Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion
Abstract
:Featured Application
Abstract
1. Introduction
2. Related Work
2.1. Fault Diagnosis Methods
2.2. Multi-Source Data Fusion Methods
2.3. Discussion
3. Fault Diagnosis Method Based on LSTM and Multi-Sensor Data Fusion
3.1. The Network Architecture of LSTM Based on Multi-Sensor Data Fusion
- Forget stage. This phase is mainly about selectively forgetting the input passed in by the previous node. That is, the network determines whether the intermediate information should be forgotten or remembered. Specifically, it controls information flow from the previous state, which needs to be retained or forgotten, by calculating the value of the forgetting gate.
- Memory stage. This stage is to decide what new information should be kept in the cell state. A sigmoid layer as the input gate layer decides which values should be updated. A tanh layer creates a vector of new candidate values, then combines above values to create an update to the state;
- Output stage. This is the final stage to decide which information is outputted. This output of the proposed CNN-LSTM contains prediction of each sensor deployed on equipment. Then, the network output is fed into the k-NN algorithm to obtain the final prediction.
3.2. Dilated Convolution Module
3.3. Attention Mechanism
4. Experiments
4.1. Data Preprocessing
4.2. Experiments and Evaluation Method
5. Application
6. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number | Device | Sampling Rate | Node Count | Range |
---|---|---|---|---|
1 | Voltage sensors | 600 Hz | 3 | 0–450 V |
2 | Current sensors | 450 Hz | 2 | 0–300 A |
3 | Vibration | 500 Hz | 2 | 0–100 mm/s |
4 | Rotating | 2 kHz | 1 | 0–20 kHz |
5 | Pressure | 2400 Hz | 2 | 0–100 MPa |
Length of Each Sample | Proportion | Number of Samples | |
---|---|---|---|
Train | 0.25 s | 70% | 3225.6 k |
Validation | 20% | 921.6 k | |
Test | 10% | 460.8 k | |
All | - | - | 4608 k |
Device | Running State | Proportion |
---|---|---|
All devices | Normal | 68.7% |
Air compressor | Abnormal (pressure) | 1.8% |
Cutting equipment | Abnormal (voltage and current) | 8.4% |
Mixing equipment | Abnormal (rotation) | 9.3% |
Stirring kettle | Abnormal or breakdown (vibration) | 11.8% |
Algorithm | Accuracy | Precision | Recall | F1-Score | Time Consumption (s) |
---|---|---|---|---|---|
Proposed | 98.97% | 98.95% | 99.47% | 99.21% | 0.437 |
RNN | 71.58% | 81.29% | 73.16% | 77.01% | 0.583 |
LSTM | 83.56% | 88.48% | 86.67% | 87.56% | 0.519 |
SVM | 69.21% | 72.77% | 77.22% | 74.93% | 1.748 |
1D-CNN | 78.07% | 85.12% | 81.38% | 83.21% | 0.364 |
Sensor | Input Voltage | Output | Resolution | Range |
---|---|---|---|---|
Voltage | - | RS485 | 600 | 0–450 V |
Current | - | 450 | 0–300 A | |
Vibration | 12 or 24 V | 4–20 mA | 0.1 mm | 0–100 mm/s |
Rotation | 24 V | 0–10 V | 0.3% | 20–10,000 Hz |
Pressure | 12–36 V | 4–20 mA | 0.06 MPa | 0.1–60 MPa |
No. | Device Name | Type |
---|---|---|
1 | CPU | I9-10900kf |
2 | RAM | 64G DDR4 |
3 | Hard driver | 512G SSD |
4 | GPU | RTX 2080s × 2 |
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Wang, Y.; Guo, X.; Liu, X.; Liu, X. Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion. Appl. Sci. 2022, 12, 9642. https://doi.org/10.3390/app12199642
Wang Y, Guo X, Liu X, Liu X. Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion. Applied Sciences. 2022; 12(19):9642. https://doi.org/10.3390/app12199642
Chicago/Turabian StyleWang, Yong, Xiaoqiang Guo, Xinhua Liu, and Xiaowen Liu. 2022. "Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion" Applied Sciences 12, no. 19: 9642. https://doi.org/10.3390/app12199642
APA StyleWang, Y., Guo, X., Liu, X., & Liu, X. (2022). Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion. Applied Sciences, 12(19), 9642. https://doi.org/10.3390/app12199642