A forging monitoring method and system

Abstract

The present invention relates to the field of forging monitoring technology, and discloses a forging monitoring method and system. The method arranges Internet of Things sensors on the forging equipment to collect a variety of data in the forging process in real time, and transmits it to the data center through the Internet of Things gateway. In the data center, after pre-processing the data, it is analyzed using a deep learning model based on the CTA-GNN algorithm to determine whether there is equipment failure or process risk. When the risk probability exceeds the preset threshold, the system automatically generates early warning information and sends it to relevant personnel through SMS, email or system notification, while recording the sending status and receiving confirmation. In addition, the present invention also provides a corresponding intelligent monitoring system, including data acquisition, transmission, processing, intelligent analysis and early warning management modules. The system can accurately judge faults, improve monitoring efficiency and accuracy, timely discover and warn potential risks, and effectively ensure the safety and stability of the forging process.

Description

Technical Field

[0001] The present invention relates to the technical field of forging monitoring, and in particular to a forging monitoring method and system. Background Art

[0002] The forging process is an important link in industrial production, and its stability and safety are directly related to product quality and production efficiency. However, traditional forging process monitoring methods often rely on manual inspections and empirical judgments, and have problems such as low monitoring efficiency, untimely fault detection, and low early warning accuracy. With the rapid development of the Internet of Things and deep learning technologies, new solutions have been provided for forging process monitoring. By using IoT sensors to collect various data during the forging process in real time, and using deep learning algorithms to analyze and predict the data, intelligent monitoring of the forging process can be achieved. This method can not only improve monitoring efficiency, but also detect equipment failures or process risks in a timely manner, providing strong protection for production safety. Therefore, the development of a forging monitoring method and system based on the Internet of Things and deep learning is of great significance to improving the stability and safety of the forging process. Summary of the Invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a forging monitoring method and system to solve the problems of low monitoring efficiency, untimely fault detection and low early warning accuracy in the prior art.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a forging monitoring method, the method comprising:

[0006] Step S100: arranging IoT sensors on the forging equipment to collect forging data during the forging process in real time;

[0007] Step S200: The collected forging data is transmitted to the data center via the Internet of Things gateway;

[0008] Step S300: performing data preprocessing on the received forging data in the data center;

[0009] Step S400: Analyze the pre-processed forging data using a deep learning-based algorithm to determine whether there is equipment failure or process risk;

[0010] Step S500: When it is determined that there is equipment failure or process risk in forging, an early warning message is sent through the data center.

[0011] Preferably, in a possible implementation manner of the first aspect, the forging data includes forging force data, forging times data, forging current data, forging temperature data and equipment vibration data.

[0012] Preferably, in a possible implementation of the first aspect, step S200 specifically includes:

[0013] The IoT sensor sends the collected forging data to the IoT gateway wirelessly;

[0014] The IoT gateway encapsulates and encrypts the received forging data;

[0015] The IoT gateway transmits the encapsulated and encrypted forging data to the data center.

[0016] Preferably, in a possible implementation of the first aspect, the data preprocessing includes data cleaning, outlier detection and data normalization.

[0017] Preferably, in a possible implementation of the first aspect, the deep learning-based algorithm adopts a CTA-GNN algorithm, and uses historical forging data, equipment failure data and forging result data to train the CTA-GNN model.

[0018] Preferably, in a possible implementation of the first aspect, the CTA-GNN model structure includes:

[0019] The input layer is used to receive forging data and construct a corresponding graph structure based on the physical structure of the forging equipment. The nodes represent the components of the forging equipment, and the edges represent the interactions between the components.

[0020] The feature extraction layer uses a graph neural network to extract the data features of the forging process from the input forging data and graph structure;

[0021] The causal-shortcut feature separation layer uses a soft masking mechanism to split the data features obtained by the feature extraction layer into causal features and shortcut features. The causal features are directly related to the actual physical process in the forging process, while the shortcut features contain noise and misleading information.

[0022] The parameterized adjustment layer performs parameterized backdoor adjustments on causal features and shortcut features to reduce the negative impact of shortcut features on prediction results;

[0023] The output layer uses the classifier to predict the probability of each equipment failure or process risk.

[0024] Preferably, in a possible implementation of the first aspect, step S500 specifically includes:

[0025] When the probability of equipment failure or process risk output by the deep learning-based algorithm exceeds the preset threshold, the data center generates corresponding warning information;

[0026] Warning information is sent to relevant personnel via SMS, email or system notification;

[0027] The data center records the sending status and receipt confirmation of the warning information.

