Dynamic feature information extraction using the special empirical mode decomposition entropy value and index energy. (15th February 2020)

• Record Type: Journal Article
• Authors: Lu, Shibao; Ye, Weiwei; Xue, Yangang; Tang, Yao; Guo, Min
• Is Part Of: Energy. Volume 193(2020)
• Publication Date: 2020-02-15
• Subjects: Draft tube (tail pipes) -- Feature extraction -- Empirical mode decomposition (EMD) -- Index energy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116610
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Based on signal feature extraction, a combination of the empirical mode decomposition (EMD) and index energy methods is adopted in this paper to extract the Draft Tube's dynamic feature information for the water turbine. Based on the eigenmode component functions derived from EMD of the signal, the index energy is calculated in this paper. Additionally, two model parameters based on indicators of energy are established, and are used as eigenvectors for the fault pattern identification. Taking an example of the pressure fluctuation signal in the water turbine's draft tube, this method is used to extract the dynamic feature information of the tail pipe, and perform the application testing. The results show that the method is of high accuracy and has not only good quality in extracting eigenvectors but also relatively good accuracy in extracting the dynamic features of complex and special water turbines. This extraction method is effective for fault pattern recognition. Highlights: Based on the eigenmode component functions derived from EMD of the signal. Two model parameters based on indicators of energy are established. The characteristic vector model parameters are established. The characteristic vector model parameters are concluded from the index energy.