Industrial equipment status monitoring method, system, medium, and equipment

Abstract

The present invention discloses a method, system, medium, and equipment for industrial equipment state monitoring, relating to the technical field of state monitoring. The method comprises: collecting monitoring signals of industrial equipment under various operating states through sensors and performing preprocessing; performing time-frequency domain conversion on the preprocessed monitoring signals to obtain a monitoring feature data set, and dividing the monitoring feature data set into a training set and a validation set according to different proportions; constructing a residual multi-scale state monitoring model, and using the training set and the validation set to train and validate the residual multi-scale state monitoring network model to obtain a final state monitoring model; the state monitoring model comprises a pre-processing layer, multiple feature extraction layers, and an operating state discrimination layer connected in series, wherein the feature extraction layer is composed of multiple Bottle2neck modules; and using the final state monitoring model to perform state monitoring on the monitored signals. The present invention enables accurate judgment of the operating state of industrial equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of condition monitoring, and in particular to a method, system, medium, and equipment for monitoring the condition of industrial equipment. Background Art

[0002] As the core of the manufacturing industry, industrial equipment has always been a focus of R&D investment. With the widespread application of intelligent manufacturing and automation technologies in the industrial sector, the operational reliability and production efficiency of industrial equipment have become key concerns for companies. However, industrial equipment components are subject to wear and aging over extended periods of operation. This not only increases maintenance costs but can also lead to serious production accidents and economic losses. Therefore, to ensure the safe and reliable operation of industrial equipment, various operational status monitoring methods are increasingly being applied in the field.

[0003] Common methods for condition monitoring of industrial equipment include rule-based and model-based monitoring. These methods rely on expert systems and modeling to achieve real-time monitoring of operating conditions. However, these methods have poor generalization capabilities and are difficult to adapt to the increasingly diverse market environment. Meanwhile, the rapid development of machine learning technology has given rise to data-driven equipment condition monitoring methods. These data-driven intelligent condition monitoring methods no longer rely on expert knowledge and have strong data adaptability, making them the preferred method for industrial machinery condition monitoring.

[0004] As the operating conditions of industrial equipment become more complex, the collected operating condition data also becomes more complicated. When extracting features from the operating status data generated under different operating conditions, the features extracted under different operating conditions will differ in expression scale. Current data-driven industrial equipment status monitoring methods often ignore these differences in the scale of extracted features and cannot fully utilize feature information at different scales, resulting in a decrease in the quality of the collected feature information, leading to a decrease in the accuracy of operating condition monitoring, which restricts the development of data-driven industrial equipment status monitoring methods.

[0005] In summary, existing data-driven intelligent condition monitoring methods cannot fully utilize feature information at different scales. Summary of the Invention

[0006] The purpose of the present invention is to solve the problem that existing condition monitoring methods cannot fully utilize feature information at different scales, and to propose an industrial equipment condition monitoring method, comprising the following steps:

[0007] S1. Collect monitoring signals of industrial equipment under various operating conditions through sensors and perform preprocessing;

[0008] S2. Perform time-frequency domain conversion on the preprocessed monitoring signal to obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions;

[0009] S3. Build a residual multi-scale condition monitoring model, use the training set and validation set to train and validate the residual multi-scale condition monitoring network model, and obtain the final condition monitoring model;

[0010] The state monitoring model includes a pre-processing layer, multiple feature extraction layers and an operation state discrimination layer connected in series, wherein the feature extraction layer is composed of multiple Bottle2neck modules;

[0011] S4. Use the final state monitoring model to perform state monitoring on the signal to be monitored.

[0012] Furthermore, the preprocessing method includes: truncating the monitoring signal segments during the startup and shutdown phases of the industrial equipment, and removing abnormal values ​​of the monitoring signal by using the isolation forest method.

[0013] Furthermore, the one-dimensional monitoring signal is converted into a two-dimensional feature map through short-time Fourier transform to obtain a monitoring feature data set.

