Fault diagnosis of rolling bearings based on parabolic acoustic mirror and support vector machine

In this paper, a new method for bearing acoustic detection is proposed based on the parabolic acoustic mirror, which uses the parabolic acoustic mirror instead of the traditional bearing end cap, places a directional microphone at the focal point of the parabolic acoustic mirror, and focuses the fault information signal emitted by the bearing at the directional microphone after the reflection of the parabolic acoustic mirror to achieve gain acquisition. At the same time, due to the inherent properties of the parabola, the non-parallel incident sound waves cannot converge at the focal point, so as to realize the directional acquisition of the bearing fault signal. Combined with the support vector machine, the acoustic features are extracted, and the high-dimensional features are reduced to three-dimensional features by using the KJADE algorithm, and they are input into the support vector machine as feature vectors for identification and classification of various bearings. Experiments show that the proposed method can accurately classify bearing faults.