Remaining useful life prediction of automotive engine oils using MEMS technologies

This paper proposes a novel adaptive methodology where both micro-sensors and models are used in conjunction with neural network/fuzzy classification algorithm to predict the quality of engine oils. The condition of the engine oil is defined as a single variable and trended. Advanced prognostic algorithms are then applied on the oil condition trends to predict the remaining useful life of engine oils. Experimental results are given.