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Title:
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A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems |
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Author:
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Jaradat, Mohammad Abdel Kareem Rasheed |
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Description:
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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 . |
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URI:
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http : / /hdl .handle .net /1969 .1 /4780
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Date:
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2013-03-12 |