Enabling a holistic approach to predictive maintenance
06 February 2017
At the last SPS/IPC/Drives event Mitsubishi Electric demonstrated a development of its condition monitoring technology for predictive maintenance, which builds on the capabilities of add-on smart sensors by integrating them with an intelligent sensor controller.
The Smart Condition Monitoring (SCM) solution complements ‘traffic light’ alerts with detailed diagnostics, in-depth analysis and recommended actions to minimise unscheduled downtime and maximise asset availability.
Sensors alone can offer a basic indication of a machine’s operating condition using a traffic light system of red, amber and green lights can ‘at-a-glance’ monitoring. However, this approach is limited in the amount of information available for analysis.
The technology has now been developed to allow the sensors to monitor the full range of parameters, allowing this information to be interpreted to give an overview on the asset health of the plant as a whole.
Mitsubishi Electric has further built on this with the SCM Kit solution that uses FAG SmartCheck sensors for monitoring and feedback. The kit provides an integrated approach to monitoring the condition of individual assets, and enables a holistic approach to be taken to monitoring the asset health of the whole plant.
Individual sensors retain the traffic light system for local warning indication at the machine, but at the same time information from multiple sensors is transferred over Ethernet to the intelligent sensor controller for in-depth monitoring and analysis.
SmartCheck sensors can be added to machines as and where required, with a simple teach function allowing the sensor and controller to learn the normal operating state of the machine, generating a memory map of key parameters. Once set up, the SCM provides 24/7 monitoring of each asset, with functions including bearing defect detection, imbalance detection, misalignment detection, lack of lubricant, temperature measurement, cavitation detection, phase failure recognition and resonance frequency detection.
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