Recognition of wear mode using multi-variable synthesis approach based on wavelet packet and improved three-line method

Condition monitoring is an indispensable means of ensuring smooth running of key equipment, because it can improve machinery availability and performance, and also reduce damage and maintenance cost. One kind of condition monitoring is oil monitoring and it is applied extensively because of its capability to provide warning and to predict faults at early stages, with stronger pertinence. But the extraction and selection of features from oil data have always been the bottleneck of its effective application. In this study, prior to extraction and selection of features, denoising was implemented on the oil spectrometric data using 1D-DPT. For the purpose of mining more effective boundary features, we designed amelioration on classical three-line method based on statistics, and thus improved the three-line method. After the denoised signal was decomposed with WT, the three features, boundary, correlation degree and centroid were extracted, respectively, using the improved three-line method, correlation coefficients and K-means clustering. On the basis of these features, multi-variable synthesis analysis was advanced and the distance criterion parameter of synthesis analysis was proposed to classify and identify wear mode. Finally, through the comparison with examples applying the classical three-line method, we demonstrate the better the ability of the improved method to classify and recognize wear patterns with higher accuracy and precision.