Title

Fault Detection of Induction Motors Using Just in Time Classifiers

Document Type

Conference Proceeding

Publication Date

2-23-2021

Publication Title

2021 7th International Conference on Control, Instrumentation and Automation, ICCIA 2021

Keywords

Abrupt concepts, Gradual concepts, Just in time classifiers, Recurrent concepts, Stator fault

Abstract

Induction motors (IMs) are efficient and reliable but may fail like any other machinery. Failure to detect IM faults in a timely manner will result in irreparable damage. In the real world, data collected from IMs is due to a change in status (load change, speed change, etc.), which can lead to concept drift (CD). In this work, stator data is collected under dynamic conditions from a setup IM in the laboratory. We use Just In Time (JIT) classifiers to detect stator fault in the non-stationary environments, which can be adapted to CD and increase classification accuracy, and the Intersection of Confidence Interval-based change detection tests is used to detect the CD. This mechanism is extended using a preprocessing method for multi-dimensional data. To evaluate this method, several scenarios have been used to simulate CD in the forms of abrupt, gradual, recurrent concepts and the appearance of a new class.

DOI

10.1109/ICCIA52082.2021.9403546

ISBN

9780738124056

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