A Load State Identification Method of Ball Milling Based on Particle Swarm Optimization and Stacked Convolutional Autoencoder

In order to solve the problem that the load of cement ball mill is difficult to judge, based on particle swarm optimization(PSO) algorithm, a stacked convolutional autoencoder(SCAE) is proposed to judge the load. Firstly, an infinite impulse response(IIR) digital band-pass filter is designed to filter the vibration signals from the mill's wall, extracting vibration signals within an appropriate frequency range. Subsequently, two convolutional autoencoders are established to capture deep features from the vibration signals. Finally, the encoders from these two convolutional autoencoders are stacked together, followed by the addition of a classification layer to form the stacked convolutional autoencoder. This model is then utilized to classify the load state. PSO is employed to optimize both the filtering frequency band and the hyperparameters of the neural network. The results demonstrate that the stacked convolutional autoencoder achieves high accuracy, with an accuracy rate exceeding 97.53% on the test set. This provides effective assistance in judging the load state of cement ball mills and improving cement grinding quality.