Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery

An industrial machinery condition monitoring methodology based on ensemble novelty detection and evolving classification is proposed in this study. The methodology contributes to solve current challenges dealing with classical electromechanical system monitoring approaches applied in industrial fram...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.49755-49766
Main Authors: Carino, Jesus A., Delgado-Prieto, Miguel, Iglesias, Jose Antonio, Sanchis, Araceli, Zurita, Daniel, Millan, Marta, Ortega Redondo, Juan Antonio, Romero-Troncoso, Rene
Format: Article
Language:English
Subjects:
Adaptation models
Classification
Condition Monitoring
Data models
Electromechanical systems
Enginyeria de la telecomunicació
Evolution
Fault detection
Fault diagnosis
Fault tolerance (Engineering)
industry applications
machine learning
Machinery condition monitoring
Machinery in the workplace
Maquinària en la indústria
Methodology
Pattern recognition
Pattern recognition systems
Processament del senyal
Reconeixement de formes
Reconeixement de formes (Informàtica)
Support vector machines
Tolerància als errors (Enginyeria)
Training
Àrees temàtiques de la UPC
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Summary:An industrial machinery condition monitoring methodology based on ensemble novelty detection and evolving classification is proposed in this study. The methodology contributes to solve current challenges dealing with classical electromechanical system monitoring approaches applied in industrial frameworks, that is, the presence of unknown events, the limitation to the nominal healthy condition as starting knowledge, and the incorporation of new patterns to the available knowledge. The proposed methodology is divided into four main stages: 1) a dedicated feature calculation and reduction over available physical magnitudes to increase novelty detection and fault classification capabilities; 2) a novelty detection based on the ensemble of one-class support vector machines to identify not previously considered events; 3) a diagnosis by means of eClass evolving classifiers for patterns recognition; and 4) re-training to include new patterns to the novelty detection and fault identification models. The effectiveness of the proposed fault detection and identification methodology has been compared with classical approaches, and verified by experimental results obtained from an automotive end-of-line test machine.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2868430