Integration of Condition-Based, Diagnostic, Prognostic, and Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

Mandelli, D.; Wang, C.; Agarwal, V.; Lin, L.

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https://hdl.handle.net/2142/121800 Copy

Description

Title

Integration of Condition-Based, Diagnostic, Prognostic, and Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

Author(s)

• Mandelli, D.
• Wang, C.
• Agarwal, V.
• Lin, L.

Issue Date

2023

Keyword(s)

• Reliability
• Diagnostic
• Prognostic
• Condition-based
• Predictive maintenance

Abstract

Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We first show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. Lastly, we show how a margin-based reliability approach can support a predictive maintenance context where margin information is employed in such a way that maintenance operations can be scheduled and performed for each asset only when needed.

Type of Resource

text

Language

eng

Handle URL

https://hdl.handle.net/2142/121800

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