Failure prediction of engine driven by CNG through prognostic approach

Abstract

Application of alternative fuels in engines is essential and encouraged for their non-polluting newlinenature as well as shifting from fossil fuel use. But engine life span should be investigated for newlinebetter efficiency and economy. In this study, industrial diesel and CNG engines have been newlineinvestigated periodically for the surface degradation through wear. Ferrographic techniques newlinehave been applied on the used lubricating oils for checking the engine wear conditions. Different newlinewear parameters have been obtained by quantitative ferrographic technique. Bath-tub curves newlinehave been plotted for the cases based on their severity index. Qualitative ferrographic technique newlineenables to obtain images of the wear particles. The raw image captured from the CCD newlinemicroscope is processed through a series of image processing techniques to obtain the final newlineimage. Fractal computation is performed to estimate the surface roughness of the particle newlineboundary, which indicates the wear failure. Fractal dimensions of the wear particles have been newlinecompared for both the cases newline