Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions

摘要

Deep learning is characterized by strong self-learning and fault classification ability without manually feature extraction stage of traditional algorithms. Deep belief network (DBN) is one of the most classic models of deep learning. However, traditional DBN is mainly restricted to learn automatically single scale features from raw vibration signal while identify the fault type, which implies some important information inherent in other scales of vibration data are neglected, thus causing easily unsatisfactory diagnosis result. To alleviate the problem, this paper presents a novel architecture named multiscale cascading deep belief network (MCDBN) for automatic fault identification of rotating machinery, which is aimed at learning the broader feature representation and improving the recognition precision. Firstly, a sliding window with data overlap is adopted to split the collected raw vibration signal to a group of equal-sized sub-signal, and then the improved multiscale coarse-grained procedure of each sub-signal is conducted to obtain the coarse-grained time series at different scales. Meanwhile, Fourier spectrum at different scales is calculated to capture multiscale characteristics. Finally, multiple DBN architecture with three hidden layers are designed to learn high-level feature representation directly from multiscale characteristics in a parallel manner and accomplish fault identification automatically through cascading way and softmax classifier without artificial expertise. Results of two experimental cases with respect to mechanical fault identification under different working conditions have well indicated that the proposed method is provided with preferable diagnostic performance compared with standard DBN and traditional multiscale feature extractors. (c) 2020 Elsevier B.V. All rights reserved.