Description: Predictive maintenance (PdM) of devices is a popular use case for ML in IIoT. It is frequently used to detect system anomalies, speeding up diagnosis and root
Predictive maintenance (PdM) of devices is a popular use case for ML in IIoT. It is frequently used to detect system anomalies, speeding up diagnosis and root cause analysis. By correlating and analyzing changes in the pattern, the algorithms will be able to predict device failure. Other critical metrics, such as device “remaining useful life” (RUL), can be reported by PdM.
All IoT applications are built on time-series data. Knowing when something could go wrong is critical to avoiding disaster. Without the need for custom algorithms, our machine learning uncovers device event data to reveal missing data points, out-of-bounds values, and other outliers before anomalous behavior affects operations.
The challenge now is that security professionals do not understand how priorities differ between IT and OT and require concrete best practices on how to maximize risk reduction in OT networks while working within very short timelines. Attempts to use traditional cybersecurity solutions will cause many OT devices to crash. The disconnect between the IT and OT teams causes tension and conflict.