Abstract
Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.
Highlights
Bearings are the core component of rotating machinery such as wind turbines, aircraft and automobiles
The low-frequency information of the condition signals can be obtained by using the wavelet packet transform (WPT), which is useful for bearing fault diagnosis
This paper presented a two-step fault diagnosis method based on WPT and convolutional neural network (CNN)
Summary
Bearings are the core component of rotating machinery such as wind turbines, aircraft and automobiles. For the model selection and construction, machine learning models, such as hidden Markov models [13], Bayesian networks [14], neural networks, and support vector machines [15], are commonly employed as the classification tool in the flied of fault diagnosis Those methods often need to consider the tradeoff between fault diagnosis training cost, training efficiency, and applicability performance. Development of fault diagnosis, deep learning methods have emerged as an effective tool to achieve feature extraction and fault recognition due to their easy trainability and accurate classification performance. CNN-based fault diagnosis approaches have been presented and achieved success, but they usually rely on the designed features or manually generated input without considering the appropriate input selection and noise information weakening.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
