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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4780 |
| Date | 25 April 2007 |
| Creators | Jaradat, Mohammad Abdel Kareem Rasheed |
| Contributors | Langari, Reza |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1001273 bytes, electronic, application/pdf, born digital |
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