Novel hybrid clustering algorithm and its application to fault diagnosis

Aiming at the fuzzy C-means (FCM) clustering algorithm supposing the uniform influence to clustering by different features and samples, and setting the cluster number beforehand, a novel hybrid clustering algorithm based on 3 layer forward neural networks (FNN), an algorithm of distribution density function of data point and the cluster validity index is proposed. Feature weighting and sample weighting are considered in this hybrid clustering algorithm and the cluster number is automatically set by using the cluster validity index to finish clustering. Feature weights are adaptively learned via FNN with the gradient descent technique under the unsupervised mode of training, and sample weights are computed through the algorithm of distribution density function of data point. Then, the feature weights and the sample weights are assigned to the corresponding features and samples to emphasize the leading effect of sensitive features and typical samples, and weaken the interference of other features and samples. The proposed algorithm is employed to analyze the benchmark data and the practical data from locomotive bearings, and the results show that the algorithm enables to automatically and correctly set cluster number and its clustering performance is better than that of the FCM.