The sheer number of infographics in the condition monitoring field is staggering; they show up on social media, and in conference presentations, whitepapers, websites and books. Infographics are effective at helping people comprehend difficult concepts that integrate an array of variables and factors.

My soon-to-be-published book, “Inspection 2.0,” covers a host of different condition monitoring methods, including sensory inspections. I was looking for an infographic to illustrate failure modes and detection methods in the time domain for different types of machines and applications but was unable to find a graphic that fit my needs.

Necessity is the mother of invention. Left without choices, I decided to construct my own graphic, naming it Condition Alarm Mapping (CAM). The final product is shown in the figures on the following pages. However, the number of variations and uses of the CAM graphic is extensive and goes far beyond the scope of this article. As an introduction, I can show and describe what it is, why it is needed, and how it is used.

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Several years ago, when my son was a Cub Scout, his den went on a night hike at our local wildlife sanctuary. He and I went together on this outing. We were led by a professional nature guide who worked full-time at the sanctuary. I wasn’t expecting much; after all, what can one see in the forest when it’s pitch dark?

It wasn’t long into the hike before I realized that I was nothing more than a babe in the woods. While I was concentrating on my footing along the trail, our guide was pointing out everything from faint insect smells to the luminescent trailers of caterpillars on tree branches. To her, the forest was an orchestra of sights, sounds and smells. Using only her senses, she named the species of countless birds, mammals and reptiles we encountered along the way. She was in her element … a true professional in tune with her environment. I was impressed.

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Try this simple experiment. With notepad in hand, go out into your plant and walk around a sampling of your most critical machines. These are the ones that have high probability of failure and/or the greatest consequences of failure. They are also known as show-stoppers and, as such, generally merit a disproportionate allocation of reliability and maintenance resources.

Next, on your notepad make a list of all the lubrication-related enhancements that have been implemented on these same machines since they were commissioned. Take special note of those that have been applied in the past five years. Solicit any amount of help (from old-timers, for instance) you need to flesh out your list.

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I have learned that excellence in lubrication is just as difficult to realize as it is rare to find. The reasons for this are many. One simple explanation is the field of lubrication is a specialty that takes education and years of experience to master – like most professions.

Companies employ professionals with specialized skills indeed (computer science, finance, marketing, etc.), but how often have you heard of a recruiter going to a college campus to interview and hire lubrication professionals? I’ve never heard of this.

Likewise, I know of no colleges or universities in North America with degree programs in lubrication. No wonder excellence in lubrication is so rare. Do you sense an opportunity here?

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.

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Abnormal wear is not like a bad rash, which tends to go away on its own in time. Instead, it’s more like early-stage cancer, which requires intervention and treatment. Oil analysis has exceptional abilities to detect abnormal conditions, both root cause (like dirty oil) and predictive (active failure in progress).

Root cause failure analysis is post-mortem. It starts with failure and works backward in search of one or more root causes. The knowledge gained reveals a plan of needed change that will prevent or delay the recurrence of similar failures. Failure is indeed a strategic teacher of better ways to design, manufacture and maintain machines.

The whole purpose of machine condition monitoring, like oil analysis, is to enable organizations to foretell the future. It produces data that points to the existing problems and the seriousness of these problems. Action is required to confirm a problem’s existence, determine and verify the root cause, and finally to remedy the problem. Sadly, this is where most oil analysis programs are delinquent. The fault lies equally with the laboratory and the end user.

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Lubricant analysts face many challenges in trying to translate laboratory data into meaningful comments for those responsible for lubricant and machine health. One could say the analyst gives a voice to the oil in communicating “what hurts”.

But before a doctor can make a diagnosis, he must ask many questions regarding the patient’s health history and lifestyle. Like the doctor, the lubricant analyst requires similar information relating to provenance – that is, the source or the origin – before the data can be accurately diagnosed and corrective actions prescribed.

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Most oil samples are taken based on a fixed schedule. For large, stationary rotating equipment, monthly or bi-monthly samples are common. Proactive maintenance programs depend on regular checks for oil cleanliness, dryness and lubricant quality.

However, machines can and do fail for a variety of reasons, and there is a certain randomness to the onset of these failures. Furthermore, the failure development period is equally unpredictable, with some failures taking months to develop, while others are sudden and abrupt.

In the March-April 2013 issue of Machinery Lubrication, I addressed machine criticality analysis as an essential tool to define the Optimum Reference State (ORS) for numerous lubrication and oil analysis activities. The Overall Criticality Matrix (OCM) is constructed from two assessments: the Machine Criticality Factor (MCF) and the Failure Occurrence Factor (FOF).

The MCF relates to the consequences of machine failure while the FOF relates to the probability of machine failure. Both the MCF and the FOF are highly influenced by the effectiveness of “early fault detection.” In other words, the effectiveness of early fault detection sharply reduces machine criticality (for details on this, read the article at www.machinerylubrication.com/Read/29346/machinery-criticality-analysis).

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