New Algorithm for Fault Detection of Diesel Engine Injector Nozzle through Vibration Analysis , T Test and SVM Neural Network

In this study, a new method for the fault detection of the locomotive engine injector nozzle based on vibration analysis and statistical tests, inside artificial neural networks, is presented. For this point, first the under study received vibration signals in the frequency domain is divided into several smaller ranges and the RMS of each range is then extracted as a frequency property and given as an input to the neural network. Because the high selection of the features reduces the accuracy of the neural network, the extracted feature vector with different levels of significance passes through the T-test filters, firstly, and then enters the neural network as an input. Using of this method, the accuracy of the neural network increases from 78.4 to 94.6%, and also help to detect the frequency ranges. According to the results, the fault of the injector nozzle crack increases the intensity of vibrations in the upper band frequencies of 1500 Hz.