6 April 1995 Fusing human knowledge with neural networks in machine condition monitoring systems
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Abstract
There is currently much interest in the application of artificial neural network (ANN) technology to the field of on-line machine condition monitoring (CM) for complex electro- mechanical systems. In this paper the authors discuss, with the help of an industrial case study, a few of the difficulties inherent in the application of neural network based condition monitoring. A method of overcoming these difficulties by utilizing a combination of human knowledge, encoded using techniques borrowed from fuzzy logic, Kohonen neural networks, and statistical K-means clustering has been constructed. The methodology is discussed in the paper by means of a direct comparison between this new approach and a purely neural approach. An analysis of other situations where this approach would be applicable is also presented and the paper discusses other current research work in the area of hybrid AI technologies which should assist further with the alleviation of the problems under consideration.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David G. Melvin, J. Penman, "Fusing human knowledge with neural networks in machine condition monitoring systems", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205134; https://doi.org/10.1117/12.205134
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