Knowing when a piece of equipment is going to fail (predictive maintenance) is much more difficult than making it last long (proactive maintenance). Even more complex is root cause analysis (RCA) which is performed postmortem, like an autopsy.

Still, reliability professionals are increasingly stressing the importance of performing RCAs following all failures of critical machinery. As odd as it sounds, it is more productive to study failures than successes.

After all, an apparent success may actually be a failure in disguise; more like a problem waiting to happen. Studying failures teaches us insightful lessons in developing predictive and proactive maintenance strategy.

Root cause failure analysis is a process of working backward through a sequence of events or steps that led to functional failure of the machine.

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No warning or short-warning failures are the worst kind. Think of a tire. It can wear out slowly over thousands of driving miles or rupture suddenly, at full highway speed, from a random piece of road debris. You can monitor tread loss over time and conveniently schedule a tire change. Conversely, who could predict the sudden appearance of a sharp piece of iron?

Fault bubbles are sudden-death conditions in waiting. They haven’t ruptured, but they are about to. Similar to a tire, fault bubbles can burst instantly. Unlike the tire, most fault bubbles in industrial machinery can be revealed by condition monitoring, which includes the careful examination by a skilled inspector. Once detected, the root cause can be arrested or at least mitigated.

In past columns, I’ve mentioned the P-F interval. As a review, “P” is the point at which a failure (in progress) is first detected, while “F” is the end point of functional inoperability. Although the P-F interval is a theoretical concept that has useful application, it is rarely applied in real-world machines. This is because the real world comes with many variable events. These events distort the predictability of the P-F interval.

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In a past article,1 I mentioned that in the world of reliability, risk can be defined as the probability of failure multiplied by the consequence(s) of failure. This simple definition should be a reliability team’s most important daily metric. In this editorial, a two-dimensional matrix is proposed that serves as a real-time moving picture of the health of plant assets (Figure 1).

I don’t manage an in-plant machinery reliability team at Noria (we have no machines), but if I did, I would want to see this matrix at the beginning and end of each day. I would want it to serve as my reliability command post in planning and scheduling maintenance work orders and related activities.

Let’s call it the Risk Management Grid (RMG) for production assets. Its singular purpose is to characterize composite asset risk factors associated with failure probability and consequence. It provides a revealing cross-sectional view of current reliability conditions and leaves a visible trail of the past to analyze and prescribe future proactive improvement initiatives.

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For any given machine, how critical is its reliability? What if it failed suddenly and catastrophically?

What would be the consequences – lost production, expensive repairs, fatality? Criticality is the logical starting point for all reliability initiatives.

There are many different ways to enhance reliability and improve the quality of maintenance. The best options should be risk-based. After all, if it doesn’t reduce risk, why do it? Why spend an incremental dollar to enhance a machine’s reliability if it doesn’t yield multiple dollars in return?

There’s also priority. What should be done first, second and third, and what should not be done at all? How do you know which machines return big dollars for enhanced reliability, which machines return marginal dollars and which machines return nothing at all?

Once you understand machine criticality and a machine’s risk profile, you can work smarter to customize improvements. For guidance, look to the Pareto principle, which states that 20 percent of the machines cause 80 percent of the reliability problems. Which machines are these?

In addition, consider that 20 percent of the causes of failure are responsible for 80 percent of the occurrences of failure. Which causes are these? It’s about precision – precision maintenance and precision lubrication. It’s also knowing how to make wise, risk-informed choices.

The post A New Look at Criticality Analysis for Machinery Lubrication appeared first on Tesibis.

The truth is, oil analysis is detective work, plain and simple. Today’s detectives are empowered with a growing bag of tricks but frankly, only a few of these tricks involve traditional “oil analysis.”

Let’s take a closer look at what’s involved in real oil detective work. But before we do, remember that the primary job of the oil analyst is not troubleshooting chronic machine problems but rather the activity of machine health management, that is, maintaining and controlling machine wellness.

I have written extensively on this subject in the past. Proactive maintenance is always where the big payoff is found. Still, even the best proactive maintenance programs can’t completely rid machines of random failures and occasional abnormal wear conditions. It is in these cases when the oil detective earns his keep.

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