Feature Extraction With Genetic Programming for Root Cause Identification in Manufacturing With Interpretable Machine Learning

For fault detection (FD) in manufacturing, various machine learning (ML) models have been widely applied to minimise human intervention and improve detection performance. Even though ML models such as neural networks (NN) have been shown to identify faults effectively, root cause identification (RCI) is becoming more difficult due to their black-box structures and the trade-off between accuracy and interpretability. In order to improve performance while maintaining interpretability, we propose a new framework named FERMAT (Feature Extraction for finding Root causes for Manufacturing Applications with Tree-based algorithms), which enhances the performance of height-limited decision trees (C4.5) through dimensionally-aware genetic programming for feature extraction. Especially in FERMAT, only interpretable features are extracted to prevent decision trees from delivering uninterpretable expressions to practitioners. In the present study, FERMAT’s applicability to RCI was verified with both manufacturing and non-manufacturing datasets with different imbalance ratios. The experimental results showed that FERMAT outperformed the other single-tree-based models by extracting good features and delivered performance comparable to the black-box models.