In this chapter, we have looked at how to develop a DL solution for predictive maintenance using IoT and the Turbofan Engine Degradation Simulation dataset. We started by discussing the exploratory analysis of the dataset before we modeled the predictive maintenance using one of the most popular tree-based ensemble techniques called RF, which uses features from the turbine engines as it is. Then, we saw how to improve the predictive accuracy using an LSTM network. The LSTM network indeed helps to reduce network errors. Nevertheless, we saw how to add a Gaussian noise layer to achieve generalization in the LSTM network, along with dropout.
Understanding the potential of DL techniques in all layers of IoT (including the sensors/sensing, gateway, and cloud layer) is important. Consequently, developing scalable and efficient solutions for IoT-enabled healthcare devices is...