Condition of important rotating electrical equipment such as motors, compressors and pumps can now be monitored to predict failures though Schneider Electric’s cloud based ‘EcoStruxure’ Asset Advisor platform.
Launch of the Asset Advisor for Electrical Distribution & Rotating Equipment promises to help reduce downtime and lost productivity. The company says 7% of rotary manufacturing equipment failures lead to frequent and unplanned downtime and decrease production capacity by 5% per year, at a major cost to manufacturers.
The platform allows users to monitor equipment remotely using Motor Current Signature Analysis (MCSA) – in place of traditional vibration monitoring – with digital data being fed into cloud based analytics tools to help anticipate issues before machinery breaks down, with minimal false positives.
Key advantages of MCSA are said to include the ability to work in harsh environments, due to the system being installed within control cabinets, not on the motor, which reduces the potential of malfunctioning.
In addition to measuring mechanical issues, data is also provided on power functions and energy consumption, providing the potential for energy saving and performance optimisation insights.
“Schneider’s new EAA will help alleviate common electrical and mechanical failure risks that lead to unplanned downtime,” said Schneider Electric’s smart factory business development manager John Flynn. “Once the analysis equipment has conducted its learning phase, anomalies and warnings provide the user with crucial data which they can act on. This, therefore, allows critical machinery to be repaired or replaced before failure.”
Once the monitoring system is installed, the MCSA begins a learning phase to understand the equipment’s frequency patterns. This process takes around two to six weeks, allowing the detection of anomalies that may occur over time.
After the testing phase has been completed, the Asset Advisor is live 24/7. The system monitors and sends notifications when issues and potential failure modes are detected, typically months and weeks before failure occurs, according to Schneider Electric.