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

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.

Automated summary

A mathematical architecture is developed for system-level condition monitoring using fault trees and deep learning. The architecture computes the operation health states of the system and its components based on streaming monitoring data. The applicability of this architecture is demonstrated on a real-world mining stone crusher system and it can be extended to dynamic risk assessment of complex engineering systems. However, caution should be taken when using deep learning models for safety-critical applications.