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Since the world’s resources of material and energy are getting progressively, by necessity, there is growing involvement in studies of wear on a global basis. Wear of sliding components result in reduced mechanical efficiency and an irretrievable loss of material in the form of wear debris. Wear at the interface between moving particles is a normal characteristic of machine operation. The kind and rate of wear depend on the machine type. Lubrication is provided between the moving surface to minimize the wear but during operations millions minute wear particles entering the lubricating oil. These particles are in suspension in the oil, larger particles may be trapped by filter while others generally too small to be removed, remain in suspension in the circulating oil.
Condition based monitoring has, in the past, been referred to as an art, when quite clearly it is a science, and despites the cost of machine, surprisingly little attention has been devoted to this science from the viewpoint of understanding and modeling failure mechanisms and the study of probability to failure. Predictive maintenance technique has now become common exercises as they maximize the machine availability time and minimize the cost of maintenance, since the machine can be stopped just before as impending problem in an other wise healthy machine.
Fault detection using vibration analysis is difficult in very low speed – high load noisy machines. In the case of slow speed bearing the vibration generated by damaged components is very low, usually close to the floor noise and difficult to identify. In these situations, Wear Debris Analysis has proven useful in providing supporting evidence on the bearing or gear status. It also provides information on the wear mechanism, which is involved.
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