On-line mechanical life prediction method for a conventional circuit breaker based on multi-parameter particle swarm optimization-support vector regression using vibration detection

For on-line mechanical condition monitoring of conventional circuit breakers, based on the analysis of vibration signal measurements and the generation principle in the circuit breaker breaking motion process, the time parameters for mechanism action with clear physical meaning are correlated with vibration events. Thus, an on-line mechanical life prediction method based on vibration detection and multi-parameter support vector regression (MP-SVR) optimized by particle swarm optimization is proposed. This method aims to use the action time parameter as the main monitoring parameter to reflect the degradation of mechanical conditions and the life failure criterion. First, the complete MPs of life degradation are obtained through the designed on-line measurement algorithm for the action time parameter and the assisted time-domain features of vibration signals in the corresponding time. Then, a quantitative life prediction model is constructed based on MP-SVR. Finally, the degraded MPs are used as input to the prediction model to predict the remaining mechanical life. Our experiment shows that this on-line model for life evaluation is definitely effective and practical in engineering applications.