The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.