In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted great concern. However, because data feature distributions present differences in applications with varying working conditions, learning models cannot provide satisfactory performance of prediction such scenarios. To address this problem, paper proposes a domain adversarial-based ro...

Modal parameters of power transformer winding are closely related to transformer manufacturing and detection technology of winding deformation based on vibration analysis method. Aimed at identifying the modal parameters of transformer winding accurately, a modal experiment is designed and made on a real 10 kV power transformer. An improved Empirical Mode Decomposition (EMD) algorithm is propos...

Understanding spatial and temporal changes of seasonal sea level cycles is important because direct influence on coastal systems. The annual cycle substantially larger than semi-annual in most parts the ocean. Ensemble empirical mode decomposition (EEMD) method has been widely used to study tidal component, long-term rise, decadal variation. In this work, EEMD analyze observed monthly anomalies...

The concepts of intrinsic mode functions and mono-components are investigated in relation to the empirical mode decomposition. Mono-components are defined to be the functions for which non-negative analytic instantaneous frequency is well defined. We show that a great variety of functions are mono-components based on which adaptive decomposition of signals are theoretically possible. We justify...