Diagnostic analytics are the most common analytics in the I-IoT. These analytics use advanced modelling techniques to analyze failure modes and to extract the root cause of an issue. Diagnostic analytics try to reply to the question Why did it happen?

The three steps of diagnostic analytics are as follows:

• Detecting anomalies
• Discovering anomalies
• Determining the root cause of anomalies

Normally, the last two steps of diagnostic analytics are done by human investigation. Diagnostic analytics can use feature extraction or anomaly reasoners to provide indicators about why an anomaly happened. Anomaly detection is, on the contrary, an automatic step performed by (more or less) sophisticated analytics.

After anomaly detection, we need to discover the failure mode and identify the cause of the issue. Normally, these activities require human knowledge of failure modes and effect analysis or a large dataset of past failures.  For instance, we can implement a set of rules (which can be deterministic, fuzzy, Bayesian, or machine learning-based), codifying the cause and effect of the fault.