An incremental FGRA-based fault diagnosis method

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.