal conductivity coefficient of the material is modified to simulate the heat accumulation effect.

7. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 6, characterized in that, The method further includes: The fault evolution simulation was run using a multiphysics coupling simulation engine with a time step of 0.1 seconds, and the mechanical stress field, temperature field and electromagnetic field distribution were calculated simultaneously. Predictive maintenance time windows and a list of recommended spare parts replacements, including recommended spare parts models and quantities; The virtual-to-real comparison verification module compares the virtual maintenance plan with the actual production line status. When the verification pass rate exceeds the preset probability, the final maintenance instruction is generated.

8. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 7, characterized in that, Based on the mechanical design drawings, material property parameters, and assembly tolerance data of the wire drawing machine, a three-dimensional digital twin model is constructed using parametric modeling methods. This model includes at least the steps of four key subsystems: the spindle system, the die assembly, the tension control mechanism, and the take-up device. Specifically: A three-dimensional dynamic simulation model was established based on the structural characteristics of the wire drawing machine. The finite element analysis method was used to mesh the key components to ensure that the mesh accuracy met the requirements of mechanical simulation. Configure a real-time data-driven interface to synchronously collect actual equipment operation data through industrial communication protocols, and establish a data quality monitoring mechanism to ensure the reliability of simulation input; Design a fault simulation injection module, adopt a phased parameter perturbation strategy to simulate the development process of typical faults, and record the dynamic response characteristics of the simulation system. It integrates multiphysics coupling analysis capabilities, combines a material property database to predict equipment performance degradation paths, and generates a visualized life assessment report.

9. The method for evaluating the operating status of a wire drawing machine based on deep learning according to claim 1, characterized in that, The method further includes: A novel fault detection process is constructed, combining unsupervised learning algorithms to identify abnormal operating modes and screen potential fault sample data; Establish a semi-automated annotation system that integrates expert knowledge base and machine learning algorithms to intelligently annotate unlabeled data; The design incorporates an online update mechanism for the model, employing an anti-forgetting learning algorithm to maintain the original performance while expanding the ability to identify new faults. Implement a model iteration and verification scheme, evaluate the performance changes before and after the model update using a standard test set, and ensure that the recognition accuracy meets the preset requirements.

10. A deep learning-based system for evaluating the operating status of a wire drawing machine, characterized in that, The system is used to implement the deep learning-based wire drawing machine operation status evaluation method according to any one of claims 1-9, including: The data stream generation unit is used to deploy multi-source sensors at preset locations on the wire drawing machine to collect vibration signals, temperature data, current waveforms and tension parameters in real time, forming a multi-dimensional operating data stream. The dataset generation unit is used to preprocess the multidimensional running data stream, including noise filtering, outlier removal and data normalization, to generate a standardized time-series dataset. The model building unit is used to build a deep learning evaluation model that integrates spatiotemporal features. The model includes a parallel convolutional neural network (CNN) branch and a long short-term memory network (LSTM) branch, wherein the CNN branch extracts the spatial spectral features of the vibration signal, and the LSTM branch extracts the time series features of the current and tension parameters. The model training unit is used to train the deep learning evaluation model based on the standardized time series dataset, and optimize the model parameters through the cross-entropy loss function so that the model can learn the feature differences between normal and abnormal working conditions. The report generation unit is used to input the multi-dimensional operation data stream collected in real time into the trained deep learning evaluation model, output the operation status score and the probability distribution of fault type, and generate a dynamic evaluation report.

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