In order to solve the problem of low accuracy in monitoring the wear state of mechanical equipment

a mathematical model of monitoring the wear state based on the recognition of abrasive particle features was proposed based on different wear mechanisms with different shapes and textures. By identifying ball wear particles and cutting wear particles through shape feature

the shape and the texture features were combined to recognize fatigue wear particles and severe sliding wear particles. The feature vector of mechanical wear state monitoring was established based on the extracted feature parameters. Through the radical basis function neural network model by quantum particle swarm optimization (QPSO)

the recognition and monitoring of mechanical wear state were realized. The experimental results show that the QPSO-RBF neural network model is simple in structure and 5% higher in recognition accuracy than the traditional PSO-RBF neural network model. It can be used for common mechanical wear condition monitoring