Plainly stated, the burgeoning cost of maintenance is a serious business problem. According to DuPont, “maintenance is the largest single controllable expenditure in a plant: in many companies it often exceeds annual net profit.” One major U.S. automotive manufacturer has a maintenance staff of between 15,000 and 18,000, all plants combined. They say “85% to 90% is crisis work” (breakdown).

While preventive maintenance, when well implemented, has been shown to produce savings in excess of 25 percent, beyond that its benefit quickly approaches a point of diminishing return. According to a Forbes Magazine study, one out of every three dollars spent on preventive maintenance is wasted. A major overhaul facility reports that “60 percent of hydraulic pumps sent in for rebuild had nothing wrong with them.” These inefficiencies are the result of maintenance performed in accordance with a schedule (guess work) as opposed to the machine’s true condition and need.

Most recently, predictive maintenance (also known as condition monitoring) has been leading the way to additional savings over preventive maintenance. The use of real time or portable instruments such as vibration monitors, thermography, ferrography, etc. has been effective at recognizing the symptoms of impending machine failure. The major benefit is the availability of an earlier warning, from a few hours to a few days, which reduces the number of breakdown “catastrophic” failures. Predictive maintenance is usually implemented concurrently with preventive maintenance and targets both the warning signs of impending failure and the recognition of small failures that begin the chain reaction that leads to big failures (i.e., damage control).

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There are several important silent assumptions of bearing reliability. However, before I address these assumptions, some even more basic assumptions and statements of fact must be established.

While it might be a bit of a leap, I’m going to assume that the bearing is well-designed, well-manufactured, properly handled and stored, and finally, correctly selected for the intended application. With that said, we’re now ready to talk about those silent assumptions that are in the maintenance function’s domain.

These assumptions relate to the internal environment and duty cycle to which a bearing is exposed. Bearing manufacturers will frequently report that only a small percentage of bearings reach their fatigue limit (catalog life). According to one major supplier, typically only 10 percent.

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It has been extensively documented and widely stated that particle contamination is the number one cause of wear and failure of hydraulic components. The problem is generally more pronounced than in other types of machinery incorporating circulating systems that use sirnjlar types of oils. This heightened contaminant sensitivity is due to the high pressures and tight tolerances which are characteristic of modern hydraulic machines. Pressure is known to have a disproportionate effect on contaminant sensitivity.

Much has been learned in the past three decades about contamination control at both a laboratory research level as well as the real-world deployment of this knowledge in machinery-intensive industries. Case studies have flourished on the practical and economic benefits of maintaining hydraulic systems and fluids at extreme levels of cleanliness. Hence, the speculation is gone relating to the business case and strategies that produce savings and benefits to user organizations. For many owners of hydraulic systems the opportunities of planned cleanleness are like low-hanging fruit that is ripe for picking. This chapter summarizes this body of knowledge and the value-producing strategies needed to control particle contamination in hydraulic fluids.

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