[0028] In a second aspect, the present invention provides a forging monitoring system, comprising:

[0029] The data acquisition module is used to arrange IoT sensors on the forging equipment to collect forging data in real time during the forging process, including forging force data, forging times data, forging current data, forging temperature data, and equipment vibration data information;

[0030] The data transmission module is used to send the forging data collected by the IoT sensor to the IoT gateway wirelessly. The IoT gateway encapsulates and encrypts the received data and transmits it to the data center.

[0031] The data processing module is used to pre-process the received forging data in the data center, including data cleaning, outlier detection and data normalization;

[0032] The intelligent analysis module uses a deep learning model based on the CTA-GNN algorithm to analyze the pre-processed forging data to determine whether there is equipment failure or process risk. The CTA-GNN model structure includes an input layer, a feature extraction layer, a causal-shortcut feature separation layer, a parameter adjustment layer, and an output layer.

[0033] The early warning management module generates corresponding early warning information according to the early warning rules when the probability of equipment failure or process risk output by the intelligent analysis module is greater than the preset threshold, and sends it to relevant personnel through SMS, email or system notification, while recording the sending status and receipt confirmation of the early warning information.

[0034] The beneficial effects of the present invention are: by using IoT sensors to collect a variety of data during the forging process in real time, and through data preprocessing and deep learning algorithm analysis, it is possible to accurately judge equipment failures or process risks. This method not only improves the accuracy and efficiency of monitoring, but also enables timely detection and early warning of faults, effectively avoiding potential safety hazards. In addition, by adopting the CTA-GNN algorithm, the present invention can more deeply mine the characteristic information in the forging data and improve the accuracy of fault prediction. At the same time, the early warning management module can automatically send early warning information and record the sending status and reception confirmation, providing relevant personnel with timely and reliable early warning information, which helps to ensure the safety and stability of the forging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A flow chart of a forging monitoring method is provided for this application.

[0037] Figure 2 A structural diagram of a forging monitoring system is provided for this application.

[0038] Explanation of the accompanying drawings: 1-data acquisition module, 2-data transmission module, 3-data processing module, 4-intelligent analysis module, 5-early warning management module. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] Example 1: Figure 1 As shown, the present invention provides a forging monitoring method, the method comprising:

[0041] Step S100: IoT sensors are arranged on the forging equipment to collect forging data during the forging process in real time.

[0042] Specifically, the IoT sensors include forging pressure sensors, current sensors, thermocouple temperature sensors, and acceleration sensors. The forging data include forging pressure data, forging times data, forging current data, forging temperature data, and equipment vibration data.

[0043] In this embodiment, the forging pressure sensor used is model XYZ-1000, which has high precision, high stability and good impact resistance. It is deployed at the position where the forging press's pressure head contacts the workpiece, and is used to monitor the pressure changes during the forging process in real time. A current sensor with model ABC-500 is installed in the control system of the forging press to monitor the current consumption of the motor during the forging process, thereby indirectly reflecting the load condition of the forging press. A thermocouple temperature sensor with model DEF-200 is used to obtain the forging temperature, which fits tightly on the forging die to ensure that the temperature changes during the forging process can be accurately measured. The equipment vibration data is provided by an acceleration sensor with model GHI-300, which is installed on the forging platform of the forging press to capture and analyze the vibration characteristics of the equipment during the forging process.

[0044] Forging data, including forging force data, forging times data, forging current data, forging temperature data, and equipment vibration data, is an important basis for evaluating the stability of the forging process and the health status of the equipment. Forging force data can directly reflect the pressure distribution during the forging process, which helps to determine whether the workpiece has undergone uniform plastic deformation; the number of forging times records the frequency of forging operations, which is of great significance for evaluating production efficiency and equipment wear, and is obtained through historical records in the data center; changes in forging current can indirectly reflect the energy consumption and load changes of the forging machine; forging temperature is a key factor affecting the plasticity and deformation resistance of the material, and is crucial for controlling forging quality and avoiding defects caused by overheating or overcooling; and equipment vibration data can reveal the operating status of the equipment and provide timely warnings of potential mechanical failures.

[0045] Step S200: The collected forging data is transmitted to the data center via the Internet of Things gateway.

[0046] Specifically, the IoT sensor sends the collected forging data wirelessly to the IoT gateway; the IoT gateway encapsulates and encrypts the received forging data; and the IoT gateway transmits the encapsulated and encrypted forging data to the data center.

[0047] In this embodiment, after completing the collection of forging data, the IoT sensor transmits the data to the IoT gateway through the built-in wireless communication module.

