Collaborative prognosis using a Weibull statistical hierarchical model

Maharshi Dhada, Lawrence Bull, Mark Girolami and Ajith Parlikad

Reliability Engineering and System Safety, 2025, vol. 262, issue C

Abstract: Collaborative prognosis is a concept that aims at identifying sub-fleets of similarly deteriorating assets and enable learning across these sub-fleets to improve the overall prognosis performance. This paper presents a Weibull statistical hierarchical model to achieve collaborative prognosis by modelling the times-to-failures of sub-fleets, or clusters, of similarly deteriorating assets comprising a fleet. The procedure for real-time collaborative prognosis using the proposed hierarchical Weibull model is described, and demonstrated for a fleet of simulated turbofans. The experiments presented herewith analyse the prediction accuracy along the life of an asset and the effect of clustering on the prediction accuracy. More specifically, a large cluster comprising of similarly operating assets shall enable accurate predictions with high confidence and vice versa. The results show that a hierarchical model mitigates the problem of high variance encountered while independently modelling the times-to-failures observed in the asset clusters with sparse data. Additional advantages include enabling the operator with control over the extent of learning, and showing similarities across the asset clusters comprising the fleet.

Keywords: Hierarchical models; Collaborative prognosis; Predictive maintenance; Bayesian statistics; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025003114
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:262:y:2025:i:c:s0951832025003114

DOI: 10.1016/j.ress.2025.111110

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().