Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks
Abstract
:1. Introduction
- A GANomaly-LSTM method is proposed, which can effectively avert the teasers of gradient disappearance and gradient explosion during the course of training time-series data, and the extracted features can achieve a good clustering effect.
- This method can realize anomaly detection in the absence of abnormal sample training.
- The proposed model performs well in anomaly detection of phase-missing current signals and vibration signals, verified under three input conditions.
- A mass of experiments have been actualized, and the results indicate that the proposed method achieves excellent advantages in effectiveness and performance compared with other classical methods.
2. Anomaly Detection Model of Linear Motor Feeding System
2.1. Factors Affecting the Running State of Linear Motor Feeding System
2.2. Anomaly Detection Model
2.2.1. The Structure of GANomaly
2.2.2. The Structure of LSTM
2.2.3. Proposed Method
The Structure of GANomaly-LSTM
Loss Function
Model Validation and Evaluation Criteria
2.3. Anomaly Detection Process
- STEP1: The sensor collects relevant time-series signals in real time, and a sample matrix is constructed for the collected normal samples.
- STEP2: The normal-sample matrix is inputted into the encoder , then the LSTM network extracts the time-series features, later the latent feature vector is gained through three-layer fully connected layers, and lastly the reconstructed data is obtained through the decoder .
- STEP3: The discriminant network discriminates the normal samples and the reconstructed data samples , and continuously narrows the gap between the two during the confrontational training process.
- STEP4: The reconstructed data is inputted into the reconstructed encoder network, and then the latent feature vector is attained.
- STEP1: After the model training, the normal and abnormal samples are used to construct the sample matrix for testing. At this time, the discriminant network is no longer used. In the testing stage, network model parameters are fixed and outputted by training stage. The potential feature vector is obtained through , then the reconstructed data is gained through , and finally, the potential feature vector of the reconstructed data is garnered through .
- STEP2: The anomaly score of the input sample is computed according to the loss functions and . The final anomaly score is obtained by normalizing .
- STEP3: It is determined whether the input sample is abnormal or not according to the relationship between the abnormal score and a certain threshold . If , the input sample will be classified as an abnormal sample; otherwise it is a normal sample.
3. Experimental Setup and Feature Extraction
3.1. Construction of Experimental Platform and Data Collection
3.2. Design of Experimental Working Conditions and Description of Data Samples
3.3. Experimental Environment and Model Parameters
3.4. Signal Feature Extraction
4. Analysis of Experimental Results
- Case1: Vibration data sample;
- Case2: Current data sample;
- Case3: Vibration and Current Composite Data Sample.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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LSTM | RNN | |
---|---|---|
Advantages | 1. The problem of gradient disappearance and gradient explosion can be overcome when training long sequence data; 2. Able to learn long-term dependence; 3. Simple to implement. | 1. Train the time-series data; 2. The network is simple and easy to operate. |
Limitations | 1. Because of the large amount of computation in network training, high performance computers are needed. | 1. Cannot process long time-series data; 2. There are problems of gradient disappearance and gradient explosion. |
Number | Equipment Name |
---|---|
1 | Computer processing center |
2 | Linear motor feeding system controller |
3 | Data acquisition card |
4 | Signal conditioner |
5 | Accelerometer |
6 | X-axis feeding system |
7 | Y-axis feeding system |
Number | Load (kg) | X/Y Axis | Displacement Interval (mm) | Speed (mm/s) |
---|---|---|---|---|
1 | No load | X axis | 60, 180, 300, 420 | 60 |
2 | No load | X axis | 60, 180, 300, 420 | 80 |
3 | No load | X axis | 60, 180, 300, 420 | 100 |
4 | No load | X axis | 60, 180, 300, 420 | 120 |
5 | No load | Y axis | 60, 180, 300, 420 | 60 |
6 | No load | Y axis | 60, 180, 300, 420 | 80 |
7 | No load | Y axis | 60, 180, 300, 420 | 100 |
8 | No load | Y axis | 60, 180, 300, 420 | 120 |
9 | No load | X-Y axis linkage | 60, 180, 300, 420 | 60 |
10 | No load | X-Y axis linkage | 60, 180, 300, 420 | 80 |
11 | No load | X-Y axis linkage | 60, 180, 300, 420 | 100 |
12 | No load | X-Y axis linkage | 60, 180, 300, 420 | 120 |
13 | Load 10 kg | X axis | 60, 180, 300, 420 | 60 |
14 | Load 10 kg | X axis | 60, 180, 300, 420 | 80 |
15 | Load 10 kg | X axis | 60, 180, 300, 420 | 100 |
16 | Load 10 kg | X axis | 60, 180, 300, 420 | 120 |
17 | Load 10 kg | Y axis | 60, 180, 300, 420 | 60 |
18 | Load 10 kg | Y axis | 60, 180, 300, 420 | 80 |
19 | Load 10 kg | Y axis | 60, 180, 300, 420 | 100 |
20 | Load 10 kg | Y axis | 60, 180, 300, 420 | 120 |
21 | Load 10 kg | X-Y axis linkage | 60, 180, 300, 420 | 60 |
22 | Load 10 kg | X-Y axis linkage | 60, 180, 300, 420 | 80 |
23 | Load 10 kg | X-Y axis linkage | 60, 180, 300, 420 | 100 |
24 | Load 10 kg | X-Y axis linkage | 60, 180, 300, 420 | 120 |
Parameters | Learning Rate | ||||
---|---|---|---|---|---|
Value | 1 | 1 | 0.5 | 0.8 | 0.0001 |
Case | AUROC | AUPRC |
---|---|---|
Case 1 | 0.946 | 0.948 |
Case 2 | 0.985 | 0.982 |
Case 3 | 0.977 | 0.969 |
Case | Precision | Recall | F1 |
---|---|---|---|
Case 1 | 0.921 | 0.953 | 0.937 |
Case 2 | 0.965 | 1.000 | 0.982 |
Case 3 | 0.943 | 0.984 | 0.963 |
AUROC | Case1 | Case2 | Case3 |
---|---|---|---|
GAN-AE | 0.728 | 0.832 | 0.822 |
GANomaly | 0.873 | 0.881 | 0.875 |
Our method | 0.946 | 0.985 | 0.977 |
Case | Average Accuracy | Detection Time (ms) |
---|---|---|
GAN-AE | 0.825 | 418 |
GANomaly | 0.864 | 357 |
Our method | 0.980 | 134 |
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Yang, Z.; Zhang, W.; Cui, W.; Gao, L.; Chen, Y.; Wei, Q.; Liu, L. Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks. Energies 2022, 15, 5671. https://0-doi-org.brum.beds.ac.uk/10.3390/en15155671
Yang Z, Zhang W, Cui W, Gao L, Chen Y, Wei Q, Liu L. Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks. Energies. 2022; 15(15):5671. https://0-doi-org.brum.beds.ac.uk/10.3390/en15155671
Chicago/Turabian StyleYang, Zeqing, Wenbo Zhang, Wei Cui, Lingxiao Gao, Yingshu Chen, Qiang Wei, and Libing Liu. 2022. "Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks" Energies 15, no. 15: 5671. https://0-doi-org.brum.beds.ac.uk/10.3390/en15155671