Research on Industrial Equipment Fault Warning Based on AO-SOM Neural Network

With the development of information technology, the intelligence level of industrial equipment is also constantly improving, and the requirements for equipment status monitoring have become more stringent. The traditional fault warning methods based on expert databases and single monitoring measurements are no longer applicable to current equipment, so a fault warning method based on AO-SOM neural network is proposed. Firstly, the neuron vector of the SOM (Self Organizing Map) neural network was optimized using the AO (Aquila Optimizer) algorithm, and an early warning neural network was established using the optimized neurons; Secondly, by combining early warning neural networks with adaptive thresholds based on confidence intervals, a warning model based on adaptive thresholds is constructed; Finally, the feasibility of the model is verified by the actual running data validation of a CNC grinder in a factory in Shandong Province. The experimental results show that the AO-SOM neural network has more accurate warning effect compared to traditional SOM neural networks and SOM neural networks based on PSO algorithm. At the same time, the early warning model based on adaptive thresholds compensates for the shortcomings of fixed thresholds in practical applications, and more accurately predicts the occurrence of equipment failures, which is of great significance for the maintenance and upkeep of industrial equipment.