[0048] The IoT gateway encapsulates and encrypts the received forging data. Encapsulation refers to packaging the original data sent by the sensor in a specific format to facilitate subsequent data processing and analysis; encryption is to ensure that the data is not illegally intercepted or tampered with during transmission, thereby ensuring data security. In this embodiment, the IoT gateway uses the AES encryption algorithm to encrypt the forging data.

[0049] After completing the encapsulation and encryption, the IoT gateway transmits the forging data to the data center via the HTTPS protocol. The data center can receive and store the forging data from the IoT gateway in real time.

[0050] Furthermore, the communication between IoT gateways and data centers utilizes redundancy and failover mechanisms to prevent single points of failure during data transmission. If an IoT gateway fails or the network connection becomes unstable, other gateways can take over data transmission, ensuring data continuity and integrity.

[0051] Step S300: Preprocessing the received forging data in the data center.

[0052] Specifically, data preprocessing includes data cleaning, outlier detection, and data normalization.

[0053] Data cleaning aims to remove or correct erroneous, duplicate, or missing values ​​in forging data. Sensors may be affected by environmental interference or equipment failures during the data collection process, resulting in anomalies or missing values ​​in the data. Therefore, during the data cleaning phase, the received forging data is inspected to identify and correct erroneous values. Duplicate or missing data records are also filled or deleted to ensure data integrity and consistency.

[0054] Outlier detection performs statistical analysis on forging data to determine the normal distribution range of the data and identifies data values ​​outside the normal range as outliers. The system marks or deletes identified outliers to avoid adverse effects on subsequent analysis.

[0055] Data normalization aims to convert forging data to a unified scale for ease of subsequent analysis and comparison. Because various parameters in forging data (such as forging force and temperature) have different dimensions and ranges, direct analysis can lead to inaccurate or difficult-to-interpret results. Therefore, during the data normalization phase, these parameters are converted to dimensionless values, ensuring that all parameters fall within a comparable range.

[0056] In this example, the drop_duplicates() function in the Pandas library is first used to remove duplicate records from the dataset. By comparing the records in the dataset row by row, identical records are identified and deleted, thereby ensuring the uniqueness of each record in the dataset.

[0057] Secondly, we use the mean filling method to handle missing values ​​in the data, using the fillna() function in the Pandas library and passing data.mean() as the filling parameter. This function calculates the mean of the non-missing values ​​in each numeric column that contains missing values ​​and replaces all missing values ​​in that column with this mean.

[0058] Next, we use the Z-score method to detect outliers in the dataset. Specifically, we use the stats.zscore() function from the SciPy library to calculate the Z-score for each numeric feature in the dataset. The Z-score represents the difference between each feature value and its mean divided by the standard deviation. We then set a threshold of 3, and consider records with an absolute Z-score greater than this threshold to be outliers.

[0059] For detected outliers, we chose to remove them. Specifically, we created a Boolean DataFrame called abnormal_data based on the Z-score returned by the stats.zscore() function, where a True value indicates that the record is an outlier. We then used the Boolean indexing functionality in the Pandas library to remove all records containing outliers.

[0060] Finally, we use the Min-Max normalization method to normalize the data. Specifically, we define a lambda function that takes a numeric Series as input, calculates the minimum and maximum values ​​of the Series, then subtracts the minimum from each value and divides it by the difference between the maximum and minimum values ​​to obtain the normalized value. We then use the apply() function in the Pandas library to apply this anonymous function to each numeric feature in the dataset, achieving data normalization.

[0061] Step S400: Analyze the pre-processed forging data using a deep learning-based algorithm to determine whether there is equipment failure or process risk.

[0062] Specifically, the deep learning-based algorithm adopts the CTA-GNN algorithm, and uses historical forging data, equipment failure data and forging result data to train the CTA-GNN model. The historical forging data, equipment failure data and forging result data are obtained from the data center.

[0063] The historical forging data includes forging force data, historical forging times data, historical forging current data, historical forging temperature data and historical equipment vibration data.

[0064] The CTA-GNN model structure includes input layer, feature extraction layer, causal-shortcut feature separation layer, parameter adjustment layer and output layer.

[0065] The algorithm receives preprocessed forging data through its input layer. This data includes forging force, forging times, current, temperature, and vibration data. Based on the physical structure of the forging equipment and the interactions between its components, it constructs a corresponding graph structure, where nodes represent the individual components of the forging equipment, and edges represent the interactions and connections between these components.

