Machine Learning-Based Condition Monitoring with Novel Event Detection and Incremental Learning for Industrial Faults and Cyberattacks

This study presents an integrated condition-monitoring approach for industrial processes. The proposed approach conveniently combines a computational intelligence-based mechanism to guarantee the resilience of the proposed scheme against unknown anomalies and a machine learning model with optimized parameters capable of unified detection and pinpointing of faults and cyberattacks in industrial plants. During the offline phase, process data are labeled, normalized, and used to train the machine learning model with hyperparameter tuned by using an optimization tool. In the online phase, the system performs real-time monitoring enhanced with a novelty mechanism to detect anomalous conditions not present in the training data, which are flagged for expert analysis and incorporated into the system through incremental learning. The implementation of the proposed strategy uses computational intelligence tools consisting of a multilayer perceptron neural network, local outlier factor, and differential evolution. The proposed framework was validated using the two-tank process benchmark, demonstrating superior detection accuracy of 99% and robustness compared to other machine learning algorithms. These results highlight the potential of combining fault diagnosis and cybersecurity in a unified architecture, thereby contributing to resilient and intelligent systems in the context of Industry 4.0/5.0.