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

Ramin Moradi, Andrés Ruiz-Tagle Palazuelos, Enrique Lopez Droguett and Katrina M Groth

Journal of Risk and Reliability, 2023, vol. 237, issue 5, 910-921

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.

Keywords: Deep learning; Fault Tree; model calibration; system-level monitoring; dynamic risk assessment; prognostics and health management (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:237:y:2023:i:5:p:910-921

DOI: 10.1177/1748006X221079964

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