Intelligent fault diagnosis of coal mine roadheaders based on particle swarm optimization of BP neural networks
Ailiang Kang
IntroductionCoal mine roadheaders operate under complex conditions characterized by prolonged exposure to high vibration and dust levels, resulting in a high failure rate. Traditional fault diagnosis methods suffer from issues such as low diagnostic accuracy and poor real-time performance. This study suggests an intelligent diagnosis model for coal mine roadheader faults based on the artificial fish swarm algorithm, particle swarm optimization, and a backpropagation neural network in an attempt