Abstract
Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.
Highlights
Rolling bearings are widely employed in many machines
Compared with the above bearing fault diagnosis methods, this paper proposes a novel fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN), in which TQWT is used to eliminate the influence of noise on original bearing vibration signal, while CNN is adopted for fault diagnosis
As this paper mainly explores the feasibility of fault diagnosis using TQWT and CNN, instead of building up a testbed, this paper directly uses the data released by the Case Western Reserve University Bearing Data Center [27]
Summary
Rolling bearings are widely employed in many machines. Its operational condition directly influences the lifetime and operator safety of the machine. Inturi et al [3] proposed an integrated condition monitoring scheme for the bearing on a wind turbine gearbox by using the discrete wavelet transform (DWT), together with vibration, acoustic, and lubrication oil analysis techniques Their experimental results show that the presented integrated condition monitoring method has better classification accuracy than a single condition monitoring approach. Manjurul et al [5] presented a bearing fault diagnosis approach using bearing acoustic emission signals and a Bayesian inferencebased multi-class support vector machine, and the proposed method is able to improve the classification accuracy. Islam et al [17] proposed a fault diagnosis system using adaptive deep convolutional neural network and wavelet packet transform to automate and better generalize the fault feature extraction and diagnosis process.
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