Manufacturers need to increase machine availability and reduce unscheduled downtime in order to respond to global competition, requiring techniques that will help to manage maintenance and maximise reliable production. Condition monitoring offers a predictive approach to plant maintenance, ensuring optimum asset performance while keeping downtime to a minimum.
The Smart Condition Monitoring (SCM) solution from Mitsubishi Electric 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 provide both an in-built ‘traffic light’ warning indication at the machine, but at the same time plain text information from multiple sensors is transferred over Ethernet to the smart sensor controller for in-depth monitoring and more detailed analysis.
Linking multiple sensors into the sensor control system enables the controller to analyse patterns of operation that are outside the norm, with a series of defined alarm conditions that provide alerts that attention is needed.
The SCM analysis provides detailed diagnostics, offers suggestions for where additional measurements should be taken, and provides maintenance staff more precise error identification with identifying the root case and even recommendations as to what rectification actions should be taken, with clear text messages presented to personnel. This information can be networked to higher-level systems for ongoing trend analysis across all of the assets around the plant.
- Predictable maintenance month before breakdown
- Reliable online monitoring of the machine
- Intelligent process monitoring
- Easy installation
- Intuitive operation
- Long term storage of historical data
- Flexible, expandable system
- Full service around machine diagnosis
The Smart Condition Monitoring system supports a number of functions that aid in predictive maintenance:
- Bearing defect detection
- Imbalance detection
- Lack of lubricant detection
- Temperature measurement
- Cavitation detection
- Phase failure recognition
- Resonance frequency detection