A derivative, integral, and proportional features extractor for fault detection in dynamic processes

Abstract

Detecting faults in dynamic systems is challenging due to temporal dependencies and signal correlations. Feature extraction from time-series data is a common step in fault detection, which is usually performed according two main approaches: knowledge-based and statistical. Knowledge-based methods provide interpretable features but require significant design time; also, they usually lack generality. Statistical methods are faster but lead to dimensionality issues and lack of interpretability. To address these challenges, we propose an interpretable and automated feature extraction method. It combines the benefits of knowledge-based and statistical methods, offering a fast solution for extracting effective and interpretable indicators without requiring prior domain knowledge. Also, this method consistently computes a fixed set of interpretable features describing the process’s dynamic behavior.We extensively compare our approach with state-of-the-art methods considering nine open-source datasets spanning various domains. Our results show that classifiers trained on features extracted with the proposed method achieve performance comparable to those provided by state-of-the-art automatic features extractors, according to F1-score, true positive rate, and false positive rate. In addition, our approach proves to be more robust to dimensionality issues and enhances interpretability, extracting a reduced set of features effective at providing insight into the detected anomalies’ characteristics. Additionally, we demonstrate that the selected features maintain consistent performance across different classifiers, showcasing their versatility.