A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments

Roozbeh Razavi-Far, Ehsan Hallaji, Mehrdad Saif, Gregory Ditzler

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications.

Original languageEnglish (US)
Article number8336956
Pages (from-to)561-570
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Diagnostic systems
  • extreme verification latency (EVL)
  • nonstationary environments
  • novelty detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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