Online remaining life prediction method for electronic devices under field operating conditions

Abstract

This application discloses an online remaining useful life prediction method, system, storage medium, and electronic device for electronic device under filed operating conditions. A Gamma state-space model is constructed, and initial parameter estimates of the model are obtained using the Expectation-Maximization algorithm. Based on the initial parameter estimates and real-time monitoring data collected from field-operated devices, the true health state of the devices is dynamically updated using a kernel-smoothed particle filter. The EM algorithm is then used again to estimate updated parameters of the Gamma state-space model, yielding the final model. The remaining useful life is subsequently calculated. This application first extracts degradation information and establishes an initial degradation model based on accelerated degradation testing data, and then combines it with field operation data to dynamically update the degradation information and model parameters using the particle filter with kernel smoothing, thereby enabling real-time RUL prediction under filed operating conditions.

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