Learning in the model space for cognitive fault diagnosis

Huanhuan CHEN, Peter TINO, Ali RODAN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

96 Citations (Scopus)

Abstract

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework. © 2012 IEEE.
Original languageEnglish
Article number6515601
Pages (from-to)124-136
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number1
DOIs
Publication statusPublished - Jan 2014
Externally publishedYes

Keywords

  • Cognitive fault diagnosis
  • fault detection
  • learning in the model space
  • one class learning
  • reservoir computing (RC)

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