Applied Intelligence

, Volume 35, Issue 2, pp 269–284

Weighted and constrained possibilistic C-means clustering for online fault detection and isolation


    • Control and Intelligence Processing Center of Excellence, School of ECEUniversity of Tehran
  • Behzad Moshiri
    • Control and Intelligence Processing Center of Excellence, School of ECEUniversity of Tehran
  • Karim Salahshoor
    • Department of Instrumentation and AutomationPetroleum University of Technology

DOI: 10.1007/s10489-010-0219-2

Cite this article as:
Bahrampour, S., Moshiri, B. & Salahshoor, K. Appl Intell (2011) 35: 269. doi:10.1007/s10489-010-0219-2


In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches.


Fault detection and isolation Possibilistic clustering Feature weighting

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© Springer Science+Business Media, LLC 2010