Cigarette making machine dust removal equipment motor fault diagnosis method based on deep transfer learning

Abstract

The invention relates to the field of mechanical state monitoring, and particularly discloses a cigarette making machine dust removal equipment motor fault diagnosis method based on deep transfer learning, and the method comprises the steps: collecting vibration and temperature signals of dust removal workshop motor equipment in real time, and carrying out the data preprocessing; constructing a transfer learning model by using a convolutional neural network to perform feature extraction and training so as to predict a vibration prediction result yprd; and meanwhile, secondary dynamic threshold judgment is carried out on the preprocessed motor temperature data, a temperature early warning model Mtemp is constructed, diagnosis is carried out, and a temperature early warning result tprd is obtained. According to the invention, the fault diagnosis of the motor is realized through the vibration prediction and temperature early warning model, and the state level and the fault information are pushed to the client, so that the management efficiency and the operation stability of the equipment are improved.

Description

Technical Field

[0001] The present application relates to the field of mechanical condition monitoring, and more specifically, to a method for diagnosing motor faults of dust removal equipment of a cigarette making machine based on deep transfer learning. Background Art

[0002] In the tobacco manufacturing industry, the widespread use and frequent operation of rotating machinery such as motors and fans make it particularly important to monitor the mechanical health of these devices.

[0003] To ensure the reliability of these critical components, the method commonly used is to monitor their vibration signals. Through advanced signal processing and spectrum analysis techniques, the operating conditions of these devices can be accurately evaluated. This vibration monitoring technology is widely used in the tobacco industry, including but not limited to dust removal systems, cigarette production lines and packaging equipment. These monitoring methods are essential to maintain the continuous operation of the production line and ensure operational safety.

[0004] However, traditional signal processing and spectrum analysis usually require maintenance personnel to have high professional knowledge and experience in order to correctly interpret vibration data and spectrum analysis results. This limits the popularization and application of monitoring technology because not all maintenance personnel can meet this requirement.

[0005] With the continuous advancement of machine learning and data mining technology, a new data-driven fault diagnosis method is becoming mainstream. This method reduces the need for highly skilled maintenance personnel, making the fault diagnosis process more efficient and easy to operate, and has been widely used in industrial production, especially in online equipment monitoring.

[0006] However, data collection in industrial settings often faces challenges, such as a lack of sufficient negative samples and the data is often unlabeled, which is a problem for machine learning and deep learning models that rely on large amounts of labeled data.

[0007] Therefore, a motor fault diagnosis method for dust removal equipment of a cigarette making machine based on deep transfer learning is provided. Summary of the invention

[0008] In order to solve the above technical problems, this application is proposed. This application provides a method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning, which uses the vibration and temperature signals of the motor of the dust removal equipment of cigarette making machines for data analysis and modeling, adapts the data model trained based on vibration data in known fields to the fault diagnosis of the motor of the dust removal equipment of cigarette making machines, and integrates the real-time early warning data of the motor temperature to improve the accuracy of fault prediction.

[0009] Specifically, according to one aspect of the present application, a method for diagnosing motor faults of a dust removal device of a cigarette making machine based on deep transfer learning is provided, which comprises:

[0010] S1. Real-time collection of vibration signals and temperature signals of the motor equipment in the dust removal workshop during operation, and storage of the vibration signals and temperature signals in a local database respectively;

[0011] S2, performing data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performing data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal;

[0012] S3. Use convolutional neural network as the core architecture of the transfer learning model. In addition to the output layer, define a 6-layer neural network as the feature extraction layer of the transfer learning network.

