Fault Diagnosis of Valve Clearance in Diesel Engine Based on BP Neural Network and Support Vector Machine

Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square(RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network(GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.

Diesel Engine Fault Diagnosis with Vibration Signal

When the vibration of diesel engine structure is measured, the signal is composed of a very complex superposition of the contributions of different vibratory sources modified by their respective transmission paths. These sources originate from several internal phenomenon in the engine such as combustion pressure variation, unbalanced reciprocating and rotating parts. In a diesel engine, movement parts work in a specific order. Once the starting point is determined, occurrence of work order in different cycle phases can be determined. This could successfully use to identifying of impulses in complex vibration signal of a diesel engine. From the variation of features of those impulses, it is possible to determine the working condition of the engine. This can use to fault diagnosis of diesel engine, specially faults related to combustion process.