A NEW DATA MINING APPROACH FOR GEAR CRACK LEVEL IDENTIFICATION BASED ON MANIFOLD LEARNING

Zhixiong Li,Jingping Wu,Yu Jiang,Xinping Yan,Li Qin

doi:10.5755/j01.mech.18.1.1276

Zhixiong Li, Jingping Wu + Show 3 more

Open Access

https://doi.org/10.5755/j01.mech.18.1.1276

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Abstract

Gear crack is a common damage model in the gear mechanisms, and an unexpected serious crack may break the transmission system down, leading to significant economic losses. Efficient incipient fault detection and diagnosis are therefore critical to machinery normal running. One of the key points of the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanic fault diagnosis, and nonlinear feature extraction for gear crack detection are scarce. This paper reports a novel data mining method based on the empirical mode decomposition (EMD) and supervised locally linear embedding (SLLE) applied to gear crack level identification. The EMD was used to decompose the vibration signals into a number of intrinsic mode functions (IMFs) for feature extraction, whilst the SLLE for nonlinear feature selection. The experimental vibration data acquired from the gear fault test-bed were processed for feature reduction and extraction using the proposed method. Study results show that the sensitive characteristics between different gear crack severity vibration signals can be revealed effectively by EMD-SLLE. The energy distribution and the statistic features of IMFs vary with the change of the gear operation conditions, and the most distinguished features can be extracted by nonlinear method of SLLE. In addition, the performance of feature extraction of SLLE is better than that of the linear method of principal component analysis (PCA).DOI: http://dx.doi.org/10.5755/j01.mech.18.1.1276

Highlights

Gear transmission systems are widely served in industrial application
To verify the efficacy of the proposed scheme, the experimental tests were carried out in the present work, and the analysis results demonstrate that the proposed method based on the empirical mode decomposition (EMD)-supervised locally linear embedding (SLLE) is effective and efficient for the gear crack level identification
We review EMD first, and SLLE

Summary

Introduction

Gear transmission systems are widely served in industrial application. Generally working in severe conditions, gears are subjected to progressive deterioration of their state [1]. Advanced techniques, including Wigner-Ville distribution (WVD) [6], empirical mode decomposition (EMD) [7, 8] and wavelet transform (WT) [9, 10], etc., are introduced into the analysis of nonstationary signals These methods can handle a large number of variables and are very powerful for fault detection [6,7,8,9,10]. The representative methods include Isomap [12], Laplacian eigenmap [13] and locally linear embedding (LLE) [14], etc Successful applications of these new nonlinear feature selection methodologies can be found in the field of image processing, speech spectrograms, EEG and ECG signals for medical diagnose [15]. To verify the efficacy of the proposed scheme, the experimental tests were carried out in the present work, and the analysis results demonstrate that the proposed method based on the EMD-SLLE is effective and efficient for the gear crack level identification

Description of new data mining algorithm

Experimental setup and tests

Application of proposed diagnosis method

Conclusions

Findings

Summary