[0013] S4: Train the transfer learning model to obtain a prediction model M T ;

[0014] S5, using the prediction model M T Predict the vibration data and get the vibration prediction result y prd ;

[0015] S6. Perform secondary dynamic threshold judgment on the pre-processed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd ;

[0016] S7, based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results;

[0017] Wherein, said S4 includes:

[0018] S41. Divide the source domain dataset D s , target domain dataset D T , for the source domain dataset D s , the target domain dataset D T Perform data preprocessing operations respectively;

[0019] S42, setting the initialization network parameters for model training, wherein the learning rate ∈=1e-3 is set, the learning rate adjustment strategy selects the Step method, and the optimization strategy selects the SGD method;

[0020] S43, the source domain dataset D sInput the transfer learning model and use batch normalization technology to accelerate model convergence. After repeated iterations, the optimal pre-training model M is obtained. S , and save it locally;

[0021] S44, the target domain dataset D T Input pre-trained model M S , and use batch normalization technology to fine-tune the model, and after repeated iterations, obtain the prediction model M that adapts to the target domain data T .

[0022] Optionally, the S2 includes: S21, resampling the vibration signal, eliminating missing values, and setting the batch size to 128; S22, adjusting the sliding window length L and overlap rate r of the resampled data, wherein the sliding window length L=128, and the overlap rate r=0.5; S23, applying Z-Score standardization to the resampled vibration signal to eliminate dimensional effects and accelerate model convergence, wherein the Z-Score calculation formula is as follows:

[0023]

[0024] Among them, X scaler is the standardized vibration data, X is the resampled vibration signal, μ is the mean of the data under the feature dimension, and σ is the standard deviation of the data under the feature; S24, removing abnormal values ​​and null values ​​from the temperature signal.

[0025] In particular, the S3 includes: the output layer introduces a custom linear layer as a classification layer, and uses the Dropout technology to reduce the overfitting phenomenon of the model.

[0026] Optionally, the S41 includes: S411, using a vibration test bench equipped with a vibration temperature sensor to collect vibration data of a motor using a faulty bearing and a normal bearing, and using the obtained labeled data as a source domain data set D S ; S412, install wireless vibration sensors on the motor equipment in the cigarette dust removal workshop, collect the motor vibration data of the new field on site within a certain period of time, and store the data in the database as the target domain data set D T ; S413, the source domain data set D S , the target domain dataset D T Execute step S2 to process the data into a format suitable for the neural network.

[0027] Optionally, the S7 includes: the motor state level includes a third state, a second state and a first state.

[0028] Optionally, S7 includes: if the vibration warning and temperature warning appear at the same time, pushing the third-level status to the client; if there is only a vibration or temperature warning, pushing the second-level status to the client; if there is no warning, pushing the first-level status to the client, and displaying the equipment fault diagnosis information according to the prediction results.

[0029] Specifically, according to another aspect of the present application, a motor fault diagnosis system for dust removal equipment of a cigarette making machine based on deep transfer learning is provided, which includes:

[0030] The motor data acquisition module is used to collect the vibration signal and temperature signal of the motor equipment in the dust removal workshop in real time during operation, and store the vibration signal and the temperature signal in a local database respectively;

[0031] A motor data preprocessing module, used for performing data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performing data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal;

[0032] The transfer learning model building module is used to use the convolutional neural network as the core architecture of the transfer learning model. In addition to the output layer, a 6-layer neural network is defined as the feature extraction layer of the transfer learning network;

[0033] The transfer learning model training module is used to train the transfer learning model to obtain the prediction model M T ;

[0034] The motor vibration prediction module is used to use the prediction model M T Predict the vibration data and get the vibration prediction result y prd ;

[0035] The motor temperature warning module is used to perform secondary dynamic threshold judgment on the preprocessed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd ;

[0036] A motor fault prediction module is used to predict the motor fault based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results;

[0037] Wherein, the transfer learning model training module includes:

[0038] Divide the source domain dataset D s , target domain dataset D T , for the source domain dataset Ds , the target domain dataset D T Perform data preprocessing operations respectively;

[0039] Set the initial network parameters for model training, where the learning rate ∈=1e-3, the learning rate adjustment strategy selects the Step method, and the optimization strategy selects the SGD method;

[0040] The source domain dataset D s Input the transfer learning model and use batch normalization technology to accelerate model convergence. After repeated iterations, the optimal pre-training model M is obtained. S , and save it locally;

[0041] The target domain dataset D T Input pre-trained model M S , and use batch normalization technology to fine-tune the model, and after repeated iterations, obtain the prediction model M that adapts to the target domain data T .

