Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification
Abstract
:1. Introduction
2. Transfer Learning
2.1. Definitions
- Domain
- Task
- Transfer Learning
2.2. Problem Categorization
2.3. Solution Categorization
2.3.1. Instance-Based
2.3.2. Feature-Based
2.3.3. Parameter-Based
2.3.4. Relational-Based
3. Predictive Maintenance
Transfer Learning in Predictive Maintenance
4. Experimental Design
4.1. Datasets
4.2. Models and Transfer Learning Approaches
4.3. Analysis
5. Baseline Approach
5.1. Preprocessing
- Total Useful Lifetime
- Pro Rata Useful Lifetime
5.2. Feature Engineering
5.3. ML Model
5.4. Transfer Learning
6. Black Box Adversarial Reprogramming
6.1. Preprocessing
6.2. ML Model
6.3. Adversarial Reprogramming Algorithm
6.3.1. Functional Structure
- Translation Input
- Prediction of the Black Box Model
- Translation Output
Listing 1. Three functions comprise the functional structure of the BAR algorithm. |
6.3.2. Training Process
- Initialization
- Sample Dataset
- One-Sided Averaged Gradient Estimator
- Loss Function
- Update W
- Select best W
6.3.3. Hyperparameters
7. Results
7.1. Baseline
7.1.1. Performance Source Domain
7.1.2. Performance Target Domain
7.2. Black Box Adversarial Reprogramming
7.2.1. Performance Black Box Model
7.2.2. Hyperparameters
- Initial coarse grid search: Each configuration is computed three times.
- Subsequent finer grid search: Each configuration is computed seven times.
- Final fine grid search: Each configuration undergoes eleven training sessions.
7.2.3. Performance Black Box Adversarial Reprogramming
8. Discussion
8.1. Interpretation
8.2. Hyperparameter Influence
- Learning Rate
- Weighting Penalty Term
- Number Random Vectors
- Size of Vectors
9. Limitations and Future Research Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Use Case | Focus | ML Method | Deep Learning | Year | Source |
---|---|---|---|---|---|
Meta Studies | Generalistic | ■ | 2019 | [40] | |
Cross-Domain Fault Diagnosis | ■ | 2019 | [42] | ||
Industry 4.0 | ■ | 2023 | [9] | ||
Prediction | Fault Datasets | CNN, LSTM | ■ | 2021 | [47] |
Feature Distribution | LSTM, DANN | ■ | 2020 | [46] | |
Feature Distribution | CDAE, SVM | ■ | 2019 | [43] | |
Feature Distribution | LSTM, CNN | ■ | 2019 | [66] | |
Feature Distribution and FOT | HMM, MLP | ■ | 2020 | [45] | |
Multi Feature Fusion | ConvNeXt | ■ | 2022 | [48] | |
Sample Selection | CNN | ■ | 2023 | [50] | |
Feature Distribution | HCA, SVM | □ | 2021 | [59] | |
Low-Quality Features | SVM | □ | 2021 | [60] | |
Diagnosis | Feature Distribution and Extraction | SAE, DNN | ■ | 2019 | [44] |
Simulation to Real World | SSAE, DNN | ■ | 2019 | [52] | |
Training | NN | ■ | 2019 | [53] | |
Training | CNN | ■ | 2019 | [54] | |
Detection | Resource Allocation | NN | ■ | 2022 | [51] |
Robot Tasks | - | □ | 2021 | [58] | |
Detection and Diagnosis | Real Time Requirements | CNN | ■ | 2020 | [49] |
1 | 2 | 3 | |
---|---|---|---|
interval of class i | (0, 50] | (50, 75] | (75, 100] |
RUL interval of class i | [100, 50) | [50, 25) | [25, 0) |
1 | 2 | 3 | 4 | |
---|---|---|---|---|
interval of class i | (0, 50] | (50, 70] | (70, 90] | (90, 100] |
RUL interval of class i | [100, 50) | [50, 30] | [30, 10] | [10, 0] |