[0014] Furthermore, the pre-processing layer includes: a convolution layer, a normalization layer, a ReLU activation layer, and a maximum pooling layer; the pre-processing layer completes the preliminary extraction of monitoring features.

[0015] Furthermore, the convolution layer of the pre-processing layer is set with a 7×7 convolution kernel.

[0016] Furthermore, the condition monitoring model includes four feature extraction layers, the first feature extraction layer has three Bottle2neck modules connected in series, the second feature extraction layer has four Bottle2neck modules connected in series, the third feature extraction layer has six Bottle2neck modules connected in series, and the fourth feature extraction layer has three Bottle2neck modules connected in series;

[0017] The Bottle2neck module consists of a dimensionality reduction convolution layer, a feature segmentation layer, a multi-scale feature extraction layer, a feature merging layer, and a channel recovery layer connected in series. The input of the dimensionality reduction convolution layer is connected to the output of the channel recovery layer through a residual connection.

[0018] The input feature map x0 is convolved with a 1×1 convolution operation through the dimensionality reduction convolution layer to obtain the feature map x. The feature segmentation layer evenly divides the output feature map x of the dimensionality reduction convolution layer into s feature map subsets. The feature map subsets are denoted by x. i Represents, where i∈{1,2,...,s};

[0019] x iInput the multi-scale feature extraction layer to get y i , expressed as:

[0020]

[0021] G1=σ(W1*x1+b1)

[0022] G2=σ(W2*K2+b2)

[0023] Among them, ⊙ represents element-by-element multiplication, which is used to apply the gated map to filter the information in the feature map; G1 and G2 represent the gated maps of different inputs respectively, and K i () represents the i-th convolution operator, W1 and W2 are learnable convolution kernels, b1 and b2 are bias terms, and σ is the sigmoid activation function;

[0024] y i After the feature merging layer performs channel splicing to obtain y, y outputs the operating status of the industrial equipment through the operating status discrimination layer.

[0025] Furthermore, the running state discrimination layer consists of an average pooling.

[0026] The present invention also provides an industrial equipment status monitoring system, comprising:

[0027] Data acquisition module, used to collect monitoring signals of industrial equipment under various operating conditions through sensors and perform preprocessing;

[0028] The data processing module is used to perform time-frequency domain conversion on the pre-processed monitoring signal, obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions;

[0029] The model construction module is used to construct a residual multi-scale condition monitoring model, and use the training set and validation set to train and validate the residual multi-scale condition monitoring network model to obtain the final condition monitoring model;

[0030] The state monitoring model comprises: a pre-processing layer, a plurality of feature extraction layers and an operation state discrimination layer connected in series, wherein the feature extraction layer is composed of a plurality of Bottle2neck modules;

[0031] The condition monitoring module is used to perform condition monitoring on the signal to be monitored using the final condition monitoring model.

[0032] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned industrial equipment status monitoring method is implemented.

[0033] The present invention also proposes an electronic device, including a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the above-mentioned industrial equipment status monitoring method.

[0034] The beneficial effects brought about by the technical solution provided by the present invention are:

[0035] The monitoring model of the present invention is provided with multiple feature extraction layers for extracting monitoring signal features. Each feature extraction layer contains multiple Bottle2neck modules. Feature extraction is performed on feature map subsets through multiple parallel feature extraction convolution layers, and channel splicing operations are performed. This improves the model's ability to extract feature expressions of monitoring signals at different scales, allowing the model to fully utilize feature information at different scales to accurately judge the operating status of industrial equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of an industrial equipment status monitoring method according to an embodiment of the present invention;

[0037] Figure 2 This is a structural diagram of a state monitoring model according to an embodiment of the present invention;

[0038] Figure 3 This is the Bottle2neck module structure diagram;

[0039] Figure 4 is an accuracy curve diagram of the operating status monitoring method according to an embodiment of the present invention;

