Noise Source Identification of a Gasoline Engine Based on Parameters Optimized Variational Mode Decomposition and Robust Independent Component Analysis

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. To solve this problem, a parameter selection method using Kurtosis index is developed to optimize the decomposition level and the quadratic penalty of VMD. After the signal is decomposed into several relevant intrinsic mode functions (IMFs), ICA is employed to extract independent signal sources. In addition, continuous wavelet transform is used to analyze the time-frequency characteristics of the ICA results. The combined technique alleviates the problem of parameters selection in VMD and overcomes the problem that the number of sensors must be larger than or equal to the number of separated components in ICA. The advantages of the proposed method are confirmed by experimental study and simulation results. The proposed method can separate the main noise sources of the gasoline engine accurately.