Fault diagnosis of internal combustion engine using empirical mode decomposition and artificial neural networks

In this paper, a novel approach has been proposed for fault diagnosis of internal combustion (IC) engine using Empirical Mode Decomposition (EMD) and Neural Network. Live signals from the engines were collected with and without faults by using four sensors. The vibration signals measured from the large number of faulty engines were decomposed into a number of Intrinsic Mode Functions (IMFs). Each IMF corresponds to a specific range of the frequency component embedded in the vibration signal. This paper proposes the use of EMD technique for finding IMFs. The Cumulative Mode Function (CMF) was chosen rather than IMFs since all the IMFs are not useful to reveal the vibration signal characteristics due to the effect of noise. Statistical parameters like shape factor, crest factor etc. of the envelope spectrum of CMF were investigated as an indicator for the presence of faults. These statistical parameters are used in turn for classification of faults using Neural Networks. Resilient Propagation which is a rapidly converging neural network algorithm is used for classification of faults. The accuracy obtained by using EMD-ANN technique effectively in IC engine diagnosis for various faults is more than 85% with each sensor. By using a majority voting approach 96% accuracy has been achieved in fault classification.