A new engine fault diagnosis method based on spectrometric oil analysis

Abstract

According to statistics, wear fault is about sixty percent to eighty percent of all the machinery faults. Spectrometric oil analysis is an important condition monitoring and fault diagnosis technique for machinery maintenance. In practice, there are two existing fault diagnosis model of the engine based on spectrometric oil analysis, namely concentration model and gradient model. However, the two above models have their respective disadvantages in condition monitoring and fault diagnosis of the engine. In this paper, a new condition monitoring and fault diagnosis method, proportional model is described. Proportional model use the correlation among the elements in the lubricating oil to detect wear condition and occurring faults in the engine. Then the limit value of proportional model is established by analyzing a lot of spectrum data. In order to validate the availability and effect of proportional model, this paper apply proportional model to an engine and sampling the lubricating oil every 5 hours. Through analyzing the lubricating oil by spectrometer, we find that proportional model could find the abnormal wear information in spectrum data, give more accurate result of wear condition and give the fault form in the engine. The results from this paper prove that this method based on proportional model is applicable and available in condition monitoring and fault diagnosis of the engine.