Slow feature-based feature fusion methodology for machinery similarity-based prognostics.
ISA Trans
; 2024 Jun 18.
Article
em En
| MEDLINE
| ID: mdl-38910090
ABSTRACT
Similarity-based prediction methods utilize degradation trend analysis based on degradation indicators (DIs). These methods are gaining prominence in industrial predictive maintenance because they effectively address prognostics for machines with unknown failure mechanisms. However, current studies often neglect the discrepancies in degradation trends when constructing DIs from multi-sensor data and lack automatic normalization of operating regimes during feature fusion. In this study, a feature fusion methodology based on a signal-to-noise ratio metric that leverages slow feature analysis (SFA) is proposed. This customized metric utilizes SFA to quantify degradation trend discrepancies of constructed DIs, while automatically filtering out the effects of multiple operating regimes during feature fusion. The effectiveness and superiority of the proposed method are demonstrated using publicly available aero-engine and rolling bearing datasets.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
ISA Trans
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA