Fuzzy lattice classifier and its application to bearing fault diagnosis

In this work, we present a novel classification scheme named fuzzy lattice classifier (FLC) based on the lattice framework and apply it to the bearing faults diagnosis problem. Different from the fuzzy lattice reasoning (FLR) model developed in literature, there is no need to tune any parameter and to compute the inclusion measure in the training procedure in our new FLC model. It can converge rapidly in a single pass through training patterns with a few induced rules. A series of experiments are conducted on five popular benchmark datasets and three bearing datasets to evaluate and compare the presented FLC with the FLR model as well as some other widely used classification methods. Experimental results indicate that the FLC yields a satisfactory classification performance with higher computation efficiency than other classifiers. It is very desirable to utilize the FLC scheme for on-line condition monitoring of bearings and other mechanical systems.

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► A fuzzy lattice classifier (FLC) based on the lattice framework is presented.
► The FLC can converge rapidly in a single pass through of the training samples.
► The main computations involved in FLC are the lattice operations.
► No parameter is needed to be tuned in FLC.
► Fewer rules are generated by FLC than the FLR model in literature.