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Noise source identification and separation of internal combustion engines is an effective tool for engine NVH (noise, vibration and harshness) development. Among various experimental approaches, noise source identification using signal processing has attracted extensive attention because of that the signal can be easily acquired and the requirements for equipment is relatively low. In this paper, variational mode decomposition (VMD) combined with independent component analysis (ICA) is used for noise source identification of a turbo-charged gasoline engine. Existing algorithms have been proved to be effective to extract signal features but also have disadvantages. One of the key problems in presently used method is that the main components of the signal, i.e. the main source of the noise, are unknown in advance. Thus the parameters selection of signal processing algorithms, which has a significance influence on the identification result, has no uniform criterion.

Although excellent progress has been made recently in powertrain fault diagnosis based on vibration signals, most of them are based on the assumption that the fault features of the training and test data are drawn from the same probability distribution. Due to the limitation of the domain shift phenomenon, the performance of the current intelligent fault diagnosis methods is significantly reduced. Even many existing transfer learning methods have the problem of low generalization ability. Inspired by sparse representation theory, a novel cross-domain fault diagnosis method based on K-means singular value decomposition (K-SVD) and long short-term memory network (LSTM) is proposed in this study. First, K-SVD can convert source domain data into a sparse dictionary and sparse coefficient. The domain-invariant features are explored in the sparse dictionary, which contains redundant features. The sparse coefficients are input into the LSTM to obtain a primary classifier.

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels.