A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems
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In this study, an efficient new hybrid approach for multiple sensors data fusion and fault detection is presented, addressing the problem with possible multiple faults, which is based on conventional fuzzy soft clustering and artificial immune system (AIS). The proposed hybrid system approach consists of three main phases. In the first phase signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently a single (fused) signal based on the information provided from the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations and experiments for multiple sensor systems have confirmed the strength of the new approach for online fusing and fault detection. The hybrid system gives a fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the new hybrid approach attractive for solving such fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical systems based on a set of extracted features; these features characterize the collected sensors data. The hybrid system is able to detect the onset of fault conditions which can lead to critical damage or failure. This early detection of failure signs can provide more effective information for any maintenance actions or corrective procedure decisions.
Jaradat, Mohammad Abdel Kareem Rasheed (2005). A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems. Doctoral dissertation, Texas A&M University. Texas A&M University. Available electronically from