[0040] Figure 5 It is a block diagram of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0042] Example 1: A flow chart of the industrial equipment status monitoring method according to an embodiment of the present invention is as follows Figure 1 , specifically including the following steps:

[0043] S1. Use sensors to collect monitoring signals from industrial equipment under various operating conditions and perform preprocessing. Different sensors are required for different industrial equipment. For example, vibration sensors can measure the vibration of objects and are used to monitor the operating status of mechanical equipment, such as gearboxes, motors, pumps, compressors, etc. By monitoring vibration signals, gear meshing problems and bearing failures can be discovered in a timely manner. Temperature sensors measure temperature changes and output electrical signals. They are used to monitor the operating temperature of equipment to prevent overheating that affects product quality or equipment life, such as temperature monitoring of motors, pumps, compressors, bearings, etc. Pressure sensors detect the pressure of gas or liquid and convert it into electrical signals. They are widely used in hydraulic and pneumatic system control and to detect the working status of pumps and compressors. Flow sensors measure the flow rate of liquid or gas and are used to monitor the flow rate of fluids in pipelines, channels, or systems to ensure the normal operation of equipment, such as flow monitoring of pumps, compressors, etc.

[0044] The preprocessing methods include: (1) truncating unstable monitoring signal segments during the startup and shutdown phases of industrial equipment; and (2) removing outliers from the monitoring signals through the isolation forest method. Isolation forest is an unsupervised learning algorithm for anomaly monitoring that can effectively remove outliers from the monitoring signals.

[0045] S2. Perform time-frequency domain conversion on the preprocessed monitoring signal to obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions.

[0046] The short-time Fourier transform (STFT) converts the one-dimensional monitoring signal into a two-dimensional feature map, resulting in a monitoring feature data set. The short-time Fourier transform (STFT) converts a one-dimensional time-domain signal into a two-dimensional time-frequency domain representation. The spectrum of each frame is arranged in chronological order, with the horizontal axis representing time and the vertical axis representing frequency. The STFT allows the temporal variation of the monitoring signal's frequency components into a two-dimensional feature map, facilitating analysis of the signal's time-frequency characteristics.

[0047] S3. Construct a residual multi-scale condition monitoring model, use the training set and validation set to train and validate the residual multi-scale condition monitoring network model, and obtain the final condition monitoring model.

[0048] The condition monitoring model includes the following: a pre-processing layer, multiple feature extraction layers, and an operation status discrimination layer, which are connected in series. The feature extraction layer is composed of multiple Bottle2neck modules. Figure 2 , Figure 2 It is a structural diagram of a condition monitoring model according to an embodiment of the present invention.

[0049] The pre-processing layer includes a convolutional layer, a normalization layer, a ReLU activation layer, and a max pooling layer. The pre-processing layer performs the initial extraction of monitoring features. The convolutional layer uses a 7×7 convolution kernel to capture large-scale local information in the feature map. It is then connected to a normalization layer to accelerate training and improve model stability. The features from the normalization layer are then connected to a ReLU activation function layer to improve model nonlinearity.

[0050] The condition monitoring model includes four feature extraction layers. The first feature extraction layer includes three Bottle2neck modules, the second feature extraction layer includes four Bottle2neck modules, the third feature extraction layer includes six Bottle2neck modules, and the fourth feature extraction layer includes three Bottle2neck modules.

[0051] The running state discrimination layer consists of an average pooling.

[0052] The Bottle2neck module consists of a cascade of dimensionality reduction convolutional layers, feature segmentation layers, multi-scale feature extraction layers, feature merging layers, and channel restoration layers. The input of the dimensionality reduction convolutional layers is connected to the output of the channel restoration layer via a residual connection. The dimensionality reduction convolutional layers and channel restoration layers are set to 1×1 convolutions.

