Which data source is most critical for implementing predictive maintenance in SCADA systems?
Think about what helps predict future failures based on past behavior.
Historical failure logs combined with sensor data trends provide the necessary information to detect patterns and predict equipment failures before they happen.
What is the output of this command that filters temperature sensor readings above 80°C from a SCADA log file?
grep 'temperature' scada.log | awk '$3 > 80 {print $0}'
Look at how awk filters based on the third column value.
The command first selects lines containing 'temperature' then awk filters those where the third field is greater than 80, printing only those lines.
What is the correct order of steps to implement a predictive maintenance workflow in a SCADA system?
Think about setting up hardware before collecting data.
First deploy sensors and integrate with SCADA (4), then collect data (1), analyze it (2), and finally schedule maintenance (3).
Which issue is most likely causing missing sensor data in the predictive maintenance analytics pipeline?
Consider what physically prevents data from reaching the system.
Network connectivity loss stops sensor data from being transmitted, causing gaps in analytics input.
What is the best practice for scheduling retraining of predictive maintenance models in a SCADA environment?
Think about adapting to new data patterns without wasting resources.
Periodic retraining triggered by data drift or equipment updates ensures models stay accurate and relevant.