Every device has an end of life or will require maintenance from time to time. Predictive maintenance is one of the most commonly used machine learning algorithms in IoT. The next chapter will cover predictive maintenance in depth, looking at sequential data and how that data changes with seasonality. This recipe will look at predictive maintenance from the simpler perspective of classification.
In this recipe, we are going to use the NASA Turbofan engine degradation simulation dataset. We are going to be looking at having three classifications. Green means the engine does not need maintenance; yellow, the engine needs maintenance within the next 14 maintenance cycles; or red, the engine needs maintenance within the next cycle. For an algorithm, we are going to use extreme gradient boosting (XGBoost). XGBoost has become popular in recent years because it tends to win more Kaggle competitions than other algorithms.
