Have you had a blood test lately? It is important to be aware of your physical condition and that your blood is running healthy. In a similar manner, oil analysis is just as important to equipment, because it analyzes not only the oil but the machine itself. This provides vital information to help determine the root cause of mechanical problems which threaten your machine. Utilizing oil analysis helps you get the most information, which can lead to your success in asset management.

- Reginald C. Ventura, Le Price International Corporation

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From the book: Lubrication Excellence 2005 Conference Proceedings
In their search for the latest and greatest management and technical innovations, which often arrive with glorious trappings and fanfare, organizations often overlook opportunities to correct deficiencies that are so ingrained within the organization that they are often accepted as normal. Even simple changes can profoundly affect an organization's effectiveness and bottom line. Clearly, the organization can't ignore highly innovative technology and business practices that might profoundly affect its performance. However, management must place priority on eliminating controllable deficiencies that the organization accepts as normal, and it must support this effort with resources.

The problem-solving method with which the roots of these deficiencies are uncovered, described and eliminated is generally referred to as root case analysis (RCA), or root cause failure analysis (RCFA). This powerful technique can and should be employed by machinery lubrication professionals to seek out and eliminate the underlying reasons for technical problems and/or human errors.

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The use of limits is a powerful tool in data interpretation, as well as oil analysis and different types of number-crunching activities. Limits are used to sieve key points and highlight information that might not be readily obvious, even after useful data has been selected. The initial selection of the right limits is crucial to the long-term usefulness of limits and efficient management of data. Too often the incorrect or insufficient limits are selected for a parameter, leading to costly errors due to misleading information.

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Best Practices for Machinery Lubrication
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