Learning Rate | Number Resamplings | Weighting Penalty Term | Number Random Vectors | Iterations per Sampling | Size of Vectors q |
---|---|---|---|---|---|
n_splits | q | epochs | vector_size |
Train Data | Test Data | |||||||
---|---|---|---|---|---|---|---|---|
Precision | Recall | f1 score | Support | Precision | Recall | f1 score | Support | |
Class 1 | 1.00 | 0.95 | 0.97 | 113 | 0.85 | 1.00 | 0.92 | 45 |
Class 2 | 0.84 | 0.97 | 0.90 | 32 | 0.46 | 0.43 | 0.44 | 14 |
Class 3 | 0.93 | 1.00 | 0.97 | 14 | 1.00 | 0.30 | 0.46 | 10 |
accuracy | 0.96 | 159 | 0.78 | 69 | ||||
macro avg | 0.92 | 0.97 | 0.95 | 159 | 0.77 | 0.58 | 0.61 | 69 |
weighted avg | 0.96 | 0.96 | 0.96 | 159 | 0.79 | 0.78 | 0.76 | 69 |
Train Data | Test Data | |||||||
---|---|---|---|---|---|---|---|---|
Precision | Recall | f1 score | Support | Precision | Recall | f1 score | Support | |
Class 1 | 0.80 | 1.00 | 0.89 | 4 | 0.33 | 1.00 | 0.50 | 1 |
Class 2 | 0.00 | 0.00 | 0.00 | 3 | 0.00 | 0.00 | 0.00 | 1 |
Class 3 | 0.88 | 1.00 | 0.93 | 14 | 1.00 | 0.50 | 0.67 | 2 |
accuracy | 0.86 | 21 | 0.50 | 4 | ||||
macro avg | 0.56 | 0.67 | 0.61 | 21 | 0.44 | 0.50 | 0.39 | 4 |
weighted avg | 0.74 | 0.86 | 0.79 | 21 | 0.58 | 0.50 | 0.46 | 4 |
Hyperparameter | Search Space | Hyperparameter | Search Space |
---|---|---|---|
0.6 | q | 50 | |
n splits | 5 | epochs | 120 |
7 | vector size | 135 |
Train Data | Test Data | |||||||
---|---|---|---|---|---|---|---|---|
Precision | Recall | f1 score | Support | Precision | Recall | f1 score | Support | |
Class 1 | 0.93 | 0.94 | 0.93 | 82 | 0.81 | 0.94 | 0.87 | 18 |
Class 2 | 0.76 | 0.90 | 0.83 | 63 | 0.86 | 0.75 | 0.80 | 24 |
Class 3 | 0.77 | 0.84 | 0.80 | 97 | 0.53 | 0.85 | 0.65 | 20 |
Class 4 | 0.96 | 0.76 | 0.85 | 101 | 0.98 | 0.75 | 0.85 | 53 |
accuracy | 0.86 | 343 | 0.79 | 115 | ||||
macro avg | 0.86 | 0.86 | 0.85 | 343 | 0.79 | 0.82 | 0.79 | 115 |
weighted avg | 0.86 | 0.85 | 0.85 | 343 | 0.85 | 0.80 | 0.81 | 115 |
Train Data | Test Data | |||||||
---|---|---|---|---|---|---|---|---|
Precision | Recall | f1 score | Support | Precision | Recall | f1 score | Support | |
1 | 0.40 | 0.50 | 0.44 | 4 | 0.00 | 0.00 | 0.00 | 1 |
2 | 1.00 | 0.67 | 0.80 | 3 | 0.00 | 0.00 | 0.00 | 1 |
3 | 0.86 | 0.86 | 0.86 | 14 | 0.67 | 1.00 | 0.80 | 2 |
accuracy | 0.76 | 21 | 0.50 | 4 | ||||
macro avg | 0.75 | 0.67 | 0.70 | 21 | 0.22 | 0.33 | 0.27 | 4 |
weighted avg | 0.79 | 0.76 | 0.77 | 21 | 0.33 | 0.50 | 0.40 | 4 |
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Bott, A.; Schreyer, F.; Puchta, A.; Fleischer, J. Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification. Mach. Learn. Knowl. Extr. 2024, 6, 1969-1996. https://doi.org/10.3390/make6030097
Bott A, Schreyer F, Puchta A, Fleischer J. Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification. Machine Learning and Knowledge Extraction. 2024; 6(3):1969-1996. https://doi.org/10.3390/make6030097
Chicago/Turabian StyleBott, Alexander, Felix Schreyer, Alexander Puchta, and Jürgen Fleischer. 2024. "Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification" Machine Learning and Knowledge Extraction 6, no. 3: 1969-1996. https://doi.org/10.3390/make6030097