In this chapter, we have explored the most important classes of analytics in the I-IoT from a theoretical point of view. We have looked at the most important use cases, including CBM, diagnostic analytics, prognostics, and predictive analytics. We also discussed the relationship of the analytics with the data in terms of model accuracy and data processing. Finally, we implemented a diagnostic algorithm, the anomaly detection exercise, and a predictive analytic model. Anomaly detection and production prediction are two of the most common I-IoT algorithms.
In the next chapter, we will focus on the kernel of the analytics—the digital twin.
