EMD, however, has the problem of mode mixing, which is defined as either a single IMF consisting of components of widely disparate scales, or a component of a similar scale residing in different IMFs [10,11].Consequently, ensemble empirical mode decomposition (EEMD), an improved version of EMD, was presented to solve the problem of mode mixing in EMD [10]. EEMD is a noise-assisted data analysis method. By adding finite white noise to the signal to be investigated, EEMD is supposed to eliminate the mode mixing problem. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms, such as the sifting number, the amplitude of the added noise, etc. In most of the current studies on EEMD, these parameters were set as constant values.

However, according to our investigation, different frequency components contained in signals have different sensitivities to these parameters [12]. As a result, the problem of mode mixing is not solved as expected and the performance of EEMD needs to be improved further.Based on the investigation of the filtering behavior of EMD/EEMD and the relation between the signal frequency components and the amplitude of the added noise, we present a new adaptive ensemble empirical mode decomposition method in this paper. In this method, the sifting number is adaptively selected and the amplitude of the added noise varies with the signal frequency components during the decomposition process. By adopting both the adaptive sifting number and the adaptive added-noise amplitude, it is expected that the proposed EEMD method is able to improve the performance of the original EEMD in feature extraction and fault diagnosis.

The remainder of this paper is organized as follows. Section 2 briefly introduces the algorithm of EEMD. Section 3 is dedicated to a description of the proposed adaptive EEMD and generates a simulation to illustrate the method. In Section 4, experiments on a planetary gearbox test rig are conducted and vibration signals are collected to demonstrate the effectiveness of the proposed method in diagnosing gear faults. In Section 5, the proposed method is applied to diagnose an early fault of a heavy oil catalytic cracking machine set. The simulation, the experimental and the application results show that the adaptive EEMD produces the improved results compared with the original EEMD.

Some concluding remarks are drawn in Section 6.2.?Ensemble Empirical Mode DecompositionEEMD was developed GSK-3 by Wu and Huang to solve the problem of mode mixing of EMD [10]. It is a noise-assisted data analysis method, which defines the true IMF components as the mean of an ensemble of trials. Each trial contains the decomposition results of the signal plus a white noise of finite amplitude decomposed by EMD [10,11].