Abstract
Increasingly energy and environmental crises put forward higher request on diesel engine. It promotes the development of diesel engine, while the complexity of structure is much higher, which leads to higher probability of faults. In order to recognize the states of engine in harsh environments effectively, variational mode decomposition (VMD) and expectation maximization (EM) are introduced into this paper to analyze multi-channel vibration signals. To select the decomposition level of VMD adaptively, a novel power spectrum segmentation based on scale-space representation is proposed for the optimization of VMD and results show this approach can discriminate different frequency components in high noise circumstance accurately and efficiently. To improve the adaptability and accuracy of EM, a feature selection approach based on genetic algorithm (GA) is introduced to preprocess original data and a cross validation method is used for selecting cluster number adaptively. Combined with these approaches, a diesel engine state recognition scheme based on multi-channel vibration signals using optimized VMD and EM is proposed. Compared with existing method, this scheme shows great advantages in accuracy and efficiency, and could be applied in actual engineering.
Highlights
As a frequently used power source, diesel engines are being widely employed in industry, agriculture and some special industries because of the advantages of low fuel consumption and large output torque [1]
With increasingly energy and environmental crises, a large number of laws and regulations for diesel engine have been enacted in many countries, which greatly promotes the development of new technologies such as turbo charging, high-pressure common-rail fuel system, electronically controlled fuel injection system, variable valve timing (VVT) and exhaust gas recirculation (EGR) and so on [2]
GENETIC ALGORITHM BASED FEATURES SELECTION genetic algorithm (GA) is a method to solve optimization inspired by biological processes of mutation, natural selection and genetic crossover, which is a powerful feature selection tool, especially for feature set with large dimensions
Summary
As a frequently used power source, diesel engines are being widely employed in industry, agriculture and some special industries because of the advantages of low fuel consumption and large output torque [1]. X. Bi et al.: Engine Working State Recognition Based on Optimized VMD and EM Algorithm and vibration, is being a promising method, in which the vibration signal is a research focus because of easy measurement, low cost and strong robustness. A number of signal processing algorithms have been researched to overcome this problem such as wavelet transform and empirical mode decomposition (EMD). A lot of successes have been achieved in the diesel engine condition recognition and fault diagnosis based on time-frequency analysis, feature extraction and pattern recognition of single channel vibration signal, there are still some problems need solving. B. GENETIC ALGORITHM BASED FEATURES SELECTION GA is a method to solve optimization inspired by biological processes of mutation, natural selection and genetic crossover, which is a powerful feature selection tool, especially for feature set with large dimensions. 3) MUTATION Select one chromosome that has a high fitness value from the current population, alter some bits of it and copy it into the new population [35]
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