Toward a framework for risk monitoring of complex engineering systems with online operational data: A deep learning-based solution

Abstract

A mathematical architecture is developed for system-level condition monitoring. This architecture is built toward performing end-to-end operation risk and condition monitoring. The streaming monitoring data is given to the architecture as the input and system-level and component-level operation health states are computed as the output. This architecture integrates fault trees as the system-level modeling method and Deep Learning (DL) as the components condition monitoring method. A number of different deep learning models are trained using both operation and maintenance data for the components. Then, the fault tree fuses the continuous components’ assessments to provide system-level health insight. The applicability of this architecture is tested by implementing it on a real-world mining stone crusher system. This approach is extendable to dynamic risk assessment of complex engineering systems. However, DL models should be used with caution for safety-critical applications. We show that having DL models with high accuracy is not enough for trusting their predictions. We discuss the calibration of DL-based condition monitoring models and demonstrate how they can improve the trustworthiness and interpretability of DL models in risk and reliability applications.

Reliability assessment of bridge hangers based on fault tree with response surface

“…Subsequently, they manipulated the fault tree analytically and resolved the resulting broken structure using a fault tree quantifier. More recently, Moradi et al (2023) trained various deep learning models using both operational and maintenance data for system components. By integrating fault tree analysis, they were able to fuse continuous component assessments and provide insights into the system's overall health at a system-level.…”