Intelligent fault warning method of rotating machinery with intraclass and interclass infographic embedding

Abstract

Abstract Rotating machinery is widely used in industrial production facilities, and once a failure occurs, it can be catastrophic. Alerting to potential defects in time to prevent further equipment degradation is a challenging task. In this paper, a novel two-stage fault warning framework is proposed for early fault warning of rotating machinery. Specifically, a new method based on intra-class and inter-class neighborhood information graph embedding orthogonal discriminant projection is firstly adopted in this framework to extract the global distribution feature information and local geometric structure information of the data so that the homogeneous distance is compressed and the heterogeneous distance is distanced. Secondly, the minimum quantization error between the sample to be measured and the optimal winning neuron weight vector is calculated by self-organizing map to characterize the health state change, and combined with the Beta distribution self-learning technique to establish the fault warning threshold to circumvent the defects brought by the traditional fixation and it. Finally, the effectiveness of the proposed method is verified in the bearing and planetary gearbox test cases, and exciting conclusions are obtained under different working conditions in the gearbox case.