[0066] In the feature extraction layer, graph neural networks are used to extract data features from the input forging data and the constructed graph structure. These features not only include the changes in various physical parameters during the forging process, but also reflect the interactions and influences between equipment components.

[0067] The causal-shortcut feature separation layer uses a soft masking mechanism to split the data features obtained by the feature extraction layer into causal features and shortcut features. Causal features are directly related to the actual physical processes in the forging process and can accurately reflect changes in equipment status and process parameters. Shortcut features, on the other hand, contain noise, misleading information, or complex correlations with other factors, which can be inaccurate or disruptive for fault prediction. This separation layer allows for more accurate focus on features that actually contribute to fault prediction.

[0068] The parameterized adjustment layer performs parameterized backdoor adjustments on causal and shortcut features, mitigating the negative impact of shortcut features on prediction results. By optimizing the algorithm's parameter settings, the model can more accurately identify and utilize causal features for prediction, while also reducing the interference of shortcut features on prediction results.

[0069] Finally, the output layer uses a classifier to predict the probability of each equipment failure or process risk. This classifier is trained and optimized based on the features extracted and processed by the previous layers, and can output a prediction result that includes various failure types and risk probabilities.

[0070] In this embodiment, the structure of the CTA-GNN model is first defined. The model includes an input layer, a feature extraction layer, a causal-shortcut feature separation layer, a parameter adjustment layer, and an output layer.

[0071] The input layer receives the node feature matrix x and the edge index matrix edge_index as input. The dimension of the node feature matrix x is (num_nodes, input_dim), indicating that there are num_nodes nodes and each node has input_dim features; the dimension of the edge index matrix edge_index is (2, num_edges), indicating the connection relationship between nodes.

[0072] In the feature extraction layer, a graph convolution layer (GCNConv) is used to extract features from the input graph data. The node feature matrix x and the edge index matrix edge_index are input to the graph convolution layer to obtain the extracted features h. The ReLU activation function is then used to perform a nonlinear transformation on the features h to enhance the model's expressiveness.

[0073] Next, the causal-shortcut feature separation layer is entered. The extracted feature h is fed into two fully connected layers to generate the causal features causal_features and shortcut features shortcut_features. The output dimension of both fully connected layers is hidden_dim / / 2, achieving dimensionality reduction and feature separation.

[0074] Then, we enter the parameter adjustment layer and use a fully connected layer to perform parameter adjustment on the extracted features h to obtain the adjusted features adjusted_features. The output dimension of this layer is the same as the input dimension, i.e. hidden_dim.

[0075] Finally, the output layer performs an average pooling operation on the adjusted features (adjusted_features) to obtain a global feature representation. This global feature representation is then input into a fully connected layer to obtain the classification result (logits). Finally, the classification result (logits) is normalized using the softmax function to obtain the predicted probability (probs).

[0076] Step S500: When it is determined that there is equipment failure or process risk in forging, an early warning message is sent through the data center.

[0077] Specifically, when the probability of equipment failure or process risk output by the deep learning-based algorithm is greater than the preset threshold, the data center generates corresponding early warning information; the early warning information is sent to relevant personnel via SMS, email or system notification; the data center records the sending status and receipt confirmation of the early warning information.

[0078] In this embodiment, after the CTA-GNN model analyzes the preprocessed forging data, if it determines that the probability of a certain equipment failure or process risk in the forging process exceeds 50%, the data center will respond immediately and generate detailed early warning information.

[0079] First, the warning information will be sent to the mobile phones of relevant personnel through the SMS platform. These personnel include production supervisors, equipment maintenance personnel and other staff; secondly, the warning information will also be sent to the mailboxes of relevant personnel through the email system; in addition, the data center will also send system notifications through the company's internal management system, which will appear in the form of notification bar messages on the computer screens or mobile devices of relevant personnel, further ensuring the visibility and accessibility of the warning information.

[0080] The data center also records the status of alert messages sent and their receipt confirmation. After sending an alert message, the data center monitors its delivery status in real time, including whether it was successfully delivered or failed due to network issues or device failures. Alert messages also include a built-in confirmation mechanism, allowing recipients to confirm their actions after reading the message.

[0081] If the warning message fails to arrive or the recipient fails to confirm it within the specified time, the data center triggers a further reminder mechanism, which includes sending the warning message again and contacting the relevant personnel directly by phone to ensure that the warning message can be responded to and handled in a timely manner.

