Principal component analysis for fault detection and diagnosis. experience with a pilot plant

  • Authors:

  • Thamara Villegas;María Jesús Fuente;Miguel Rodríguez

  • Affiliations:

  • Dept. of Electronic and Circuits, Simón Bolívar University, Caracas, Venezuela;Dept. of Engineering and Control, Valladolid University, Valladolid, Spain;Dept. of Industrial Tecnology, Simón Bolívar University, Caracas, Venezuela

  • Venue:
  • CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
  • Year:
  • 2010

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Abstract

This paper describes the application of Principal Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. PCA is a linear dimensionality reduction technique. In order to diagnosis the faults, the PCA approach includes one PCA model for each system behavior, i.e., a PCA model for normal operation conditions and a PCA model for each faulty situation. Data set is generated in closed loop. The method of fault detection and diagnosis is based on the definition of threshold minimum. These are calculated by the Q statistics and levels of significance. The PCA models outputs (in this case the Q statistics) are compared with theirs thresholds minimum, with and without faults. The only one that does not violate it threshold says us the actual system situation, i.e., identify the fault. Finally, this technique is applied to a two tanks system, and can be demonstrated that it is possible to detect and identify faults.