Intelligent identification of key characteristic points and its application in electromagnet operation condition monitoring of operating mechanism

The electromagnet’s state of operation plays a crucial role in maintaining the operational mechanism of circuit breakers, as it acts as a trigger for the opening and closing action of the mechanism. However, due to the interference of complex electrical environments and the limitation of sensing conditions, there are significant deficiencies in the robust condition monitoring of electromagnets. A hybrid two-stage method was proposed to diagnose the running state of the electromagnet of the operating mechanism. By intelligently identifying the key characteristic points of the electromagnet current signal, the proposed method indirectly realized the intelligent diagnosis of the state of the electromagnet. In the first identification stage, an intelligent U-Net neural network suitable for the one-dimensional signal was proposed to realize the adaptive identification of crucial feature points via the obtained current signal of electromagnets. In the second condition monitoring stage, based on the position and the current value of the key feature points, the operating state of the electromagnet could be identified specifically. The experimental findings demonstrated that the suggested strategy was capable of successfully identifying the key characteristic points, with a near-perfect recognition success rate. The proposed method realized the adaptive identification of various electromagnet faults with only a few fault samples, which provided a guarantee for robust state identification of electromagnets and had the advantage of high interference resistance.