Plant Fault Diagnosis System using Negative Selection Algorithm

Naoki Kimura, Yuki Ichikawa, Kazunori Tanihara, Yuichi Makiya, Gen Inoue, Yoshifumi Tsuge

Research output: Chapter in Book/Report/Conference proceedingChapter


Early fault detection and correct diagnosis are required for chemical plants. Therefore, the existing fault detection systems using upper/lower thresholds have difficulties to detect faults when the correlation among process variables breaks without excess of any thresholds. In our previous study, an artificial immune system—especially, negative selection algorithm— had been adopted to fault detection system. Negative selection algorithm is one of methods of artificial immune systems which imitate the vital immune system. We have built up a multiagent based fault detection system using negative selection algorithm. In our system, a set of detectors is generated in each two-dimensional variable space consists of two process variables. In this study, we extend the system to plant fault diagnosis based on the fault detection result using negative selection algorithm. In this paper, we will illustrate our fault detection and diagnosis system using negative selection algorithm. And we will show the detection and diagnosis results when a malfunction occurs in a dynamic plant simulator of a boiler plant.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
Publication statusPublished - Jan 2022

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications


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