Time-Series Machine Learning for Fault Diagnosis and Severity Estimation in Industrial Processes

Industrial fault detection and diagnosis are essential for maintaining operational reliability and minimizing performance degradation in process industries. This study presents an integrated machine learning framework for fault detection, fault-type classification, and fault severity estimation using a synthetic chemical-process time-series dataset comprising six reactors operating under multiple conditions. The framework combines a five-class fault diagnosis model with a gated severity estimation stage that is activated only when a fault is detected, enabling simultaneous assessment of process condition and operational impact. Eight process variables were selected through statistical and process-oriented analysis, while one-minute difference features and reactor identity information were incorporated to capture short-term process dynamics and equipment-specific operating characteristics. The framework was evaluated using an episode-aware methodology incorporating fault-episode partitioning, leakage-prevention measures, grouped cross-validation, and episode-level analysis. The selected classification model achieved a balanced accuracy of 76.25% and a macro F1-score of 82.44%. For severity estimation, the complete end-to-end pipeline achieved R2 = 0.153 across active-fault observations, illustrating the impact of fault detection errors on downstream severity assessment. When evaluated across all observations, including predominantly normal conditions, the corresponding R2 increased to 0.871. Under an oracle scenario using the true fault type, severity estimation achieved R2 = 0.920. The results provide fault-specific and episode-level insights and support a proof of concept within this synthetic industrial process environment.