  Remaining Useful Life (RUL) estimation plays a critical role in Prognostics
and Health Management (PHM). Traditional machine health maintenance systems are
often costly, requiring sufficient prior expertise, and are difficult to fit
into highly complex and changing industrial scenarios. With the widespread
deployment of sensors on industrial equipment, building the Industrial Internet
of Things (IIoT) to interconnect these devices has become an inexorable trend
in the development of the digital factory. Using the device's real-time
operational data collected by IIoT to get the estimated RUL through the RUL
prediction algorithm, the PHM system can develop proactive maintenance measures
for the device, thus, reducing maintenance costs and decreasing failure times
during operation. This paper carries out research into the remaining useful
life prediction model for multi-sensor devices in the IIoT scenario. We
investigated the mainstream RUL prediction models and summarized the basic
steps of RUL prediction modeling in this scenario. On this basis, a data-driven
approach for RUL estimation is proposed in this paper. It employs a Multi-Head
Attention Mechanism to fuse the multi-dimensional time-series data output from
multiple sensors, in which the attention on features is used to capture the
interactions between features and attention on sequences is used to learn the
weights of time steps. Then, the Long Short-Term Memory Network is applied to
learn the features of time series. We evaluate the proposed model on two
benchmark datasets (C-MAPSS and PHM08), and the results demonstrate that it
outperforms the state-of-art models. Moreover, through the interpretability of
the multi-head attention mechanism, the proposed model can provide a
preliminary explanation of engine degradation. Therefore, this approach is
promising for predictive maintenance in IIoT scenarios.
