Vibrating screen fault detection based on video frame prediction

As a key technical equipment for improving coal quality, efficiency and promoting clean and efficient utilization, it is important for vibrating screens to detect their faults quickly and accurately. Traditional vibrating screen fault detection methods usually rely on the vibration signals collected by sensors, and identify the faults through spectral analysis, time domain feature extraction, etc. However, these methods face the problems of inaccurate detection, insufficient real-time performance and poor adaptability to environmental changes under complex working conditions. To address this limitation, this paper proposes a video frame prediction model, DST-UNet, to detect the operation of vibrating screens through video. The model combines convolutional units with different kernel sizes to simultaneously extract local detail information and global structural information in the operation of vibrating screens; uses ECA as a temporal attention mechanism to improve the model's understanding and sensitivity to temporal sequences and employs convolutional units to generate spatio-temporal weights to dynamically fuse spatial and temporal features. In addition, an evaluation metric MTR is proposed to assess the model performance. The experimental results show that, compared with models such as SimVP, 3D convolution and PredRNN, the method improves the sensitivity, accuracy and processing speed of fault detection while reducing the model complexity and can realize efficient detection with limited data sets, which helps to identify equipment faults quickly and reduce the risk of damage.