Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network
<p>DPCA-VGG16 state recognition model.</p> "> Figure 2
<p>VGG16 Structure.</p> "> Figure 3
<p>Experimental system.</p> "> Figure 4
<p>Signal demodulation diagram. (<b>a</b>) Input frequency is 5 Hz; (<b>b</b>) Input frequency is 10 Hz; (<b>c</b>) Input frequency is 15 Hz; (<b>d</b>) Input frequency is 20 Hz; (<b>e</b>) Input frequency is 25 Hz; (<b>f</b>) Input frequency is 30 Hz.</p> "> Figure 4 Cont.
<p>Signal demodulation diagram. (<b>a</b>) Input frequency is 5 Hz; (<b>b</b>) Input frequency is 10 Hz; (<b>c</b>) Input frequency is 15 Hz; (<b>d</b>) Input frequency is 20 Hz; (<b>e</b>) Input frequency is 25 Hz; (<b>f</b>) Input frequency is 30 Hz.</p> "> Figure 5
<p>Accuracy and Loss Curve. (<b>a</b>) Vertical diameter direction; (<b>b</b>) Horizontal radial; (<b>c</b>) Axial direction.</p> "> Figure 5 Cont.
<p>Accuracy and Loss Curve. (<b>a</b>) Vertical diameter direction; (<b>b</b>) Horizontal radial; (<b>c</b>) Axial direction.</p> "> Figure 6
<p>Confusion matrix of operation status identification results. (<b>a</b>) Vertical diameter direction; (<b>b</b>) Horizontal radial; (<b>c</b>) Axial direction.</p> "> Figure 7
<p>PCA dimensionality reduction diagram of operating state recognition results. (<b>a</b>) Vertical diameter direction; (<b>b</b>) Horizontal radial; (<b>c</b>) Axial direction.</p> "> Figure 8
<p>Confusion matrix identified by the time domain diagram. (<b>a</b>) Vertical diameter direction; (<b>b</b>) Horizontal radial; (<b>c</b>) Axial direction.</p> "> Figure 9
<p>Resnet Network Accuracy and Loss Curve.</p> "> Figure 10
<p>Alexnet Network Accuracy and Loss Curve.</p> ">
Abstract
:1. Introduction
2. DPCA-VGG16 State Recognition Model
2.1. DPCA Algorithm
2.2. VGG16 Structure
3. Design of the Experimental Platform
4. Experiment and Analysis
4.1. Dataset Feature Extraction
- (1)
- Divide the vibration signal data into equal-length segments (200 segments) over time.
- (2)
- Perform DPCA demodulation on each segment, converting the time-domain diagram into a demodulation frequency domain diagram.
- (3)
- Group all the demodulated images into 6 categories based on different input frequencies, with each group containing 200 image samples.
- (4)
- Randomly select 160 image samples as the training set and 40 image samples as the test set, ensuring that the same algorithm model performs consistently on different sets by shuffling the data before loading it.
- (5)
- After applying the VGG16 model, continue fitting the data, drawing accuracy and loss curves.
4.2. Result Analysis
4.3. Contrast Test
5. Conclusions
- (1)
- The DPCA-VGG16 traction machine state recognition model was proposed with a recognition accuracy of 96.94%. It is better to directly import the time-domain graph into the convolutional network model. The comprehensive recognition accuracy, training duration, and feasibility are superior to recognition models such as AlexNet, GoogleNet, and ResNet.
- (2)
- According to the given information, it can be inferred that the signal characteristics of traction machine vibrations are most prominent in the vertical diameter direction, resulting in stronger recognition performance. The axial direction exhibits relatively clear signal characteristics as well, while the horizontal and radial directions have weaker signal characteristics.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
Weight/kg | 381 |
Traction ratio | 2:1 |
Number of poles/P | 32 |
Rated power/kW | 13.4 |
Rated load/kg | 1000 |
Rated frequency/Hz | 51 |
Rated speed r/min | 191 |
Rated voltage/V | 340 |
Rated current/A | 30.2 |
Efficiency/% | 85.1 |
Test Direction | Vertical Diameter Direction (a) | Horizontal Radial (b) | Axial Direction (c) |
---|---|---|---|
Model | 352C33 | 352C33 | 352C33 |
Sensitivity | 10.23 | 10.38 | 9.94 |
Number of Training Sessions in Data Direction (Times) | Vertical Diameter Direction | Horizontal Radial | Axial Direction | |||
---|---|---|---|---|---|---|
Accuracy (%) | Loss | Accuracy (%) | Loss | Accuracy (%) | Loss | |
3 | 39.69 | 1.6599 | 52.73 | 1.5050 | 29.17 | 1.7333 |
5 | 79.37 | 0.9291 | 75.91 | 0.7251 | 71.35 | 1.0825 |
10 | 96.77 | 0.1274 | 95.44 | 0.1595 | 97.14 | 0.1187 |
20 | 100 | 0.0051 | 100 | 0.0166 | 99.74 | 0.0142 |
Data Direction Input Frequency (Hz) | Vertical Diameter Direction | Horizontal Radial | Axial Direction | |||
---|---|---|---|---|---|---|
DPCA-VGG16 | Time Domain vgg16 | DPCA-VGG16 | Time Domain vgg16 | DPCA-VGG16 | Time Domain vgg16 | |
5 | 100 | 100 | 100 | 100 | 97.5 | 94.29 |
10 | 100 | 81.25 | 97.56 | 61.76 | 88.89 | 100 |
15 | 100 | 97.5 | 83.78 | 66.67 | 97.56 | 97.44 |
20 | 100 | 80.65 | 100 | 55.10 | 100 | 95.24 |
25 | 100 | 75.56 | 82.22 | 62.86 | 100 | 78.26 |
30 | 100 | 55.77 | 97.37 | 92.86 | 97.44 | 71.79 |
Data Direction Input Frequency (Hz) | Vertical Diameter Direction | Horizontal Radial | Axial Direction | |||
---|---|---|---|---|---|---|
DPCA-VGG16 | Time Domain vgg16 | DPCA-VGG16 | Time Domain vgg16 | DPCA-VGG16 | Time Domain vgg16 | |
5 | 100 | 100 | 100 | 100 | 100 | 82.5 |
10 | 100 | 65 | 100 | 52.5 | 100 | 97.5 |
15 | 100 | 97.5 | 77.5 | 65 | 100 | 95 |
20 | 100 | 62.5 | 97.5 | 67.5 | 87.5 | 100 |
25 | 100 | 85 | 92.5 | 55 | 100 | 90 |
30 | 100 | 72.5 | 92.5 | 97.5 | 95 | 70 |
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Li, D.; Yang, J.; Pan, Z.; Li, N. Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network. Sensors 2023, 23, 6646. https://doi.org/10.3390/s23146646
Li D, Yang J, Pan Z, Li N. Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network. Sensors. 2023; 23(14):6646. https://doi.org/10.3390/s23146646
Chicago/Turabian StyleLi, Dongyang, Jianyi Yang, Zaisheng Pan, and Nanyang Li. 2023. "Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network" Sensors 23, no. 14: 6646. https://doi.org/10.3390/s23146646
APA StyleLi, D., Yang, J., Pan, Z., & Li, N. (2023). Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network. Sensors, 23(14), 6646. https://doi.org/10.3390/s23146646