Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals. (15th October 2020)

• Record Type: Journal Article
• Authors: He, Zhiyi; Shao, Haidong; Cheng, Junsheng; Yang, Yu; Xiang, Jiawei
• Is Part Of: Measurement. Volume 163(2020)
• Publication Date: 2020-10-15
• Subjects: Gearbox intelligent diagnosis -- Feature tensor -- Multi-source signals -- Kernel flexible and displaceable convex hull -- Tensor machine; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107965
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: KFDCH-TM is proposed for the gear fault intelligent diagnosis with multi-source signals. Kernel flexible and displaceable convex hull in feature tensor space is defined. The displaceable factor is defined to improve the robustness of KFDCH-TM. Feature tensors are obtained from the multi-source signals. Experimental results show that KFDCH-TM achieves good diagnostic results. Abstract: The methods based on traditional pattern recognition and deep learning have been successfully applied in gearbox intelligent diagnosis. However, traditional pattern recognition methods cannot directly classify feature tensors of multi-source signals, and deep learning networks hardly handle the classification of small samples. Therefore, for the gearbox intelligent diagnosis with multi-source signals, a novel tensor classifier called kernel flexible and displaceable convex hull based tensor machine (KFDCH-TM) is proposed. In KFDCH-TM, the kernel flexible and displaceable convex hull of tensor samples in tensor feature space is defined firstly. Then, an optimal separating hyper-plane between two kernel flexible and displaceable convex hulls is constructed. Meanwhile, feature tensors extracted from multi-source signals through wavelet packet transform (WPT) are used to diagnose gearbox fault by KFDCH-TM. The results of two cases demonstrate that KFDCH-TM can effectively identify gearbox fault with multi-source signals and has better robustness.