Signal Model-Based Fault Coding for Diagnostics and Prognostics of Analog Electronic Circuits

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

28 Scopus Citations
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Author(s)

  • Zhenbao Liu
  • Taimin Liu
  • Junwei Han
  • Shuhui Bu
  • Xiaojun Tang
  • And 1 others
  • Michael Pecht

Detail(s)

Original languageEnglish
Article number7539593
Pages (from-to)605-614
Journal / PublicationIEEE Transactions on Industrial Electronics
Volume64
Issue number1
Online published10 Aug 2016
Publication statusPublished - Jan 2017

Abstract

Analog circuits have been extensively used in industrial systems, and their failure may make the systems work abnormally and even cause accidents. In order to monitor their status, detect faults, and predict their failure early, this study proposes a signal model-based fault coding to monitor the circuit response after being stimulated to perform a fault diagnosis without training a large amount of sample data and fault classifiers. Manifold features extracted from circuit responses are associated with a fault-indicating curve in the feature space, in which a group of fault bases are uniformly and continuously distributed along with gradual deviation from the nominal value of one critical component. These bases can be deployed in a factory setting but used during field operation. Fault coding is converted to a novel optimization problem, and the optimized solution forms a fault code representing fault class, suitable for realizing fault detection, and isolation for different components. A fault indicator based on comparison between fault codes can describe performance degradation trends. To improve the prediction accuracy, historical degradation data are collected and considered as a priori exemplars, and a novel exemplar-based conditional particle filter is proposed to track a degradation process for the prediction of remaining useful performance. Case studies on two analog filter circuits demonstrate that the proposed method achieves relatively high fault diagnosis and prognosis accuracy. The main advantages of our study are two-fold: first, the high diagnostic accuracy can still be obtained even if there is no large amount of training data; second, the prognostic effect remains relatively stable whenever triggering prognosis module.

Research Area(s)

  • Analog circuit, failure prognosis, fault coding, fault diagnosis, signal model

Citation Format(s)

Signal Model-Based Fault Coding for Diagnostics and Prognostics of Analog Electronic Circuits. / Liu, Zhenbao; Liu, Taimin; Han, Junwei; Bu, Shuhui; Tang, Xiaojun; Pecht, Michael.

In: IEEE Transactions on Industrial Electronics, Vol. 64, No. 1, 7539593, 01.2017, p. 605-614.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal