DeepHealth: A Self-Attention Based Method for Instant Intelligent Predictive Maintenance in Industrial Internet of Things

With the rapid development of artificial intelligence and industrial Internet of things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this paper, a self-attention based framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to carry out instant decision-making. On this basis, we conduct destructive experiments based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations.