LAUSR

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.