Fault diagnosis using deep neural networks for industrial alarm sequence clustering

Fault diagnosis using deep neural networks for industrial alarm sequence clustering

연구 분야: Infrastructure

학회: Applied Intelligence

Significant progress has been made in the field of industrial alarm management systems (AMS) in terms of diagnostic and prognostic accuracy. However, persistent challenges, such as poorly configured alarm setups and floods, contribute to an increased number of false alarms, consequently reducing the efficiency of the monitoring system. In addition, more sophisticated models and interactive visualization tools are needed to support supervisors and maintenance operators. This paper proposes a novel approach based on deep learning that combines autoencoder and self-organizing maps to extract valuable features and a clustering algorithm to identify related alarm groups. This bi-level methodology is applied to real manufacturing system datasets, demonstrating its effectiveness in identifying false alarms, reducing alarm sequence interpretation time, enhancing understanding of alarm interrelationships, and providing a basis for causal analysis and root cause identification. The approach also compares favorably with the classical methods in the literature, laying the foundation for improved industrial safety management. The system also offers maintenance recommendations to decision makers, further validating alarm sequences.