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 early fault detection and future fault forecasting.
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.
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.
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.
