Supervised Manifold Learning Based on Multi-Feature Information Discriminative Fusion within an Adaptive Nearest Neighbor Strategy Applied to Rolling Bearing Fault Diagnosis
Author:
Wang Hongwei1, Yao Linhu2ORCID, Wang Haoran1, Liu Yu2, Li Zhiyuan2, Wang Di2, Hu Ren2, Tao Lei1
Affiliation:
1. Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China 2. College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Abstract
Rolling bearings are a key component for ensuring the safe and smooth operation of rotating machinery and are very prone to failure. Therefore, intelligent fault diagnosis research on rolling bearings has become a crucial task in the field of mechanical fault diagnosis. This paper proposes research on the fault diagnosis of rolling bearings based on an adaptive nearest neighbor strategy and the discriminative fusion of multi-feature information using supervised manifold learning (AN-MFIDFS-Isomap). Firstly, an adaptive nearest neighbor strategy is proposed using the Euclidean distance and cosine similarity to optimize the selection of neighboring points. Secondly, three feature space transformation and feature information extraction methods are proposed, among which an innovative exponential linear kernel function is introduced to provide new feature information descriptions for the data, enhancing feature sensitivity. Finally, under the adaptive nearest neighbor strategy, a novel AN-MFIDFS-Isomap algorithm is proposed for rolling bearing fault diagnosis by fusing various feature information and classifiers through discriminative fusion with label information. The proposed AN-MFIDFS-Isomap algorithm is validated on the CWRU open dataset and our experimental dataset. The experiments show that the proposed method outperforms other traditional manifold learning methods in terms of data clustering and fault diagnosis.
Funder
Key R&D Program of Shanxi Province National Key Research and Development Program of China Bidding Project of Shanxi Province National Key Research and Development Program of Shanxi Province Central Guidance for Local Science and Technology Development Projects
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference35 articles.
1. Zhu, Z., Lei, Y., Qi, G., Chai, Y., Mazur, N., An, Y., and Huang, X. (2023). A Review of the Application of Deep Learning in Intelligent Fault Diagnosis of Rotating Machinery. Measurement, 206. 2. Trstanova, Z., Leimkuhler, B., and Lelièvre, T. (2020). Local and Global Perspectives on Diffusion Maps in the Analysis of Molecular Systems. Proc. R. Soc. Math. Phys. Eng. Sci., 476. 3. Wang, H., Fang, Z., Wang, H., Li, Y., Geng, Y., Chen, L., and Chang, X. (2023). A Novel Time-Frequency Analysis Method for Fault Diagnosis Based on Generalized S-Transform and Synchroextracting Transform. Meas. Sci. Technol., 35. 4. Peng, C., Gao, H., Liu, X., and Liu, B. (2023). A Visual Vibration Characterization Method for Intelligent Fault Diagnosis of Rotating Machinery. Mech. Syst. Signal Process., 192. 5. Fault Diagnosis Method Using Supervised Extended Local Tangent Space Alignment for Dimension Reduction;Su;Measurement,2015
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