Life grade recognition method based on supervised uncorrelated orthogonal locality preserving projection and K-nearest neighbor classifier

Abstract

A novel life grade recognition method based on Supervised Uncorrelated Orthogonal Locality Preserving Projection (SUOLPP) and K-nearest neighbor classifier (KNNC) is proposed in this paper. A time–frequency domain feature set is first constructed to completely extract the feature of different life grades, then SUOLPP is proposed to automatically compress the high-dimensional time–frequency domain feature sets of training and test samples into the low-dimensional eigenvectors with better discrimination, and finally the low-dimensional eigenvectors of training and test samples are input into KNNC to conduct life grade recognition. SUOLPP algorithm considers both local information and label information in designing the similarity matrix, and requires the output basis vectors to be statistically uncorrelated and orthogonal in order to improve the life grade feature extraction power of OLPP. KNNC ranks the test samples׳ neighbors among the training samples and uses the class labels of similarity neighbors to classify the unknown input test samples, so that it has such advantages as less calculation amount, finer timeliness and higher pattern recognition accuracy compared with support vector machine (SVM) and Fuzzy C-Means Clustering (FCM). The life grade recognition example on deep groove ball bearings demonstrated the effectivity of the proposed life grade recognition method.