Convolutional Autoencoder-Based Multichannel Signal Monitoring Method

Unexpected breakdown of the equipment significantly reduces productivity. The development of monitoring system that can detect abnormal conditions early is essential. In this study, we propose the multivariate monitoring method based on a convolutional autoencoder algorithm that can effectively reconstruct sensor data. The proposed monitoring method identifies the equipment status and detects critical variables that cause the alarms. We evaluate the performance of the proposed method with actual sensor data collected from construction equipment.