Abstract
Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.
Highlights
Rolling bearings are widely used in modern production equipment and nearly 50 percent mechanical faults are occurred on bearing and related components [1]
Compared with the above-mentioned bearing fault diagnosis methods, this paper proposes a novel bearing fault diagnosis method based on wireless sensor networks (WSNs) and convolutional neural network (CNN), in which WSNs are used to measure and transmit the bearing vibration signal, while CNN algorithm on a laptop is used for bearing fault diagnosis
The end nodes and coordinator are used to collect and transmit bearing vibration signal to the centralized computer, while the centralized computer is employed to achieve bearing fault diagnosis based on CNN
Summary
Rolling bearings are widely used in modern production equipment and nearly 50 percent mechanical faults are occurred on bearing and related components [1]. Feng et al [16] achieve signal acquisition, data processing using fast Fourier transform and Hilbert transform, and feature extraction using envelope spectrum analysis on the end node of WSNs, and only transmit the fault characteristics to the coordinator node and the PC Experiments show that this method can reduce data transmission by 95% compared with direct transmission of raw data. Sadoughi et al [31] divided feature extraction and CNN fault diagnosis into blocks, first selecting specific data features, and taking these features as CNN input This method can effectively solve the problem of simultaneous state monitoring and fault diagnosis of multiple rolling bearings.
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