[0053] The input feature map x0 is convolved with a 1×1 convolution operation through the dimensionality reduction convolution layer to obtain the feature map x. The feature segmentation layer evenly divides the output feature map x of the dimensionality reduction convolution layer into s feature map subsets. The feature map subsets are denoted by x. i Represents, where i∈{1,2,...,s}. Each feature subset x i It has the same spatial size as the feature map x0, but the number of channels is 1 / s of the feature map x0.

[0054] x i Input the multi-scale feature extraction layer to get y i In the multi-scale feature extraction layer, except for the first feature map subset x1, each feature map subset x i There are corresponding 3×3 convolutions, using K i Indicates. Use y i K i () output. When i is greater than 3, the feature subset x i With K i-1 The output of () is added as K i(). In order to reduce the parameters while increasing s, the 3×3 convolution of x1 is omitted. At the same time, in order to improve the feature expression ability of the first feature map subset x1, a gated feature fusion module (Gated Fully Fusion, GFF) is set up to fuse the inputs from the first feature subset x1 and K2(). Therefore, y i can be written as:

[0055]

[0056] G1=σ(W1*x1+b1)

[0057] G2=σ(W2*K2+b2)

[0058] Where ⊙ represents element-wise multiplication, which is used to apply the gated map to filter the information in the feature map. G1 and G2 represent the gated maps of different inputs, respectively, and K i () represents the i-th convolution operator, W1 and W2 are learnable convolution kernels, b1 and b2 are bias terms, and σ is the sigmoid activation function.

[0059] Each 3×3 convolution operator K i () can be obtained from all feature subsets {x j ,j≤i} receives feature information. Feature subset x j After the 3×3 convolution operator, the output result has a ratio of x j Larger receptive fields. The output of the Bottle2neck module contains receptive fields of varying numbers and combinations.

[0060] y i ,i∈{1,2,...,s} is spliced ​​through the feature merging layer, and the multiple small segment feature maps y after parallel processing of the multi-scale feature extraction layer are i Splicing is performed in the dimension direction to increase the dimension of the feature.

[0061] Finally, the operating status of the industrial equipment is output through the operating status judgment layer. Figure 3 , Figure 3 This is the Bottle2neck module structure diagram. Figure 3 The s in is 4.

[0062] S4. Use the final state monitoring model to perform state monitoring on the signal to be monitored.

[0063] Example 2: The present invention also provides an industrial equipment status monitoring system, comprising:

[0064] Data acquisition module, used to collect monitoring signals of industrial equipment under various operating conditions through sensors and perform preprocessing;

[0065] The data processing module is used to perform time-frequency domain conversion on the pre-processed monitoring signal, obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions;

[0066] The model construction module is used to construct a residual multi-scale condition monitoring model, and use the training set and validation set to train and validate the residual multi-scale condition monitoring network model to obtain the final condition monitoring model;

[0067] The state monitoring model comprises: a pre-processing layer, a plurality of feature extraction layers and an operation state discrimination layer connected in series, wherein the feature extraction layer is composed of a plurality of Bottle2neck modules;

[0068] The condition monitoring module is used to perform condition monitoring on the signal to be monitored using the final condition monitoring model.

[0069] To demonstrate the effectiveness of this method, the following gearbox experiment was conducted. The test bench consists of two motors, a gearbox, a flywheel, a data acquisition board, and a computer. The two motors used in the experiment are ABB mv1008-225 (1.2kW). One of them is used as a prime mover to drive the multi-stage gearbox, and the other is used as an asynchronous generator to simulate various resistance torques. All components are connected to the drive shaft through a coupling. During the test, the input speed of the equipment was set to 1400 rpm, and the speeds of the two meshing gears in the gearbox were 1184 rpm and 840 rpm, respectively. Seven types of operating states of the gearbox were simulated and tested to verify this method.

