Preventing Unplanned Downtime
Preventing Unplanned Downtime Due to Equipment Failure
When a PLC failed during custody transfer at a pipeline offload/onload facility, the terminal backed up, resulting in $1.9 million in lost revenue and downtime. Operators had noticed a slight change in operational values months before, but the degradation was so gradual that it was thought to be normal.
The PLC was experiencing “ghost drift”, where it slowly slipped out of scope over a long period time, hiding an impending failure that led to costly downtime.
An important part of downtime prevention involves determining normal values for asset behavior, and recognizing when they are moving towards a critical state.
Using Anomaly Detection to Identify Malfunctioning Devices Before They Fail
The Nozomi Networks solution tackles this common critical infrastructure challenge head on with automated network monitoring and anomaly detection. Our products learn normal asset and process behavior and alert you to deviations.
Upon deployment, the solution uses machine learning and artificial intelligence to observe network traffic and create asset and process baselines. It models behavior and correlates multiple types of data, including information about similar devices within the ecosystem, to determine what normal activity looks like.
In the second protection phase, the solution automatically detects when a specific asset or process is deviating from its baseline, and moving towards a state that could disrupt operations. It then alerts you that it’s time to investigate. This significantly reduces troubleshooting efforts and enables you to take action before equipment failure incident occurs.