A hybrid intelligent system for fault detection and sensor fusion

Authors:
Mohammad Abdel Kareem Jaradat;Reza Langari
Affiliations:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, United States and Department of Mechanical Engineering, Faculty of Engineering, Jordan University of Science & T ...;Department of Mechanical Engineering, Texas A&M University, College Station, Texas, United States
Venue:
Applied Soft Computing
Year:
2009

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Abstract

In this paper, an efficient new hybrid approach for multiple sensor fusion and fault detection is proposed, addressing the problem with multiple faults, which is based on conventional fuzzy soft clustering and artificial immune systems. For this new approach, requires no prior knowledge or information about the sensors, or the system behavior, and no learning processes are required. The proposed hybrid approach consists of two main phases. In the first phase a single fuser for the input sensor signals is generated using the fuzzy clustering c-means algorithm. The fused output is based on the cluster centers that contain the maximum number of the input elements. In the second phase a fault detector was generated base on the artificial immune system AIS.