A fault diagnosis model through G-K fuzzy clustering

A new method for fast building the knowledge based fault diagnosis model by means of fuzzy clustering is proposed. The scheme is contrived by Gustafson-Kessel (GK) algorithm, which is of many good properties. In this paper, it is first investigated how to integrate the properties of fault diagnosis systems into the GK clustering algorithm in the product space of input and output variables. Then the way to convert the fuzzy clusters to the fault diagnosis model is suggested. Hence, an efficient algorithm to acquire the knowledge-based fault diagnosis model from observations is worked out. As a result, the obtained fault diagnosis model can identify fault patterns of different shape and orientation in one data set. Moreover, by introducing the concept of the fuzzy degree of faultiness (DoF), the proposed approach seems to be much more flexible and with more powerful ability to deal with data contaminated by noise compared with the traditional fault diagnosis method. Finally, an experiment of the fault diagnosis of a satellite power supply subsystem demonstrates the effectiveness of the proposed fault diagnosis model.