Turning an oil analysis program into a feisty profit center is well within reach of today’s modern maintenance organizations. In fact, it is commonly achieved. The strategy, perhaps, depends less on what the data is trying to communicate than the confidence the user assigns to the data. In fact, successful users of oil analysis have learned that achieving high confidence in oil analysis data is a team effort; the goals and responsibilities are shared equally between both the user and the laboratory. Such programs, when well-applied, result in a satisfying win-win business relationship.

From the user’s perspective, there are many strategic elements to developing a positive working relationship with commercial oil analysis laboratories. When the end user has a basic understanding of the business needs of the laboratory and structures a program consistent with satisfying those needs, the underpinnings of a successful relationship exist. Likewise, the laboratory needs to provide the essential services needed to insure that the oil analysis program is sufficiently information-intensive and builds core value from the point of view of the user and his goals. These goals vary in emphasis from user to user but typically relate to subjects such as reduced lubricant consumption, root cause condition control, and incipient fault detection.

Because a high percentage of today’s oil analysis users are setting up primary or auxiliary labs onsite, the lab-user relationship is changing and needs to be redefined. In such cases the service provider is internal and, being a stakeholder in the organization, would share many of the goals of reliability and cost reduction. However, unlike the commercial lab the onsite er corporate lab may not ~ motivated to achieve high sample volume, peak productivity, and near term profits. Consequently, the relationship must mold to the business environment.

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Condition monitoring can be easily performed by following a few simple principles. Among these principles include monitoring two sets of conditions:

1. The operating and environmental conditions that precede failure, and
2. Early-stage failure symptoms

Several models are presented that show the benefits of monitoring machine conditions, as well as the consequences of ignoring them . Also discussed is the integration of both proactive and predictive maintenance techniques to extend machine life.

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It is often said that a well-engineered oil analysis program has three essential categories. In the past, I’ve referred to this as the iron triangle of oil analysis. The three categories are:

1. Fluid Properties Analysis. Information on a lubricant’s physical and chemical properties such as viscosity, acid number, demulsibility, additives, etc.
2. Fluid Contamination Analysis. The presence of uninvited guests such as dirt, glycol, soot, fuel, water, etc.
3. Fluid Wear Debris Analysis. Concentration and characterization of wear metals suspended in used oil.

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Lacking real knowledge in lubrication, companies must still make lubrication decisions. Decisions often seem straightforward on the surface, but below this surface, they are plagued with pitfalls that can cost companies dearly. Many are lured into making bad decisions due to pressures imposed by overzealous vendors or by management’s cost-cutting directives. For instance, those with lubrication knowledge know that saving money by buying cheap oil is almost always a false economy. On the other extreme, buying quality oil to remedy bad lubrication is also a false economy. Sadly, many companies fail to make the important distinction between good lubricants and good lubrication. Yet, it :s this distinction that defines whether or not we are on a trajectory to lubrication excellence.

Good lubrication requires knowledge, initiative and persistence. It’s proactive, not reactive and certainly not passive. You cannot buy your way into good lubrication – just like you can’t use money to tame unruly maintenance culture. Lubricants of high science and quality, at any price, cannot forgive the maintenance practitioner for not knowing how much, how often and by what method. No one is born with such insight.

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