Mechanical fault feature extraction based on contractive auto-encoder and locality preserving projections

doi:10.16731/j.cnki.1671-3133.2025.02.011

Abstract

Abstract: The performance of the locality preserving projections algorithm mainly depends on the construction of the nearest neighbor map. The construction of the nearest neighbor map was easily affected by the interference of redundant information in the original data and the impact of not having a good basis for selecting the appropriate heat kernel parameters.As a result,the local structural information of high-dimensional data cannot be fully explored,and it was also easy to be more sensitive to noise and outliers in the low-dimensional embedding process.And its feature extraction ability in fault diagnosis applications was affected. To address the above problems,a Locality Preserving Projections algorithm based on Contractive Auto-Encoder and Manifold Ranking (CAE-MRLPP) was proposed and used for mechanical equipment fault diagnosis. Firstly,it combined the label information and Spearman correlation coefficient to pre-adjust the sample spacing. Secondly,the idea of manifold ranking was introduced to construct the weights based on the information of the sorting position of the sample points and the neighboring points in each other′s neighborhood sets and the information of the number of mutual neighbors of the two. Lastly,the contractive auto-encoder was fused with the locality preserving projections based on the manifold ranking,and the optimal projection matrix was solved by iterative optimization of the gradient descent method.Then the low-dimensional representation of the fault data was obtained. A number of verifications were carried out on the rolling bearing dataset and the pumping unit dataset,and the fault identification accuracy was more than 98 %. This indicated that the algorithm has good feature extraction ability,can effectively improve the fault identification accuracy,and has good robustness and generalization ability.

Key words: locality preserving projections, feature extraction, fault diagnosis, contractive auto-encoder, oil pumping machine, rolling bearing

CLC Number:

TH165+.3

HAO Yuxing, LIU Qingqiang. Mechanical fault feature extraction based on contractive auto-encoder and locality preserving projections[J]. Modern Manufacturing Engineering, 2025, 533(2): 84-93.

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