Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier

• Published in: Measurement : journal of the International Measurement Confederation Vol. 48; pp. 136 - 148
• Main Authors: Su, Zuqiang, Tang, Baoping, Ma, Jinghua, Deng, Lei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2014
• Subjects: Accuracy; Adaptive nearest neighbor classifier; Classifiers; Density; Fault diagnosis; Faults; Incremental enhanced locally linear embedding; Manifold learning; Strategy; Training; Vibration; Vibration signal; Fault diagnosis; Manifold learning; Adaptive nearest neighbor classifier; Vibration signal; Incremental enhanced locally linear embedding
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2013.10.041

•Propose an incremental manifold learning method for dimension reduction.•Propose adaptive nearest neighbor classifier for fault recognition.•Use different-domain features to construct high-dimensional fault sample.•Validate the effectiveness of the proposed method by gearbox fault diagnosis. A novel fault diagnosis method based on incremental enhanced supervised locally linear embedding (I-ESLLE) and adaptive nearest neighbor classifier (ANNC) is proposed to improve the accuracy of machinery fault diagnosis. Firstly, I-ESLLE is proposed for the non-linear dimensionality reduction of high-dimensional fault samples obtained from vibration signals. I-ESLLE can not only acquire the low-dimensional intrinsic manifold structure embedded in the high-dimensional input space, but also can deal with new fault samples in an iterative and batch model. Then, the low-dimensional fault samples are fed into the proposed ANNC for fault type identification. ANNC exploits “representation-based distance” to select the nearest training samples of new fault sample and identifies fault type in a weighting strategy. Moreover, the number of nearest training samples of each new fault sample is adaptively determined according to the density of the local distribution of the new fault sample. To verify the validity of the proposed fault diagnosis method, a fault diagnosis experiment of gearbox is performed, and the results indicate that the proposed fault diagnosis method outperforms the traditional methods and achieves higher diagnostic accuracy.