In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...

Stock prices as time series are, often, non-linear and non-stationary. This paper presents an ensemble forecasting model that integrates Empirical Mode Decomposition (EMD) and its variation Ensemble Empirical Mode Decomposition (EEMD) with Artificial Neural Network (ANN) for short-term forecasts of stock index. In first stage, the data is decomposed into a smaller set of Intrinsic Mode Function...

Gearbox is an important mechanical device to transmit power. In order to ensure the normal operation of gearbox under the condition of top load, high efficiency and high precision, it’s necessary to extract fault feature information using signal processing method and to further analyze and research gearbox fault. In the paper an improved de-noising method based on de-noising of singular value d...

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decom...