Abstract
Discriminative feature extraction is a challenge for data-driven fault diagnosis. Although deep learning algorithms can automatically learn a good set of features without manual intervention, the lack of domain knowledge greatly limits the performance improvement, especially for nonstationary and nonlinear signals. This paper develops a multiscale information fusion-based stacked sparse autoencoder fault diagnosis method. The autoencoder takes advantage of the multiscale normalized frequency spectrum information obtained by dual-tree complex wavelet transform as input. Accordingly, the multiscale normalized features guarantee the translational invariance for signal characteristics, and the stacked sparse autoencoder benefits the unsupervised feature learning and ensures accurate and stable diagnosis performance. The developed method is performed on motor bearing vibration signals and worm gearbox vibration signals, respectively. The results confirm that the developed method can accommodate changing working conditions, be free of manual feature extraction, and perform better than the existing intelligent diagnosis methods.
Highlights
Rotating machinery plays an important role in modern industries and has become more automatic, precise, and efficient [1]
For the dual-tree complex wavelet decomposition, the (5, 7)-tap symmetry biorthogonal filters were used at the first level, and at the rest of the levels, the 14-tap linear phase filters produced by Q-shift solution [19] were used. e neural network in the developed DCWT-SSAE has five layers, in which the node number of the input layer is determined by the output
In order to get the ideal features, the original signal must be divided into segments alternately, and the averaging process is essential to eliminate the bad effects of the difference of each segment and random features caused by noise, while the developed DCWT-SSAE method uses dual-tree complex wavelet transforms to overcome the time-varying in the time domain, so the processes of dividing signal and averaging local features can be omitted, and the stacked sparse autoencoder benefits the unsupervised feature learning and ensures accurate and stable diagnosis performance
Summary
Rotating machinery plays an important role in modern industries and has become more automatic, precise, and efficient [1]. Erefore, an accurate and robust fault diagnosis tool for rotating machinery needs to be developed [2]. Fault diagnosis methods can be classified into either model-based methods or data-driven methods [3]. Model-based approaches need precise physical models of the system, which is a challenging task in most cases due to the system structure complexity [4], whereas the data-driven methods always combine artificial intelligence with signal processing method, and these methods identify different faults by a series of steps, including data collection, feature extraction, and classifier training [3]. Data-driven methods can be used in complex systems and do need not to build an accurate mechanical failure physical model. In the traditional intelligent diagnosis method, the quality of the extracted features directly affects the classifier training performance [6]. Since mechanical systems often work in complex and variable environments, including load
Sign-in/Register to view highlights & summary 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.
