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

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