Title

A hybrid ensemble scheme for diagnosing new class defects under non-stationary and class imbalance conditions

Document Type

Conference Proceeding

Publication Date

12-9-2017

Publication Title

Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017

Volume

2017-December

First Page

355

Keywords

Fault diagnosis, Hybrid ensemble, imbalanced data, Incremental learning, induction motor, New class defects, nonstationary environments

Last Page

360

Abstract

Strategic necessities to design and implement practical diagnostic systems are the abilities of incremental learning and diagnosing new class defects under non-stationary and class imbalance conditions. In this work, a hybrid ensemble scheme, named Learn++NCS, is adopted for diagnosing bearing defects in induction motors. This diagnostic scheme includes a feature extraction module and a hybrid ensemble scheme. The former intends to extract discriminant features from the vibrational signals. The latter collects various class imbalance sets of samples chunk by chunk from a non-stationary environment, constructs a hybrid ensemble by means of a consultation and voting mechanism, incrementally learns novel features-defects relations and diagnoses new class defects. Experimental results present the effectiveness of the proposed hybrid scheme.

DOI

10.1109/SDPC.2017.74

ISBN

9781509040209

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