The Importance of Machine Selection in an Oil Analysis Program
Tags: oil analysis, lubricant sampling, oil changes
Proper machine selection can be viewed as a foundational element to receiving a firm return on investment (ROI) for an oil analysis program. Rarely is it cost-effective to include all oiled or hydraulic systems and components in the program. By attempting to include all machines or even most machines in the oil analysis program, you risk passing the point of diminishing returns on the cost and effort required to deploy this approach.
There are several methodologies concerning machine selection for oil analysis. One such method utilizes quartile coverage. As implied by the name, the asset list, with associated criticality values, is divided into quartiles. This method further breaks the quartiles into upper and lower halves, in essence breaking the entire list of candidate machines into eight sections. By doing so, you can assign the machines into a specific area, thus allowing for some level of guidance in machine selection.
This example of quartile-based program
coverage has 200 total possible assets.
When utilizing this method, you must understand that the lower your program is on the coverage table, the higher the focus on only the very critical machines. For example, if you are just developing your oil analysis program, or even re-developing the program, and the decision is made to only reach the upper fourth quartile in coverage, you must sample the most critical machines first. Once the decision is made to increase quartile coverage, you then must work your way down the criticality list to determine the next machines to add.
A second method is to simply use the Pareto principle for machine selection. The Pareto principle, or the 80:20 rule, suggests that the top 20 percent of your critical machines have the potential to cause 80 percent of your problems. Ideally, these machines will fall into the top 20 percent of your criticality scale. By using this simple rule, it becomes very easy to make a decision regarding machine selection.
| 79% | of lubrication professionals rate machine criticality as the most important factor when selecting machines for the oil analysis program, according to a recent poll at machinerylubrication.com |
If you truly focused on the top 20 percent of raw criticality values for machine selection, the Pareto principle would break down as shown in the table below. However, if you want to consider the actual top 20 percent of machines, which is recommended, you should take this a step further. In calculating the actual percentage of machines, compile a list of all machines and the associated criticality value of each. You then use simple mathematics to determine the actual criticality value cutoff point for the top 20 percent of machines.
Following this process should not imply that you ignore the lower 80 percent of the plant equipment. Instead, it offers a starting point to focus your initial efforts. It also provides a continuation point for when program expansion is desired.
Pareto principle breakdown of the top 20 percent
of raw criticality values for machine selection.
In utilizing either method for machine selection, you still must take into account a few more factors. Generally, the primary factor after criticality is sump volume. When you consider a small centrifugal pump contains about a quart of oil, it is easy to realize how performing regular oil analysis may be defeating the purpose of value-added tasks.
When sampling a system with very small sump volumes, you run the risk of basically performing a full oil change each time a sample is pulled. This is the case because of the hardware flushing process required to adhere to best-practice sampling. Components that may fall into this realm include small gearboxes, pumps and oil-filled bearing housings.
In today’s technology-centric environment, sump size is less of an issue, provided necessary measures are taken to modify equipment so the overall oil volume can be increased.
The fluid environment severity should also be considered. For example, if you have two like machines with criticality levels that are similar or even equal in calculated value, simply looking at criticality alone would suggest the same oil analysis strategy for each machine or component. However, if you look at the overall environment in which the fluid must operate, one component may warrant a slightly different strategy than the other.
Development of a comprehensive oil analysis program takes effort. It is easy to take the path of least resistance and simply create a program “on the fly.” However, very little measurable return is experienced in this method. To achieve the best ROI, criticality and the severity of the fluid operating parameters must be considered. It is only then that you can call your program truly world-class.
Understanding the Pareto Principle
The Pareto principle, also known as the 80:20 rule, provides a strategy for working smart in your maintenance organization. It states that 20 percent of the causes of failure are responsible for 80 percent of the occurrences of failure. It is essential to understand these important causes, which are referred to as the critical few. These are the select few causes within your realm of control on which you should focus. They might include particle contamination, moisture contamination, the wrong lubricant, a degraded lubricant or even mechanical causes such as misalignment and imbalance. The Pareto principle enables you to focus your resources, set priorities and identify low-hanging fruit to get the biggest bang for your buck and help reduce the risk to your organization. Another 80:20 rule relates to the machines themselves, since not all machines present the same level of risk. According to this rule, 20 percent of the machines in your plant may be responsible for 80 percent of the downtime. These machines are the bad actors that are most prone to failure. They may be running at catalog load or even exceed the recommended load, as opposed to those machines that are running at only 5, 10 or 20 percent of the rated load. If you understand which machines are the riskiest and have been the most prone to failure in the past, you can focus your maintenance and reliability technology resources on those machines.
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