Fault Diagnosis Methods Based on Local Tangent Space Alignment and K-Nearest Neighbor Classifier

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (11) : 134-139.

Jingsheng Jiang, Huaqing Wang, Yanliang Ke, Wei Xiang

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Abstract

Aiming at the problem that the performance of local tangent space alignment(LTSA) is greatly influenced by nearest neighbor k, the fault diagnosis mode of LTSA and K-nearest neighbor classifier(KNN)based on clustering criterion is proposed. First, the vibration signal collected was used to construct high dimensional matrix, then the high dimensional matrix was standardized before dimensionality reduction. According to clustering criterion, the nearest neighbor k in LTSA is chosen, and then the low dimensional feature vector of high dimensional matrix in the LTSA was extracted. Finally, the extracted low dimensional feature vectors were put into KNN to do fault pattern recognition. The results from fault diagnosis experimental system show that this method based on clustering criterion can effectively overcome the choice blindness of the optimal neighbor number k, and improve the precision of dimension reduction and recognition rate of the fault diagnosis pattern. Compared with the principal component analysis (PCA) method and Laplacian eigenmaps（LE）method, the method proposed is more suitable for bearing fault pattern recognition.

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local tangent space alignment (LTSA) / K-nearest neighbor (KNN) / clustering criterion / fault diagnosi

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Jingsheng Jiang, Huaqing Wang, Yanliang Ke, Wei Xiang. Fault Diagnosis Methods Based on Local Tangent Space Alignment and K-Nearest Neighbor Classifier[J]. Journal of Vibration and Shock, 2017, 36(11): 134-139

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