Method for Recognizing Mechanical Status of Container Crane Motor Based on SOM Neural Network

Abstract

The recognition of mechanical state of container crane motor is a complex nonlinear mode classification problem. In this paper, a deep learning method based on self-organizing feature map (SOM) neural network was developed for the cluster analysis of motor vibration signals to characterize the mechanical state of the lifting motor. Through Python software simulation, the vibration intensity signal of the lifting motor can be divided into five different categories, and the interval range of each type of vibration intensity signal is statistically obtained, which corresponds to the five mechanical states of the motor. The SOM neural network can realize the effective and rapid self-adaptive classification of the motor vibration signal, realize the recognition of the mechanical status of the crane motor, and provide a certain basis for the maintenance of the motor.