Abstract
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.
Highlights
The rolling bearing is an important part of rotating machinery
The results show that the computational cost and orthogonality index (OI) using the ECEEMDAN method are versions (Table 1)
This paper proposes a novel method for fault feature identification concerning rolling bearings
Summary
The rolling bearing is an important part of rotating machinery. Fault diagnosis is significant to ensure normal machinery operation [1,2,3]. Developing a signal processing method to identify the fault feature of rolling bearings is necessary. Empirical mode decomposition (EMD) [11] is an adaptive data-driven method to process non-linear and non-stationary signals. This technology has been applied in various fields, such as the fault diagnosis of rotational machinery [12,13], signal filtering [14,15], and biomedical signal processing [16]. The ensemble EMD (EEMD) [17] was developed by adding Gaussian white noise into the original signal to solve the problem of mode mixing.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
