A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets. (August 2017)

• Record Type: Journal Article
• Authors: Al-Dahidi, Sameer; Di Maio, Francesco; Baraldi, Piero; Zio, Enrico
• Other Names: Podofillini Luca guest-editor.; Sudret Bruno guest-editor.; Stojadinović Božidar guest-editor.; Zio Enrico guest-editor.; Kröger Wolfgang guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 4(2017:Aug.)
• Page Start: 350
• Page End: 363
• Publication Date: 2017-08
• Subjects: Fault prognostics -- remaining useful life -- locally adaptive ensemble -- heterogeneous fleet -- homogeneous discrete-time finite-state semi-Markov model -- fuzzy similarity–based model -- aluminum electrolytic capacitors; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X17693519
• Languages: English
• ISSNs: 1748-006X

Abstract

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.