An improved ensemble empirical mode decomposition method and its application to pressure pulsation analysis of hydroelectric generator unit. (December 2014)

• Record Type: Journal Article
• Authors: Xue, Xiaoming; Zhou, Jianzhong; Zhang, Yongchuan; Zhang, Weibo; Zhu, Wenlong
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 6(2014:Dec.)
• Page Start: 543
• Page End: 557
• Publication Date: 2014-12
• Subjects: Ensemble empirical mode decomposition -- complementary ensemble empirical mode decomposition -- over-sampling ensemble empirical mode decomposition -- feature extraction -- hydroelectric generator unit; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X14538246
• Languages: English
• ISSNs: 1748-006X

Abstract

The noise-assisted method of ensemble empirical mode decomposition represents a significant improvement over the original empirical mode decomposition for eliminating the mode mixing problem. However, the ensemble empirical mode decomposition method will generate some additional problems, including the contamination of the residue noise in the signal reconstruction and the high computational cost. In this work, an improved ensemble empirical mode decomposition method, combining the complementary ensemble empirical mode decomposition and a time-saving ensemble empirical mode decomposition method by over-sampling the investigated signal, is proposed to solve these problems. By using the proposed method, the residue of the added white noise in the signal reconstruction can be eliminated completely by adding white noises in pairs with positive and negative signs to the targeted signal, and the computational cost can be saved drastically by processing the original signal with the cubic spline interpolation technique. Two simulation signals have been used to demonstrate the effectiveness of the proposed method in this article. The analysis results indicate that this method has good performance in eliminating the residue noise and reducing the costing time, which also provides more accurate decomposition results than the original ensemble empirical mode decomposition. Finally, the application to the feature extraction of pressure pulsation signal of hydroelectric generator unit shows that the proposed method has strong practicability.