Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

•We propose a general method for extracting a health indicator from signal measures.•The method allows building monotonic, trendable and prognosable health indicators.•The method is applied to turbofan engine degradation data.•The obtained prognostic model outperforms other literature approaches. Th...

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Bibliographic Details
Published in:Mechanical systems and signal processing 2018-03, Vol.102, p.382-400
Main Authors: Baraldi, P., Bonfanti, G., Zio, E.
Format: Article
Language:English
Subjects:
Associative kernel regression
Binary differential evolution
Degradation
Engines
Evolutionary algorithms
Fault diagnosis
Fault diagnostics
Feature extraction
Health
Health indicator
Medical prognosis
Multiple objective analysis
Optimization
Predictive maintenance
Prognostics
Statistics
Studies
Turbofan engine
Turbofan engines
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Summary:•We propose a general method for extracting a health indicator from signal measures.•The method allows building monotonic, trendable and prognosable health indicators.•The method is applied to turbofan engine degradation data.•The obtained prognostic model outperforms other literature approaches. The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.
ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2017.09.013