Abstract
To process the non-stationary vibration signals and improve accuracy of bearing fault diagnosis, this paper presents a novel intelligent fault diagnosis method based on the adaptive fast ensemble empirical mode decomposition (AFEEMD) and one-dimensional convolutional neural networks (1D CNNs). First, the AFEEMD algorithm is utilized to decompose the raw signals into intrinsic mode functions (IMFs). Then, the time and frequency statistic features of the first several IMFs are analyzed to form feature vector, which are used as the input of 1D CNNs to identify the bearing fault. The performance of the proposed method is validated using the dataset from the Case Western Reserve University (CWRU). Compared with the traditional back propagation neural network (BPNN), the results show that the proposed AFEEMD-1D CNNs method not only can obtain higher accuracy and achieve more reliable performance, but also can improve the generalization performance. Due to the end-to-end feature learning capacity, it can be extended to other machinery for fault diagnosis.
Highlights
The bearings are the crucial component of the rotating machinery
Inspired by the advantage of adaptive fast ensemble empirical mode decomposition (AFEEMD) and 1D convolutional neural networks (CNNs), we present an intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs in this paper
This paper presents an intelligent end-to-end data driven method for bearing fault diagnosis based on AFEEMD and 1D CNNs
Summary
The bearings are the crucial component of the rotating machinery. Their operation condition directly affects the accuracy and stability of the rotating machinery. The statistic features in time domain and frequency domain of the first several IMFs are used to form feature vector which represents the most of fault information [4]. It will be used as the input of the 1D CNNs. The most advantage is that the input dimension and complexity of the 1D CNNs model can be reduced. Sun et al [6] proposed a CNN-based fault diagnosis method for gears and obtained a higher accuracy, where the dual-tree complex wavelet transform (DTCWT) was used to extract the signals’ features. Numerous traditional CNNs methods for fault diagnosis use two-dimensional images as input, ignoring the vibration signal with one-dimensional characteristics. Inspired by the advantage of AFEEMD and 1D CNNs, we present an intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs in this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
