Abstract
This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.
Highlights
Gearboxes play an important role in numerous industrial machines, vehicles, and wind turbines [1,2,3]
This section principally validates the proposed fault identification framework constructed in Section 4 for an multi-degree tooth faults (MDTFs) gearbox under inconsistent rotational speeds based on the data collected from a real-world testing platform
The effectiveness of this model is entirely evaluated based on the following operations: localized adaptive denoising technique (LADT), visual enriching feature configuration (WVI’s), and fault identification based on deep convolutional neural network architecture (DCNA)
Summary
Gearboxes play an important role in numerous industrial machines, vehicles, and wind turbines [1,2,3]. A fault in the gearbox can result in catastrophic failures, economic losses, and danger to the operating staff. For this reason, early fault detection of the gearbox is of primary importance. The conditionbased monitoring approach suggests maintenance action based on the data collected from the gearbox. This strategy allows the gearbox to function for a long time with minimal maintenance costs [5,6]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have