Intelligent Early Fault Management Using a Continual Deep Learning Information System for Industrial Operations

Purpose- This study explores the interaction between operations management and information systems by applying the Design Science Research (DSR) methodology for intelligent early fault management. Prior research primarily addressed post-fault identification and classification but has struggled with catastrophic forgetting. Thus this work proposes an innovative data-driven artifact that leverages a deep learning (DL)-based approach for earl fault detection and future fault forecasting.

Design/methodology/approach- Following the DSR methodology, the work proposes an innovative data-driven artifact for early fault management. The proposed artifact extracts key features from industrial sensor data in real time using a deep sparse autoencoder with a sparsity penalty. These features are then processed using an exponentially weighted moving average method for monitoring process variations, while a Transformer-based neural network forecasts potential faults. To mitigate catastrophic forgetting, the elastic weight consolidation technique is applied during offline training to preserve previous patterns when new information becomes available.

Findings- The artifact enhances operational decision-making by generating early warning alerts and delivering actionable insights. Experimental evaluation using real-world sensor data validates that the proposed approach outperforms existing DL methods.

Originality/value- Unlike traditional approaches that are limited to fixed fault distributions, this work introduces novel design propositions for industrial fault management systems, enabling dynamic learning and continuous improvement with new data.