FaultBuster: data driven fault detection and diagnosis for industrial systems

Nicola Bergantino, Fabio Caponetti, Sauro Longhi

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Abstract

Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks. Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while further investigations of the diagnostic system are still needed.
Original languageEnglish
Title of host publication7th Workshop on Advanced Control and Diagnosis
Publication date2009
Pages46
Publication statusPublished - 2009
Event7th Workshop on Advanced Control and Diagnosis - Zielona Góra, Poland
Duration: 19 Jan 200920 Nov 2009
Conference number: 7

Workshop

Workshop7th Workshop on Advanced Control and Diagnosis
Number7
CountryPoland
CityZielona Góra
Period19/01/200920/11/2009

Keywords

  • Articial intelligence
  • Statistical Process Control
  • Diagnostic inference
  • Fault detection

Cite this

Bergantino, N., Caponetti, F., & Longhi, S. (2009). FaultBuster: data driven fault detection and diagnosis for industrial systems. In 7th Workshop on Advanced Control and Diagnosis (pp. 46) http://www.issi.uz.zgora.pl/ACD_2009/