[0042] Optionally, the motor fault prediction module includes: the motor state level includes a third state, a second state and a first state.

[0043] Compared with the prior art, the present application provides a method for diagnosing motor faults of dust removal equipment for cigarette making machines based on deep transfer learning. In the case where the target domain data set lacks sufficient labels, the method uses the pre-trained model of the source domain for feature extraction and preliminary classification, which effectively addresses the problem of training prediction models caused by the scarcity of field data labels. Moreover, by using transfer learning, the model can learn common features from the source domain data and apply these features to the target domain, so that it can maintain a high recognition accuracy under variable working conditions, thereby improving the cross-working condition recognition capability of the model. In addition, by combining the motor temperature warning results, the method further enhances the prediction accuracy and reliability of the model under diversified operating conditions. This method does not require high technical skills of the operator, supports online automatic monitoring, can quickly identify potential problems of the equipment, reduces the need for manual on-site inspections, saves human resources, and promotes the construction of digital factories, improving the management efficiency and quality of dust removal equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0045] Figure 1The figure illustrates a prediction model flow chart of a method for diagnosing motor faults of dust removal equipment of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0046] Figure 2 The figure illustrates a flow chart of a method for diagnosing motor faults of dust removal equipment of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0047] Figure 3 The figure illustrates a flow chart of step S2 in the method for diagnosing motor faults of dust removal equipment of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0048] Figure 4 The figure illustrates a neural network architecture diagram of a method for diagnosing motor faults in a dust removal device of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0049] Figure 5 The figure illustrates a flow chart of step S4 in the method for diagnosing motor faults of dust removal equipment of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0050] Figure 6 The diagram illustrates a flow chart of step S41 in step S4 of a method for diagnosing motor faults of a dust removal device of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0051] Figure 7 The diagram illustrates a training loss curve of a motor fault diagnosis method for a dust removal device of a cigarette making machine based on deep transfer learning according to an embodiment of the present application.

[0052] Figure 8 The figure illustrates the accuracy change trend of the motor fault diagnosis method of the dust removal equipment of the cigarette making machine based on deep transfer learning according to the embodiment of the present application.

[0053] Fig. 9 A schematic diagram of a motor fault diagnosis system for dust removal equipment of a cigarette making machine based on deep transfer learning according to an embodiment of the present application is illustrated. DETAILED DESCRIPTION

[0054] With the continuous advancement of transfer learning technology, it is now possible to use existing labeled data to initially train the model, then apply it to new data sets and improve the model's fault diagnosis ability for new data through fine-tuning. This approach has shown its effectiveness in multiple engineering projects, especially in improving the model's prediction accuracy for new equipment data and its ability to adapt to different working conditions.

[0055] Facing the challenges of data collection for tobacco machinery and equipment, such as the difficulty in obtaining data and the lack of negative samples, as well as the equipment operating under constantly changing working conditions, transfer learning technology provides an effective solution. By building a data-driven motor fault diagnosis model, the key equipment status of tobacco machinery can be effectively monitored, thereby improving the management efficiency and stability of the equipment. The application prospects of this technology are very broad in the tobacco industry.

[0056] Therefore, a motor fault diagnosis method for dust removal equipment of a cigarette making machine based on deep transfer learning is proposed to overcome the problems mentioned in the above background technology.

[0057] Specifically, Figure 1 The figure shows a flow chart of a prediction model of a method for diagnosing motor faults of a dust removal device of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Figure 1 As shown in the figure, this method collects vibration and temperature signal data of motor equipment in the dust removal workshop, completes the construction of the motor vibration prediction model through data preprocessing, network construction, model training, model fine-tuning and other processes, and builds the motor temperature warning model through preprocessing of motor temperature data and working condition identification. In this way, the transfer learning technology and multi-dimensional information fusion technology are used to realize the fault prediction of motor equipment under variable working conditions and the abnormal motor temperature warning, thereby realizing the real-time status monitoring of motor equipment.

