The internal combustion engine (ICE) is widely used in applications such as automobiles, motorcycles and ships. After its long-term use faults occur that degrade its performance or cause it to malfunction. Therefore ICE fault detection and diagnosis (FDD) research is important for preventing serious economic loss and even human injuries caused by undetected faults.

The development of an ICE FDD experimental setup is described. The setup uses sensors to measure the ICE vibration and FDD techniques implemented as a computer program. One common ICE fault, the misfire fault, is deliberately induced in some of the experiments by disconnecting a spark plug. The objective of the FDD is to determine if there is a misfire fault (or not), and which spark plug is faulty.

Several FDD algorithms are proposed, one category of which is based on data processing techniques such as the variational mode decomposition (VMD) and the wavelet transform. This category of FDD algorithms includes the VMD-based FDD algorithm, wavelet-based kernel principle component analysis (KPCA) and VMD-based KPCA. The VMD-based FDD algorithm introduces a new FDD index based on VMD and statistics. According to the included experimental results, all of these algorithms are capable of detecting and locating the misfire fault with 100% accuracy.

A new SVSF-based training algorithm for the radial-basis-function (RBF) artificial neural network is also proposed. The running-averaged wavelet coefficients of vibration data are used as the network input. The included experimental results show these SVSF-trained networks above achieve 100% accuracy in classifying the misfire faults. The SVSF-based training algorithm also produces a faster convergence rate.