The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of\nmaintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics\nof rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features\nreextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is\nestablished using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a\nquantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input,\nthe relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic\nremaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of\nrolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability\ndistribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and\nengineering application value.
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