[0058] Below, the embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0059] Embodiment 1:

[0060] The data set used in this embodiment is the vibration test bench experimental data and the vibration signal data collected from the key motor equipment in the dust removal workshop. The vibration test bench data is used as the source domain data set D S There are 4 operating conditions in total, 4 major categories and 10 minor fault types. The data collected from the motor equipment in the dust removal workshop is used as the target domain dataset D T , a total of 4 working conditions.

[0061] This embodiment adopts a motor fault diagnosis method for dust removal equipment of a cigarette machine based on deep transfer learning, and its specific process is as follows:

[0062] Figure 2 The figure shows a flow chart of a method for diagnosing motor faults of a dust removal device of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Figure 2As shown, according to the embodiment of the present application, the motor fault diagnosis method of the dust removal equipment of the cigarette machine based on deep transfer learning includes: S1, real-time collection of vibration signals and temperature signals of the motor equipment in the dust removal workshop during operation, and storing the vibration signals and the temperature signals in a local database respectively; S2, performing data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performing data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal; S3, using a convolutional neural network as the core architecture of the transfer learning model, and defining a 6-layer neural network as the feature extraction layer of the transfer learning network in addition to the output layer; S4, training the transfer learning model to obtain a prediction model M T ; S5, using the prediction model M T Predict the vibration data and get the vibration prediction result y prd ; S6, perform secondary dynamic threshold judgment on the pre-processed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd ; S7, based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results.

[0063] In the embodiment of the present application, the step S1 collects the vibration signal and temperature signal of the motor equipment in the dust removal workshop in real time during operation, and stores the vibration signal and the temperature signal in the local database respectively. It should be understood that vibration and temperature are important indicators of the operating status of the equipment. Combining vibration and temperature signals can monitor the equipment status from multiple dimensions, thereby improving the comprehensiveness of fault detection. Therefore, a vibration and temperature sensor is installed to sample the vibration and temperature signals of the motor equipment during operation.

[0064] In an embodiment of the present application, the step S2 performs data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performs data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal. It should be understood that the vibration signal as raw data is not in the format required by the network model. Therefore, before using the vibration data of the motor for model training, the vibration signal is first preprocessed by data resampling, window segmentation, and data standardization to convert it into the format required by the model. Furthermore, considering that sensor failure or data transmission errors may cause abnormal values, missing values, etc. in the temperature signal, based on this, the temperature signal is preprocessed by null values ​​and abnormal values ​​to ensure the accuracy and integrity of the data.

[0065] Specifically, Figure 3 The figure shows a flow chart of step S2 in the method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to an embodiment of the present application. Figure 3 As shown, the step S2 includes: S21, resampling the vibration signal, eliminating missing values, and setting the batch size to 128; S22, adjusting the sliding window length L and the overlap rate r of the resampled data, wherein the sliding window length L=128, and the overlap rate r=0.5; S23, applying Z-Score standardization to the resampled vibration signal to eliminate the dimensionality effect and accelerate the model convergence, wherein the Z-Score calculation formula is as follows:

[0066]

[0067] Among them, X scaler is the standardized vibration data, X is the resampled vibration signal, μ is the mean of the data under the feature dimension, and σ is the standard deviation of the data under the feature; S24, removing abnormal values ​​and null values ​​from the temperature signal.

[0068] In the embodiment of the present application, the step S3 adopts a convolutional neural network as the core architecture of the transfer learning model. In addition to the output layer, a 6-layer neural network is defined as the feature extraction layer of the transfer learning network. It should be understood that transfer learning allows the model to use the knowledge trained in one field (source domain) to improve the learning efficiency and performance in another related field (target domain). However, it may be difficult to obtain a large amount of labeled data in the target domain (such as specific equipment in the field of tobacco manufacturing). Convolutional neural networks (CNN) are good at automatically extracting features from raw data, reducing the reliance on professional knowledge and manual feature engineering, which is particularly valuable when the amount of data is large and complex. Based on this, a convolutional neural network (CNN) is used as the core architecture of the transfer learning model to construct a feature extraction layer. In this way, the learning efficiency and performance of the model can be improved when the data is limited and complex.

