CATS - Global, Nashik

MACHINE CONDITION

INTRODUCTION

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.

FUNCTIONAL OBJECTIVES

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.

ATTRIBUTES

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.

ENABLER

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.