The need for eliminating catastrophic breakdowns and unnecessary maintenance costs has and will continue to drive the adoption of condition monitoring solutions across the industry segments :-
  • Turbines, Machines and Equipment's
  • VIDUR Accurately measures maximum displacement from the mean position Amplitude, Velocity and Acceleration in time and frequency domain.
Image file of damages
Videos of human building, veda and bridge etc.
  • Vibration measurements are commonly considered to be a sound indicator of a machine's overall health state (global monitoring)
  • Developed condition monitoring strategy with VIDUR can be applied for detecting excessive vibration levels that can lead to engine component failure.
  • Continuous monitoring of turbine shafts for torsional vibration.
  • CATS machine-learning algorithms do not rely on the quality of the training data but rather adaptively classify the different states/operation condition of the engine examined.
  • Optimizing maintenance activities during planned shutdowns and lowering the instances of unscheduled outages.
  • The unique laser based monitoring is : Affordable, Accurate and Acceptable.
  • Important Characteristics that prevents from :-
    • Severe Machine or structure damage
    • High Power consumption
    • Machine Unavailability
    • Accumulation of Unfinished goods.
    • Quality problems and issues
    • Occupational Hazards and human discomfort.
  • VIDUR predictive maintenance, helps producers address maintenance issues in advance of equipment breakdown :
    • preventing extended downtime,
    • avoiding unnecessary damage,
    • improving operational efficiency,
    • and reducing maintenance costs.
  • It allows operators to stop production for just a few minutes each month, for example, to replace a fan that's about to break, rather than replacing each fan every year whether it needs it or not.
  • To implement systematic 4.0 predictive maintenance, industry companies must first equip all critical machines with sensors that monitor in-use equipment conditions, such as vibration, temperature, and pressure.
  • They should then aggregate the data from these sensors into repositories, or "data lakes."
  • In addition, they should establish the performance thresholds that will trigger maintenance whenever the value of a sensor goes beyond the relevant threshold. Finally, they should use machine-learning algorithms to analyze historical data, run simulations, uncover the root causes of past failures, and predict the risk of failure for each machine.
  • Predictive maintenance can add value to three primary installations in a industry plant the gear boxes; the mills, which are expensive and numerous; and the kiln, which requires long interventions in cases of breakdown and must be shut down for several weeks each year to replace the refractory lining.
© 2021 CATS-GLOBAL All rights reserved