Abstract
Wear over time affects engine’s reliability and efficiency. On-line wear monitoring could provide timely information about engine health condition. In the current study, on-line monitoring of engine wear via an on-line visual ferrograph was performed in reliability tests of gasoline engines, and a wavelet-analysis-based differential method of data analysis for wear condition estimation was proposed. The tests were designed for 220 h, which consist of a running-in stage of 20 h and a thermal shock cycle test stage of 200 h. One of the tests was terminated because of failures in the main bearings and crankshaft journal at 146th hour of thermal shock cycle test, while the other two completed successfully without failures. Index of particle coverage area, which represents wear-debris concentration in lube oil, was studied, and piecewise trend-extraction of index of particle coverage area was achieved by wavelet decomposition and reconstruction. The first-order differential of the index of particle coverage area trend was used to represent the wear rate or the generation rate of debris for health condition assessment of engines. Off-line oil analyses were performed in laboratory via an analytical ferrograph, and engine disassembly results of the engines were given to determine the causes of engine failures if it happened. It is found that favorable trend extraction from index of particle coverage area could be achieved by the segmented wavelet de-noising. Moreover, on-line visual ferrograph monitoring estimated the engine wear at macro-levels effectively, and it provided an advance warning for the failures after the continued deterioration of the engine wear. The study and application of this method can make early failure prediction of engine and avoid serious fault.
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