Improving learning vector quantization classifier in machine fault diagnosis by adding consistency

This paper presents a hybrid neural network system which combines the learning vector quantization (LVQ) classifier with the theory of consistency. The hybrid system employs consistency to measure the degree of matching between the input feature vectors and the output classes. In the calculation of the consistency, the probability distribution is embedded to describe the occurring frequencies of various classes in a neighborhood region associated with the input feature. This successfully avoids the case that usually occurs in complex classification problems of machine faults, that is, one or a few deviated input feature affecting the Euclidean distance and leads to misclassifications. Experiments shows that the consistency can improve the classification capability of LVQ by not only reducing the influence of distorted features but also making the boundaries of overlapped classes more discriminative. From the results of identifying faults occurred in a tapping machine, it is demonstrated that the successful rate of classification using this hybrid method outweighed the backpropagation and the conventional LVQ classifiers