Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers.

► Rough set used to form rules for the fault diagnosis of centrifugal pump.
► Fuzzy inference system (FIS) used to evaluate the rough set rules.
► Different membership functions on the FIS performance are evaluated.
► Performance of Ant-Miner-Fuzzy and multi layer perceptron (MLP) are also evaluated.
► Roughset-fuzzy classifier performs better.