[0070] This dataset contains vibration signals collected under normal operating conditions and six abnormal operating conditions. The data was collected using two vibration sensors, perpendicular to the axis, at a sampling frequency of 10,240 Hz. The sampling period was set to 100 seconds, resulting in 4,096,000 data points for each condition. The signals were divided into 1,400 samples, each containing 4,000 data points. For each condition, the signal samples were split into a training set and a validation set with a ratio of 800:200.

[0071] As shown in Table 1, the confusion matrix visualization of the operating status monitoring on this dataset is shown. The labels are C0-healthy, C1-broken tooth, C2-loose gear, C3-cracked gear, C4-broken bearing outer ring, C6-loose gear fixing plate, and C7-axial imbalance. The numbers represent the number of samples. For example, the number of samples with the predicted label C0 and the true label C0 is 207; the number of samples with the predicted label C0 and the true label C1 is 1, indicating that this sample was misclassified by the model. As can be seen in Table 1, the labels all converge on the diagonal of the confusion matrix, and the true labels of various operating states can mostly correspond to the predicted labels. It can be seen that the operating status monitoring method of the present invention can achieve accurate classification of various operating states, indicating that the operating status monitoring method of the present invention can well complete the operating status monitoring task.

[0072] Table 1

[0073]

[0074]

[0075] As shown in Table 2 or Figure 4 As shown in Figure 2, four running status monitoring methods, namely VGG16, Resnet50, Resnet18, and GoogLeNet, are used to compare with the running status monitoring method of the present invention. Table 2 shows the results of the running status monitoring method of the present invention and the other four running status monitoring methods. It can be seen that the accuracy and F1 score of the running status monitoring method of the present invention are higher than those of the other four methods as a whole. Figure 4 The accuracy curve of the operating status monitoring method of the present invention is shown in the figure. In this experiment, the accuracy increased rapidly in the first few tests, and then fluctuated at a high accuracy, further verifying the superiority of the method of the present invention for operating status monitoring of industrial equipment.

[0076] Table 2

[0077] Model Prediction accuracy (%) Model of the present invention 99.319 VGG16 99.150 Resnet50 98.231 Resnet18 98.521 GoogLeNet 95.248

[0078] Embodiment 3: In an exemplary embodiment, a computer-readable storage medium is included, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned industrial equipment status monitoring method is implemented.

[0079] Example 4: Please refer to Figure 5 In an exemplary embodiment, an electronic device 500 is further included, including at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0080] The communication bus 502 is used to implement the connection and communication between these components.

[0081] The processor 501 is configured to execute instructions stored in the memory 505 so that the electronic device 500 executes the above-mentioned industrial equipment status monitoring method.

[0082] The user interface 503 may include a display screen, and the optional user interface 503 may also include a standard wired interface or a wireless interface.

[0083] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0084] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the state of industrial equipment, characterized in that: The following steps are involved: S1. Collect monitoring signals of industrial equipment under various operating conditions through sensors and perform preprocessing; S2. Perform time-frequency domain conversion on the preprocessed monitoring signal to obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions; S3. Build a residual multi-scale condition monitoring model, use the training set and validation set to train and validate the residual multi-scale condition monitoring network model, and obtain the final condition monitoring model; The state monitoring model includes a pre-processing layer, multiple feature extraction layers and an operation state discrimination layer connected in series, wherein the feature extraction layer is composed of multiple Bottle2neck modules; S4. Using the final state monitoring model to perform state monitoring on the signal to be monitored; The condition monitoring model includes four feature extraction layers. The first feature extraction layer has three Bottle2neck modules in series, the second feature extraction layer has four Bottle2neck modules in series, the third feature extraction layer has six Bottle2neck modules in series, and the fourth feature extraction layer has three Bottle2neck modules in series. The Bottle2neck module consists of a dimensionality reduction convolution layer, a feature segmentation layer, a multi-scale feature extraction layer, a feature merging layer, and a channel recovery layer connected in series. The input of the dimensionality reduction convolution layer is connected to the output of the channel recovery layer through a residual connection. Input feature map After the dimensionality reduction convolution layer performs a 1×1 convolution operation, the feature map x is obtained. The feature segmentation layer evenly divides the output feature map x of the dimensionality reduction convolution layer into s feature map subsets. The feature map subsets are used Indicates that ; Feature map subset Input the multi-scale feature extraction layer to obtain features , expressed as: Among them, ⊙ represents element-wise multiplication, which is used to apply the gated map to filter the information in the feature map; and Represent the gating mapping of different inputs, represents the i-th convolution operator, and is a learnable convolution kernel, and is the bias term, σ is the sigmoid activation function; After the feature merging layer performs channel splicing to obtain y, y outputs the operating status of the industrial equipment through the operating status discrimination layer.