[0069] Specifically, the step S3 includes: introducing a custom linear layer into the output layer as a classification layer, and reducing the overfitting phenomenon of the model by using the Dropout technology.

[0070] In particular, Figure 4 The figure shows a neural network architecture diagram of a method for diagnosing motor faults of a dust removal device of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Figure 4As shown in Figure 1, the architecture of the neural network includes convolutional layers (basic components for feature extraction), batch normalization layers (used to stabilize and accelerate the training process of the neural network), maximum pooling layers (used to reduce the spatial size of features and increase the level of abstraction of features while maintaining important information), average pooling layers (another pooling technique that calculates the average value within the region and can retain more feature information than the maximum pooling layer), and fully connected layers (the last few layers of CNN, used to map features to the final classification results). This architecture is the core of the transfer learning model, which is specifically used for feature extraction and classification.

[0071] In the embodiment of the present application, the step S4 is to train the transfer learning model to obtain a prediction model M T . It should be understood that the training process can adapt the model to the specific working environment and conditions in the tobacco manufacturing field. That is, model training is a key step in realizing the motor fault diagnosis method of the dust removal equipment of the cigarette machine based on deep transfer learning in the embodiment of the present application. It enables the model to make full use of existing knowledge, adapt to specific environments, and improve the accuracy and efficiency of fault diagnosis, thereby bringing significant economic and operational benefits to the tobacco manufacturing field. Therefore, the transfer learning model is further trained.

[0072] Specifically, Figure 5 The figure shows a flow chart of step S4 in the method for diagnosing motor faults of dust removal equipment of cigarette making machine based on deep transfer learning according to an embodiment of the present application. Figure 5 As shown, the step S4 includes: S41, dividing the source domain data set D s , target domain dataset D T , for the source domain dataset D s , the target domain dataset D T Perform data preprocessing operations respectively; S42, set the initialization network parameters for model training, wherein the learning rate ∈=1e-3 is set, the learning rate adjustment strategy selects the Step method, and the optimization strategy selects the SGD method; S43, the source domain data set D s Input the transfer learning model and use batch normalization technology to accelerate model convergence. After repeated iterations, the optimal pre-training model M is obtained. S , and save it locally; S44, the target domain dataset D T Input pre-trained model M S , and use batch normalization technology to fine-tune the model, and after repeated iterations, obtain the prediction model M that adapts to the target domain data T .

[0073] That is, this method is in the target domain dataset D T In the absence of sufficient labels, the pre-trained model M of the source domain is used SFeature extraction and preliminary classification are performed to effectively deal with the problem of predictive model training caused by the scarcity of field data labels. Moreover, by using transfer learning, the model can learn common features from the source domain data and apply these features to the target domain, so that it can maintain a high recognition accuracy under variable working conditions, thereby improving the model's cross-working condition recognition ability.

[0074] In particular, Figure 6 The figure shows a flow chart of step S41 in step S4 of the method for diagnosing motor faults of dust removal equipment of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Figure 6 As shown, the step S41 includes: S411, using a vibration test bench equipped with a vibration temperature sensor to collect vibration data of a motor using a faulty bearing and a normal bearing, and using the obtained labeled data as a source domain data set D S ; S412, install wireless vibration sensors on the motor equipment in the cigarette dust removal workshop, collect motor vibration data in the new field within a certain period of time, and store the data in the database as the target domain data set D T ; S413, the source domain data set D S , the target domain dataset D T Execute step S2 to process the data into a format suitable for the neural network.

[0075] In particular, in the embodiment of the present application, 100 iterations of training are performed on the source domain and target domain training sets, and each iteration corresponds to an update of the model parameters. Specifically, Figure 7 The figure shows a training loss curve of a method for diagnosing motor faults of a dust removal device of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Figure 7 As shown, as the number of iterations increases, the training loss generally shows a downward trend. This shows that the model gradually learns the characteristics of the data during the training process, and the prediction performance of the model is improving. In other words, the motor fault diagnosis method for dust removal equipment of a cigarette machine based on deep transfer learning in the embodiment of the present application shows good performance improvement and stability during the training process, the model training strategy is effective, and the final model is expected to achieve a high fault diagnosis accuracy in practical applications.

