OIL HEALTH MONITORING | S120 SENSOR
Considered to be one of the most crucial tests in oil analysis, the importance of particle counting can’t be overstated. Particle counting helps identify potential problems in fluids and lubricants by monitoring and detecting the number and size of different particles in the oil sample. From spotting high contamination or wear conditions to checking turbine oil cleanliness, particle counting is an invaluable part of any oil analysis or condition monitoring program.
Maximum Savings
To reduce the operational risk and to avoid fatal faults in the critical machines, the sensor, which is permanently installed, allows optimise the asset operation and achieves higher and better returns.
High Sensitivity
With OilWear® the presence can be detected of particles of over 4μ, which can be classified in accordance with their shape, discriminating between
particles and bubbles and precisely detecting different types of fault
Integration
OilWear® has complete integration with SCADA/PC/PLC through both analogue and digital communication protocols, so that a single sensor can give information on the degradation of oil and on its contamination.
Improved Reliability
By rigorously monitoring the condition of critical machinery, OilWear® allows you to optimise the periods of operation and maintenance and so increase the availability of the process.
Robustness
OilWear® presents results entirely proven in all areas. The sensor has been tested and integrated into ‘Condition Monitoring’ systems, demonstrating its solidity in all environments and situations
OilWear® allows estimates to be made to plan the maintenance tasks with time, thus avoiding downtimes occurring at the most awkward moments.
Immediacy
With OilWear®, anticipate potential faults with early knowledge of the state of the critical machines and establish alarm levels from the outset and in the different operating conditions of your machine.
Real Time
OilWear® detects faults in real-time, and can offer information at any time to solve problems before they occur.