2. The method for monitoring the state of industrial equipment according to claim 1, wherein: The preprocessing method includes: truncating the monitoring signal segments during the startup and shutdown phases of industrial equipment, and removing outliers in the monitoring signal through the isolation forest method.

3. The industrial equipment status monitoring method according to claim 1, characterized in that: The one-dimensional monitoring signal is converted into a two-dimensional feature map through short-time Fourier transform to obtain a monitoring feature data set.

4. The method for monitoring the state of industrial equipment according to claim 1, wherein: The pre-processing layer includes: a convolution layer, a normalization layer, a ReLU activation layer, and a maximum pooling layer; the pre-processing layer completes the preliminary extraction of monitoring features.

5. The method for monitoring the state of industrial equipment according to claim 4, characterized in that: The convolution layer of the pre-processing layer is set with a 7×7 convolution kernel.

6. The method for monitoring the state of industrial equipment according to claim 1, characterized in that: The running state discrimination layer consists of an average pooling.

7. An industrial equipment status monitoring system, characterized in that: include: Data acquisition module, used to collect monitoring signals of industrial equipment under various operating conditions through sensors and perform preprocessing; The data processing module is used to perform time-frequency domain conversion on the pre-processed monitoring signal, obtain a monitoring feature data set, and divide the monitoring feature data set into a training set and a validation set according to different proportions; The model construction module is used to construct a residual multi-scale condition monitoring model, and use the training set and validation set to train and validate the residual multi-scale condition monitoring network model to obtain the final condition monitoring model; The state monitoring model comprises: a pre-processing layer, a plurality of feature extraction layers and an operation state discrimination layer connected in series, wherein the feature extraction layer is composed of a plurality of Bottle2neck modules; A condition monitoring module, used for performing condition monitoring on the signal to be monitored using the final condition monitoring model; The condition monitoring model includes four feature extraction layers. The first feature extraction layer has three Bottle2neck modules in series, the second feature extraction layer has four Bottle2neck modules in series, the third feature extraction layer has six Bottle2neck modules in series, and the fourth feature extraction layer has three Bottle2neck modules in series. The Bottle2neck module consists of a dimensionality reduction convolution layer, a feature segmentation layer, a multi-scale feature extraction layer, a feature merging layer, and a channel recovery layer connected in series. The input of the dimensionality reduction convolution layer is connected to the output of the channel recovery layer through a residual connection. Input feature map After the dimensionality reduction convolution layer performs a 1×1 convolution operation, the feature map x is obtained. The feature segmentation layer evenly divides the output feature map x of the dimensionality reduction convolution layer into s feature map subsets. The feature map subsets are used Indicates that ; Feature map subset Input the multi-scale feature extraction layer to obtain features , expressed as: Among them, ⊙ represents element-wise multiplication, which is used to apply the gated map to filter the information in the feature map; and Represent the gating mapping of different inputs, represents the i-th convolution operator, and is a learnable convolution kernel, and is the bias term, σ is the sigmoid activation function; After the feature merging layer performs channel splicing to obtain y, y outputs the operating status of the industrial equipment through the operating status discrimination layer.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: The electronic device comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used for communication, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device executes the method according to any one of claims 1 to 6.

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