[0076] In particular, in the embodiment of the present application, the accuracy test is performed on the target domain verification set. Specifically, Figure 8 The figure shows the accuracy change trend of the method for diagnosing motor faults of dust removal equipment of cigarette making machine based on deep transfer learning according to the embodiment of the present application. Figure 8As shown in Figure 1, it specifically shows the changes in two accuracy indicators, namely, target domain training accuracy and target domain test accuracy, which reflect the performance of the model on training data and unseen data respectively. That is, Figure 8 It is a visual representation of the effect of the fine-tuning process in the evaluation step S44. By observing the changes in the target domain training accuracy (target train acc) and the test accuracy (target test acc), we can understand the learning progress and final performance of the model in the target domain. Figure 8 As can be seen from the figure, as the training progresses, both the target domain training accuracy and the test accuracy show an increasing trend. This indicates that the model gradually learns the characteristics of the target domain data during the training process and has good generalization ability on unseen data.

[0077] In the embodiment of the present application, the step S5 uses the prediction model M T Predict the vibration data and get the vibration prediction result y prd It should be understood that after the model training, the next step is the prediction process of vibration data. Through the prediction analysis of vibration data, predictive maintenance can be performed before the failure occurs to prevent potential equipment failure. Therefore, further use of the prediction model M T Predict the vibration data and get the vibration prediction result y prd .

[0078] In the embodiment of the present application, the step S6 performs a secondary dynamic threshold judgment on the pre-processed motor temperature data to construct a temperature warning model M. temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd It should be understood that the motor generates heat during operation, and continuous temperature monitoring is essential to prevent overheating and failure. Through dynamic monitoring and early warning of temperature data, measures can be taken before a failure occurs to avoid potential damage. Furthermore, considering that the secondary dynamic threshold judgment can adjust the early warning threshold according to real-time data, it can be adjusted according to real-time data to reduce false alarms and missed alarms and improve the accuracy of the early warning system. Based on this, the secondary dynamic threshold judgment is performed on the preprocessed motor temperature data and a temperature early warning model M is constructed. temp This helps to achieve automated temperature monitoring and early warning, thereby reducing the need for manual on-site inspections and saving human resources.

[0079] In the embodiment of the present application, the step S7, based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client, and display the equipment fault diagnosis information based on the prediction results. It should be understood that pushing the motor status to the client in real time based on the prediction results is crucial to ensure the effectiveness and practicality of the motor fault diagnosis method. By defining different levels of status levels, the severity of the fault can be quantified, and providing customers with clear motor status information and fault diagnosis results will help customers implement a graded response strategy to avoid faults and reduce unexpected downtime.

[0080] Specifically, the motor status level includes level 3 status, level 2 status and level 1 status. If the vibration warning and temperature warning appear at the same time, the level 3 status (alarm) is pushed to the client, if there is only vibration or temperature warning, the level 2 status (warning) is pushed to the client, if there is no warning, the level 1 status (normal) is pushed to the client, and the equipment fault diagnosis information is displayed according to the prediction results.

[0081] In summary, the method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to the embodiment of the present application is explained. The method collects vibration and temperature signals of motor equipment in dust removal workshop in real time, performs data preprocessing, and uses convolutional neural network to construct a transfer learning model for feature extraction and training to predict vibration prediction results y prd At the same time, the temperature warning model M is constructed by performing secondary dynamic threshold judgment on the preprocessed motor temperature data. temp , and perform diagnosis to obtain temperature warning results prd The present invention realizes the diagnosis of motor faults through vibration prediction and temperature early warning models, and pushes the status level and fault information to the client, thereby improving the management efficiency and operation stability of the equipment.

