Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring
Abstract
:1. Introduction
2. Methodology of Input Paradigm and Time Series Presentation
2.1. Problem Description
2.2. Design of Different Input Paradigms
2.2.1. Downsampling Paradigm
2.2.2. Periodic Paradigm
2.2.3. Subsequence Paradigm
2.3. PHM2010 Dataset Description
2.4. Display of Time Series Generated by Different Input Paradigms
3. TCM Method Based on an Improved CNN-BiLSTM Hybrid Model
3.1. Model Architecture
3.1.1. CNN
3.1.2. BiLSTM
3.2. Model Training and Testing
3.2.1. Data Preprocessing
3.2.2. Implementation Details
4. Results and Discussion
4.1. Exploration of Different Input Paradigms
4.1.1. Model Performance of Different Input Paradigms
4.1.2. Dimensionality Reduction Potential of Different Input Paradigms
- Downsampling paradigm: Set intervals Nd to 1000, 200, 100, and 20, respectively.
- Periodic paradigm: Set the number of cycles Np to 0.1, 0.5, 1, and 5, respectively.
- Subsequence paradigm: Set the number of subsequences Ns to 30, 150, 300, and 1500, respectively.
4.2. Further Exploration of the Subsequence Paradigm
4.2.1. The Impact of Different Numbers of Subsequences on Model Performance
- (1)
- Display of Newly Generated Time Series
- (2)
- Predictive Performance
4.2.2. Impact of Different Temporal Indicators on Model Performance
- (1)
- Display of Newly Generated Time Series
- (2)
- Predictive Performance
4.3. Comparison with Other Methods
5. Conclusions and Future Works
- (1)
- A new end-to-end framework for tool wear prediction was designed. Firstly, a suitable input paradigm was selected to generate new time series data directly into the model, eliminating the need for complex manual feature extraction. Then an improved CNN-BiLSTM hybrid model was utilized for prediction, capable of capturing the complex spatiotemporal correlation between the multi-sensor data and tool wear.
- (2)
- The subsequence paradigm had the lowest overall MAE and RMSE prediction performance metrics and the shortest computation time compared to the downsampling paradigm and the periodic paradigm. This shows that the subsequence paradigm is a great way to make TCM more effective and faster.
- (3)
- Further in-depth exploration of the subsequence paradigm revealed that the model’s MAE and RMSE were lowest when there were 120 subsequences and the temporal indicator was set to its highest value. This was after threefold cross-validation.
- (4)
- Finally, we demonstrated the superiority of the proposed method by ditching feature engineering, overcoming the limitations of a single model architecture, and constructing high-quality input data by comparing the prediction performance of several classical and contemporary methods using the same dataset.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Milling Parameters | Value | Equipment Type | Equipment Model |
---|---|---|---|
Milling method | Climb milling | Machine | Roders RFM760 (Roders, Soltau, Germany) |
Cooling method | Dry cutting | Tool | Ball-end carbide milling cutter (Roders, Soltau, Germany) |
Spindle speed (r/min) | 10,400 | Workpiece material | Stainless steel HRC52 |
Feed rate (mm/min) | 1555 | Vibration sensor | Kistler 8636C acceleration sensor (Kistler, Winterthur, Swiss) |
Tool feeding amount (mm) | 0.001 | AE sensor | Kistler 8152 acoustic emission sensor (Kistler, Winterthur, Swiss) |
Cutting width (mm) | 0.125 | Force sensor | Kistler 9265B dynamometer (Kistler, Winterthur, Swiss) |
Cutting depth (mm) | 0.2 | Data acquisition card | DAQ NI PCI 1200 (National Instruments, Austin, TX, USA) |
Cutting length (mm) | 108 | Charge amplifier | Kistler charge amplifier (Kistler, Winterthur, Swiss) |
Sampling frequency (kHz) | 50 | Microscope | LEICA MZ12 microscope (Leica, Solms, Germany) |
