In Internet of Things (IoT) devices, streaming data is generated for one event at a time. DL-based approaches can examine this data in order to diagnose the problem across the fleet in real time, and the future health of individual units can be predicted in order to enable on-demand maintenance. This strategy is known as predictive (or condition-based) maintenance. This approach is now emerging as one of the most promising and lucrative industrial applications of the IoT.
Considering these motivations, in this chapter, we will look at how to develop a DL solution for predictive maintenance for IoT using the Turbofan Engine Degradation Simulation dataset. The idea behind predictive maintenance is to determine whether the failure patterns of various types can be predictable. Furthermore, we will discuss how to collect data from IoT-enabled devices...
