Abstract
Rolling bearing is a very important mechanical part and extensively applied in mechanical equipment. It provides stable support and rotation accuracy for the rotor in rotating machines. The health of rolling bearing is essential to guarantee the reliable operation of the entire rotating machines. In this paper, a vision-based data-driven method of fault diagnosis for rolling bearings in rotating machinery is proposed. Firstly, the displacement information under different fault states is acquired from the obtained vibration images, and its frequency-domain feature can be obtained after fast Fourier transform. Secondly, a linear model is established to obtain a mapping function that can reflect the correspondence between vibration characteristics and fault types. Thirdly, fault diagnosis can be realized based on the vibration data under different fault states and acquired mapping function. Finally, the experimental results prove that the method presented in this article can achieve rolling bearing fault diagnosis with relatively high correct rate.
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Liu, B., Peng, C., Zhu, M. (2022). A Vision-Based Data-Driven Method of Fault Diagnosis for Rolling Bearings in Rotating Machinery. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_197
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DOI: https://doi.org/10.1007/978-981-15-8155-7_197
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