[0082] In addition, it should be noted that although the motor fault diagnosis method for cigarette machine dust removal equipment based on deep transfer learning is designed and optimized for cigarette machine dust removal equipment, it does not mean that it can only be used for cigarette machine dust removal equipment. The core advantage of the transfer learning model is the ability to apply knowledge learned from one field (source domain) to another field (target domain). This means that as long as the data features of the new equipment have a certain similarity with the data features used when training the model, the model has the potential to adapt to the new equipment through appropriate adjustments. Moreover, the various components of the method (such as data acquisition, preprocessing, feature extraction, model training, and fault diagnosis) can be regarded as relatively independent modules. This modular design allows the method to be adjusted and reorganized according to the needs of different equipment. Therefore, the technical framework and modular design of the method based on deep transfer learning make it potentially adjustable and applicable to the fault diagnosis of motors of other industrial equipment. Of course, this may require retraining and adjustment of the model to adapt to the specific characteristics and needs of the new equipment.

[0083] Embodiment 2:

[0084] Fig. 9 The figure shows a schematic diagram of a motor fault diagnosis system for a dust removal device of a cigarette machine based on deep transfer learning according to an embodiment of the present application. Fig. 9 As shown, the motor fault diagnosis system 10 for dust removal equipment of a cigarette making machine based on deep transfer learning according to the embodiment of the present application includes: a motor data acquisition module 100, which is used to collect vibration signals and temperature signals of the motor equipment in the dust removal workshop in real time during operation, and store the vibration signals and the temperature signals in a local database respectively; a motor data preprocessing module 200, which is used to perform data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and to perform data preprocessing operations on null values ​​and abnormal values ​​on the temperature signal; a transfer learning model building module 300, which is used to adopt a convolutional neural network as the core architecture of the transfer learning model, and define a 6-layer neural network as the feature extraction layer of the transfer learning network in addition to the output layer; a transfer learning model training module 400, which is used to train the transfer learning model to obtain a prediction model M T ; Motor vibration prediction module 500, for using the prediction model M T Predict the vibration data and get the vibration prediction result y prd The motor temperature warning module 600 is used to perform a secondary dynamic threshold judgment on the pre-processed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temg Diagnose the temperature data and get the temperature warning result t prd ; Motor fault prediction module 700, for predicting the vibration result y based on the prdand the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results.

[0085] Here, those skilled in the art can understand that the specific functions and operations of each step in the above-mentioned cigarette machine dust removal equipment motor fault diagnosis system 10 based on deep transfer learning have been referred to above. Figure 2 The description of the motor fault diagnosis method of the dust removal equipment of a cigarette making machine based on deep transfer learning has been introduced in detail, and therefore, its repeated description will be omitted.

[0086] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit of the technical solution of the present invention.

Claims

1. A method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning, characterized in that: include: S1. Real-time collection of vibration signals and temperature signals of the motor equipment in the dust removal workshop during operation, and storage of the vibration signals and temperature signals in a local database respectively; S2, performing data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performing data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal; S3. Use convolutional neural network as the core architecture of the transfer learning model. In addition to the output layer, define a 6-layer neural network as the feature extraction layer of the transfer learning network. S4: Train the transfer learning model to obtain a prediction model M T ; S5, using the prediction model M T Predict the vibration data and get the vibration prediction result y prd ; S6. Perform secondary dynamic threshold judgment on the pre-processed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd ; S7, based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results; Wherein, said S4 includes: S41. Divide the source domain dataset D s , target domain dataset D T , for the source domain dataset D s , the target domain dataset D T Perform data preprocessing operations respectively; S42, setting the initialization network parameters for model training, wherein the learning rate ∈=1e-3 is set, the learning rate adjustment strategy selects the Step method, and the optimization strategy selects the SGD method; S43, the source domain dataset D s Input the transfer learning model and use batch normalization technology to accelerate model convergence. After repeated iterations, the optimal pre-training model M is obtained. S , and save it locally; S44, the target domain dataset D T Input pre-trained model M S , and use batch normalization technology to fine-tune the model, and after repeated iterations, obtain the prediction model M that adapts to the target domain data T .

