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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It is intuitively obvious that smart maintenance decisions require a heightened sense of both the probability and consequences of machine failure.

However, when lubricants fail, there are consequences that are, at least initially, independent of machine failure. These include the lubricant replacement cost (material, labor, flushing, etc.) and associated downtime. These costs can exist in the presence of a perfectly healthy and operating machine.

Of course, lack of timely replacement of a defective lubricant will invariably lead to dire machine failure consequences. For some machines, these cascading events can produce enormous collateral damage and financial hardship to an organization.

In the next issue, I will explain how nearly all decisions related to lubricant analysis and inspection depend on four factors: Overall Machine Criticality, Overall Lubricant Criticality, Machine Failure Modes Effects Analysis (M-FMEA) and Lubricant Failure Modes Effects Analysis (L-FMEA).

For instance, regarding inspections, these factors influence what to inspect, when to inspect and how to inspect. In relation to oil analysis, these factors affect where to sample, how often to sample, which tests to conduct, which alarms to set and the general data-interpretation strategy.

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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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