An incremental FGRA-based fault diagnosis method

Abstract

In order to effectively solve the uncertainty small-samples fault diagnosis problem, a practical data-driven grey-based fault detection and diagnosis (FDD) method for complex equipments is investigated. Firstly, an improved fuzzy-grey relational analysis (FGRA) technique is proposed by introducing dynamic identification coefficient and fuzzy relational weight. Compared with the traditional Deng's grey relational analysis (DGRA) technique, the proposed FGRA technique not only can strengthen the veracity and reliability but also can reduce the dependence of uncertain man-made identification and weight coefficient. Secondly, a simple and practical FGRA-based fault diagnosis process is designed. It belongs to a data-driven analytic method which does not need to consider the either statistic assumptions or distributions of diagnosis variables. Finally, the validity and practicability of the proposed FGRA-based method is demonstrated by a example of rotor fault diagnosis, and the results show that the proposed method is more effective than the DGRA-based method.