Design of Fault Monitoring Algorithm for Electrical Automation Control Equipment Based on Multi-Sensor

A single sensor can only monitor a single state or environmental parameter. The operation of electrical automation control equipment conforms to the barrel effect, that is, a certain factor exceeds the maximum operating limit that electrical automation control equipment can bear, and the fault will inevitably occur. The basic purpose of information fusion is to make full use of multiple sensor resources. Based on multi-sensor data fusion technology, this paper introduces data fusion technology into the field of fault monitoring of electrical automation control equipment. An end-to-end adaptive fault monitoring algorithm for electrical automation control equipment is proposed based on multi-head attention mechanism and CNN (Convolutional Neural Network). This algorithm has stronger robustness and, combined with CNN, improves the accuracy of network fault classification. The research results show that when the signal-to-noise ratio of the noise signal is 7 dB, the accuracy of multi-attention CNN prediction has reached 97% and has remained at 100% since then. The experimental results show that the multi-attention CNN algorithm has better noise adaptability and stronger robustness in noise interference environment compared with other mainstream algorithms. The fault monitoring algorithm of electrical automation control equipment proposed in this paper can help share the work pressure of staff.