ML Models for PdM
Survival analysis, anomaly detection, RUL estimation, and deep learning
Types of PdM Models
Types of PdM Models
Different ML approaches address different PdM questions:
| Approach | Question Answered | Methods |
|---|---|---|
| Anomaly Detection | Is the tool behaving abnormally right now? | Isolation Forest, Autoencoders, PCA, One-Class SVM |
| Classification | Will this component fail within N hours? | Random Forest, XGBoost, Neural Networks |
| RUL Estimation | How many hours until failure? | LSTM, CNN on time series, survival models |
| Survival Analysis | What's the probability of survival past time T? | Cox PH, Weibull, Random Survival Forests |
Key Concept: The Rare Failure Problem
In a well-maintained fab, actual failures are rare (class imbalance: 99.9%+ normal). This creates challenges for supervised learning. Approaches: anomaly detection (unsupervised), synthetic oversampling (SMOTE), cost-sensitive learning, or semi-supervised methods that learn "normal" and flag deviations.
Deep Learning for PdM
Deep Learning for PdM
Deep learning has shown promise for PdM, particularly for directly modeling raw sensor time series:
- 1D-CNNs: Convolutional networks applied to sensor time series can automatically learn relevant temporal patterns without manual feature engineering.
- LSTMs/GRUs: Recurrent networks capture long-range dependencies across multiple process runs (e.g., slow drift over hundreds of runs).
- Transformer-based models: Attention mechanisms can identify which time steps and which sensors are most predictive of impending failure.
- Autoencoders: Learn a compressed representation of "normal" equipment behavior. Large reconstruction error = abnormal behavior.
In practice, gradient-boosted trees (XGBoost, LightGBM) on engineered features often outperform deep learning in this domain due to limited training data and the effectiveness of domain-informed features.
Knowledge Check
Knowledge Check
1 / 1Why is anomaly detection often preferred over supervised classification for PdM?