[0082] Example 2: Figure 2 As shown, the present invention provides a forging monitoring system, the system comprising:

[0083] Data acquisition module 1 is used to arrange IoT sensors on the forging equipment to collect forging data in real time during the forging process, including forging force data, forging times data, forging current data, forging temperature data, and equipment vibration data information;

[0084] Data transmission module 2, used to send the forging data collected by the IoT sensor to the IoT gateway via wireless mode. The IoT gateway encapsulates and encrypts the received data and transmits it to the data center;

[0085] Data processing module 3, used for preprocessing the received forging data in the data center, including data cleaning, outlier detection and data normalization;

[0086] Intelligent analysis module 4 uses a deep learning model based on the CTA-GNN algorithm to analyze the pre-processed forging data to determine whether there is equipment failure or process risk. The CTA-GNN model structure includes an input layer, a feature extraction layer, a causal-shortcut feature separation layer, a parameter adjustment layer, and an output layer;

[0087] Early warning management module 5, when the probability of equipment failure or process risk output by the intelligent analysis module is greater than the preset threshold, generates corresponding early warning information according to the early warning rules, and sends it to relevant personnel through SMS, email or system notification, and records the sending status and receipt confirmation of the early warning information.

[0088] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A forging monitoring method, characterized in that: The method comprises: Step S100: arranging IoT sensors on the forging equipment to collect forging data during the forging process in real time; Step S200: The collected forging data is transmitted to the data center via the Internet of Things gateway; Step S300: performing data preprocessing on the received forging data in the data center; Step S400: Analyze the pre-processed forging data using a deep learning-based algorithm to determine whether there is equipment failure or process risk; The deep learning-based algorithm adopts the CTA-GNN algorithm and uses historical forging data, equipment failure data and forging result data to train the CTA-GNN model; The CTA-GNN model structure includes: The input layer is used to receive forging data and construct a corresponding graph structure based on the physical structure of the forging equipment. The nodes represent the components of the forging equipment, and the edges represent the interactions between the components. The feature extraction layer uses a graph neural network to extract the data features of the forging process from the input forging data and graph structure; The causal-shortcut feature separation layer uses a soft masking mechanism to split the data features obtained by the feature extraction layer into causal features and shortcut features. The causal features are directly related to the actual physical process in the forging process, while the shortcut features contain noise and misleading information. The parameterized adjustment layer performs parameterized backdoor adjustments on causal features and shortcut features to reduce the negative impact of shortcut features on prediction results; The output layer uses the classifier to predict the probability of each equipment failure or process risk; Step S500: When it is determined that there is equipment failure or process risk in forging, an early warning message is sent through the data center.

2. The forging monitoring method according to claim 1, wherein: The forging data includes forging force data, forging times data, forging current data, forging temperature data and equipment vibration data.

3. The forging monitoring method according to claim 1, wherein: The step S200 specifically includes: The IoT sensor sends the collected forging data to the IoT gateway wirelessly; The IoT gateway encapsulates and encrypts the received forging data; The IoT gateway transmits the encapsulated and encrypted forging data to the data center.

4. The forging monitoring method according to claim 1, wherein: The data preprocessing includes data cleaning, outlier detection and data normalization.

5. The forging monitoring method according to claim 1, wherein: Step S500 specifically includes: When the probability of equipment failure or process risk output by the deep learning-based algorithm exceeds the preset threshold, the data center generates corresponding warning information; Warning information is sent to relevant personnel via SMS, email or system notification; The data center records the sending status and receipt confirmation of the warning information.

6. A forging monitoring system, characterized in that: The system is used to perform a forging monitoring method according to any one of claims 1 to 5, comprising: The data acquisition module is used to arrange IoT sensors on the forging equipment to collect forging data in real time during the forging process, including forging force data, forging times data, forging current data, forging temperature data, and equipment vibration data information; The data transmission module is used to send the forging data collected by the IoT sensor to the IoT gateway wirelessly. The IoT gateway encapsulates and encrypts the received data and transmits it to the data center. The data processing module is used to pre-process the received forging data in the data center, including data cleaning, outlier detection and data normalization; The intelligent analysis module uses a deep learning model based on the CTA-GNN algorithm to analyze the pre-processed forging data to determine whether there is equipment failure or process risk. The CTA-GNN model structure includes an input layer, a feature extraction layer, a causal-shortcut feature separation layer, a parameter adjustment layer, and an output layer. The early warning management module generates corresponding early warning information according to the early warning rules when the probability of equipment failure or process risk output by the intelligent analysis module is greater than the preset threshold, and sends it to relevant personnel through SMS, email or system notification, while recording the sending status and receipt confirmation of the early warning information.

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