Parameter | Learning Rate | Batch Size | Dropout Rate | Epoch | Optimizer | Activation |
---|---|---|---|---|---|---|
Value | 0.001 | 128 | 0.2 | 500 (early stopping) | Adam | ReLU |
Input Paradigms | Test Data | Computational Time (s) | |||||
---|---|---|---|---|---|---|---|
C1 | C4 | C6 | |||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | ||
downsampling-100 | 7.49 | 9.99 | 10.66 | 14.56 | 9.26 | 11.96 | 93.26 |
periodic-1 | 9.03 | 11.87 | 11.30 | 16.53 | 7.76 | 10.33 | 108.12 |
subsequence-300 | 7.10 | 8.92 | 8.92 | 11.39 | 10.88 | 13.78 | 76.89 |
Input Paradigms | Input Sequence Length | Test Set | |||||
---|---|---|---|---|---|---|---|
C1 | C4 | C6 | |||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | ||
Downsampling paradigm | 30 | 9.18 | 11.87 | 11.33 | 16.44 | 7.00 | 9.25 |
150 | 6.73 | 8.95 | 10.59 | 15.92 | 10.57 | 13.46 | |
300 | 7.49 | 9.99 | 10.66 | 14.56 | 9.26 | 11.96 | |
1500 | 8.40 | 10.59 | 9.81 | 14.85 | 9.02 | 11.81 | |
Periodic paradigm | 30 | 9.94 | 13.89 | 13.54 | 20.17 | 12.24 | 16.76 |
150 | 9.50 | 12.45 | 11.33 | 16.67 | 8.72 | 12.05 | |
300 | 9.03 | 11.87 | 11.30 | 16.53 | 7.76 | 10.33 | |
1500 | 9.99 | 13.40 | 11.42 | 16.54 | 11.08 | 14.27 | |
Subsequence paradigm | 30 | 5.87 | 7.43 | 10.35 | 12.50 | 11.16 | 14.42 |
150 | 5.64 | 7.06 | 9.89 | 12.57 | 9.20 | 11.48 | |
300 | 7.10 | 8.92 | 8.92 | 11.39 | 10.88 | 13.78 | |
1500 | 6.86 | 8.64 | 8.29 | 12.20 | 9.66 | 12.79 |
Subsequence Number | Test Set | |||||
---|---|---|---|---|---|---|
C1 | C4 | C6 | ||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | |
30 | 5.87 | 7.43 | 10.35 | 12.50 | 11.15 | 14.42 |
60 | 5.61 | 6.89 | 7.60 | 9.83 | 10.59 | 14.17 |
90 | 5.52 | 7.40 | 8.10 | 11.94 | 10.35 | 13.29 |
120 | 5.53 | 6.93 | 7.70 | 10.10 | 8.66 | 11.84 |
150 | 5.64 | 7.06 | 9.89 | 12.57 | 9.20 | 11.48 |
180 | 5.83 | 7.25 | 9.54 | 12.58 | 9.77 | 13.06 |
210 | 6.35 | 8.01 | 7.54 | 10.28 | 12.22 | 16.39 |
240 | 5.90 | 7.40 | 8.71 | 11.44 | 10.31 | 13.83 |
270 | 7.00 | 8.67 | 8.77 | 12.32 | 11.64 | 15.63 |
300 | 7.10 | 8.92 | 8.92 | 11.39 | 10.88 | 13.78 |
Temporal Indicator | Test Set | Computational Time (s) | |||||
---|---|---|---|---|---|---|---|
C1 | C4 | C6 | |||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | ||
Maximum Values | 5.53 | 6.93 | 7.70 | 10.10 | 8.66 | 11.84 | 43.00 |
Minimum Values | 11.52 | 14.94 | 8.86 | 11.54 | 12.07 | 16.81 | 44.99 |
Mean Values | 18.56 | 25.22 | 14.30 | 22.18 | 7.44 | 10.51 | 54.17 |
Method | Feature Engineering | Test Data | |||||
---|---|---|---|---|---|---|---|
C1 | C4 | C6 | |||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | ||
LR [49] | Yes | 24.4 | 31.1 | 16.3 | 19.3 | 24.4 | 30.9 |
SVR [68] | Yes | 10.1 | 13.12 | 13.69 | 15.4 | 12.42 | 14.74 |
SSAE-SVR [68] | Yes | 8.6 | 11.89 | 11.52 | 14.75 | 8.25 | 10.95 |
CNN [69] | Yes | 9.31 | 12.19 | 11.29 | 14.59 | 34.69 | 40.48 |
LSTM [54] | No | 22.3 | 22.7 | 18.7 | 19.4 | 27.1 | 29.2 |
BiLSTM [54] | No | 12.8 | 14.6 | 10.9 | 14.2 | 14.7 | 17.7 |
CNN-LSTM [70] | No | 11.18 | 13.77 | 9.39 | 11.85 | 11.34 | 14.33 |
CABLSTM [55] | No | 7.47 | 8.17 | \ | \ | \ | \ |
Proposed method | No | 5.53 | 6.93 | 7.70 | 10.10 | 8.66 | 11.84 |
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Wang, C.; Wang, G.; Wang, T.; Xiong, X.; Ouyang, Z.; Gong, T. Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring. Sensors 2024, 24, 5300. https://doi.org/10.3390/s24165300
Wang C, Wang G, Wang T, Xiong X, Ouyang Z, Gong T. Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring. Sensors. 2024; 24(16):5300. https://doi.org/10.3390/s24165300
Chicago/Turabian StyleWang, Chengguan, Guangping Wang, Tao Wang, Xiyao Xiong, Zhongchuan Ouyang, and Tao Gong. 2024. "Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring" Sensors 24, no. 16: 5300. https://doi.org/10.3390/s24165300