Fault detection and diagnosis using statistical control charts and artificial neural networks

  • Leger R
  • Garland W
  • Poehlman W
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In order to operate a successful plant or process, continuous improvement
must be made in the areas of safety, quality and reliability. Central
to this continuous improvement is the early or proactive detection
and correct diagnosis of process faults. This research examines the
feasibility of using cumulative summation (CUSUM) control charts
and artificial neural networks together for fault detection and diagnosis
(FDD). The proposed FDD strategy was tested on a model of the heat
transport system of a CANDU nuclear reactor. The results of the investigation
indicate that a FDD system using CUSUM control charts and a radial
basis function (RBF) neural network is not only feasible but also
of promising potential. The control charts and neural network are
linked by using a characteristic fault signature pattern for each
fault which is to be detected and diagnosed. When tested, the system
was able to eliminate all false alarms at steady state, promptly
detect six fault conditions, and correctly diagnose five out of the
six faults. The diagnosis for the sixth fault was inconclusive. (C)
1997 Elsevier Science Limited.

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  • R P Leger

  • W J Garland

  • W F S Poehlman

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