Computer
Advances in the Dempster-Shafer theory of evidence
Neural network design
Ant algorithms for discrete optimization
Artificial Life
Artificial neural network approach for fault detection in rotary system
Applied Soft Computing
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Rough Computing: Theories, Technologies and Applications
Rough Computing: Theories, Technologies and Applications
International Journal of Data Analysis Techniques and Strategies
Vibration based fault diagnosis of monoblock centrifugal pump using decision tree
Expert Systems with Applications: An International Journal
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
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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.