Abstract
Due to enhanced safety, cost‐effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder‐ (SSAE‐) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE‐DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.
Highlights
Reliable fault diagnosis of industrial machinery is an essential task, as it contributes to the safety and reliability of the machinery, and decreases the associated maintenance and operational costs [1,2,3,4,5,6,7]
Vibration acceleration signals collected from complex industrial machines provide useful information about their health status, and vibration condition monitoring is considered a standard approach that allows for corroboration as a part of reliable fault diagnosis schemes [8,9,10,11,12]
A combination of the coefficients of the linear time-invariant autoregressive model and nearest neighbor classifier were utilized for fault diagnosis [24]. These networks can efficiently perform fault classification under constant shaft speeds. The efficiency of these fault diagnosis schemes decreases when tested with data for variable shaft speeds
Summary
Reliable fault diagnosis of industrial machinery is an essential task, as it contributes to the safety and reliability of the machinery, and decreases the associated maintenance and operational costs [1,2,3,4,5,6,7]. Ten statistical parameters reflecting the bearing health conditions were first calculated, and the calculated features were provided as an input to the ANN for fault classification [22]. A combination of the coefficients of the linear time-invariant autoregressive model and nearest neighbor classifier were utilized for fault diagnosis [24]. These networks can efficiently perform fault classification under constant shaft speeds. A mechanism is required to address the issue for the underlying network so that it can efficiently extract useful information from nonstationary shaft speed data, making it suitable for efficient fault diagnosis in variable shaft speed conditions
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