Produção Nacional
Revisado por pares
Improved similarity-based modeling for the classification of rotating-machine failures
2017; Elsevier BV; Volume: 355; Issue: 4 Linguagem: Inglês
10.1016/j.jfranklin.2017.07.038
ISSN1879-2693
AutoresMatheus Araújo Marins, Felipe M. L. Ribeiro, Sérgio L. Netto, Eduardo A. B. da Silva,
Tópico(s)Gear and Bearing Dynamics Analysis
ResumoSimilarity-based modeling (SBM) is a technique whereby the normal operation of a system is modeled in order to detect faults by analyzing their similarity to the normal system states. First proposed around two decades ago, SBM has been successfully used for fault detection in varied systems. In spite of this success, there is not much study performed in the literature regarding its design, that encompasses both similarity metrics and model training. This work aims at contributing with an in-depth study of SBM for fault detection considering these two design aspects. This is done in the context of proposing a novel system to identify rotating-machinery faults based on SBM, that is employed either as a standalone classifier or to generate features for a random forest classifier. New approaches for training the model and new similarity metrics are investigated. Experimental results are shown for the recently developed Machinery Fault Database (MaFaulDa) that has an extensive set of sequences and fault types, and for the Case Western Reserve University (CWRU) bearing database. Results for both databases indicate that the proposed techniques increase the generalization power of the similarity model and of the associated classifier, achieving accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database.
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