2. The method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to claim 1 is characterized in that: The S2 comprises: S21, resample the vibration signal, eliminate missing values, and set the batch size to 128; S22, adjusting the sliding window length L and the overlap rate r of the resampled data, wherein the sliding window length L=128, and the overlap rate r=0.5; S23. Apply Z-Score standardization to the resampled vibration signal to eliminate the dimension effect and accelerate the convergence of the model. The Z-Score calculation formula is as follows: Among them, X scaler is the standardized vibration data, X is the resampled vibration signal, μ is the mean of the data under this feature dimension, and σ is the standard deviation of the data under this feature; S24, removing abnormal values ​​and null values ​​from the temperature signal.

3. The method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to claim 2 is characterized in that: The S3 includes: the output layer introduces a custom linear layer as a classification layer, and uses the Dropout technology to reduce the overfitting phenomenon of the model.

4. The method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to claim 3 is characterized in that: The S41 includes: S411, using a vibration test bench equipped with a vibration temperature sensor to collect vibration data of motors using faulty bearings and normal bearings, and using the obtained labeled data as the source domain data set D S ; S412, install wireless vibration sensors on the motor equipment in the cigarette dust removal workshop, collect the motor vibration data of the new field in a certain period of time, and store the data in the database as the target domain data set D T ; S413: the source domain dataset D S , the target domain dataset D T Execute step S2 to process the data into a format suitable for the neural network.

5. The method for diagnosing motor faults of dust removal equipment of cigarette making machine based on deep transfer learning according to claim 4 is characterized in that: The S7 includes: the motor state level includes a third state, a second state and a first state.

6. The method for diagnosing motor faults of dust removal equipment of cigarette making machines based on deep transfer learning according to claim 5 is characterized in that: The S7 includes: if the vibration warning and the temperature warning appear at the same time, the third-level status is pushed to the client; if there is only a vibration or temperature warning, the second-level status is pushed to the client; if there is no warning, the first-level status is pushed to the client, and the equipment fault diagnosis information is displayed according to the prediction result.

7. A motor fault diagnosis system for dust removal equipment of cigarette making machine based on deep transfer learning, characterized in that: include: The motor data acquisition module is used to collect the vibration signal and temperature signal of the motor equipment in the dust removal workshop in real time during operation, and store the vibration signal and the temperature signal in a local database respectively; A motor data preprocessing module, used for performing data resampling, window segmentation, and data standardization preprocessing operations on the vibration signal, and performing data preprocessing operations of null values ​​and abnormal values ​​on the temperature signal; The transfer learning model building module is used to use the convolutional neural network as the core architecture of the transfer learning model. In addition to the output layer, a 6-layer neural network is defined as the feature extraction layer of the transfer learning network; The transfer learning model training module is used to train the transfer learning model to obtain the prediction model M T ; The motor vibration prediction module is used to use the prediction model M T Predict the vibration data and get the vibration prediction result y prd ; The motor temperature warning module is used to perform secondary dynamic threshold judgment on the preprocessed motor temperature data and build a temperature warning model M temp , and using the temperature warning model M temp Diagnose the temperature data and get the temperature warning result t prd ; A motor fault prediction module is used to predict the motor fault based on the vibration prediction result y prd and the temperature warning result t prd , push the motor status level to the client and display the equipment fault diagnosis information based on the prediction results; Wherein, the transfer learning model training module includes: Divide the source domain dataset D s , target domain dataset D T , for the source domain dataset D s , the target domain dataset D T Perform data preprocessing operations respectively; Set the initial network parameters for model training, where the learning rate ∈=1e-3, the learning rate adjustment strategy selects the Step method, and the optimization strategy selects the SGD method; The source domain dataset D s Input the transfer learning model and use batch normalization technology to accelerate model convergence. After repeated iterations, the optimal pre-training model M is obtained. S , and save it locally; The target domain dataset D T Input pre-trained model M S , and use batch normalization technology to fine-tune the model, and after repeated iterations, obtain the prediction model M that adapts to the target domain data T .

8. The motor fault diagnosis system for dust removal equipment of a cigarette making machine based on deep transfer learning according to claim 7 is characterized in that: The motor fault prediction module includes: the motor state level includes a third-level state, a second-level state and a first-level state.

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