A Masked One-Dimensional Convolutional Autoencoder for Bearing Fault Diagnosis Based on Digital Twin Enabled Industrial Internet of Things

Bearings are the core component of mechanical equipment. The health status of bearings is the key to the stable operation of the system. Bearing fault diagnosis model can discover damaged bearings in time, which has a large economic value for enterprises. The previous bearings fault diagnosis model suffers from problems such as small fault data and unrepresentative features, which leads to poor model generalization performance. Therefore, in this work, we propose a masked one-dimensional convolutional autoencoder (MOCAE) for bearing fault diagnosis based on digital twin enabled industrial internet of things (IIoT). The model monitors the bearing data using a set of IIoT platforms. The digital twin technology is used to build a digital twin model of the bearing device, and the parameters of the digital twin model are trained by the fault data obtained from the IIoT platform. The trained digital twin model can then simulate whether the bearing is faulty. In this digital twin model, MOCAE model is proposed for diagnosing faulty bearing signals. The MOCAE model first extracts the features from the time series signal of the bearing using a one-dimensional convolutional autoencoder, which can enhance the reconstruction ability of hidden features to make them more representative. Next, the MOCAE model automatically extracts the feature information contained in the time series signal data by self-training in order to reduce the dependence on the labeled data. The comprehensive experimental results on real bearing datasets show the superiority of the MOCAE model.