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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Excessive heat is a severe contaminant. It wreaks havoc on oil (chemically and physically) and retards lubricant performance by increasing wear, corrosion and friction. Friction and wear cause more heat, which sends the machine into a cycle of despair.

Heat must be controlled within the machine’s operating limits. This varies considerably between machine types and applications. Lubricants have their own unique limits as well.

Attempting to solve heat problems by simply adding a cooler or enlarging the cooler just masks the symptom and prolongs the solution. Abnormal heat is a telegraphed S.O.S. call that commands attention and remediation.

Critical temperatures on most high-speed and high-value machines are monitored in real time, often at multiple points, such as guide and thrust bearings (typically imbedded thermal couples). A common example of bearing temperature monitoring is shown in Figure 1.

Here, a temperature excursion was noticed first before any other symptoms. After inspection, a lubrication issue (cake-lock) was found to be the root cause.

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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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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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According to Massachusetts Institute of Technology professor emeritus and luminary tribologist Ernest Rabinowicz, there are three things that cause machines to lose their usefulness: obsolescence, accidents and surface degradation. Without question, obsolescence is fundamental to the evolution of engineering and technology. The old must make way for the new. Yet some inventions have long life cycles, the grease fitting for example. Its design has changed little since Oscar Zerk invented it in the early 1920s, yet is still widely used today. The automobile, on the other hand, is dynamic and in constant flux. While the classics cars live on into perpetuity, most automobiles face practical obsolescence long before they are functionally inoperable.

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As head of Noria’s failure investigation group, I’ve led many interesting studies in search of failure root causes and remedies. These include missile system failures, highway accidents, helicopter crashes, and turbine-generator wrecks. Most of these nearly 100 investigations were substantially hampered by errors made in collecting and preserving evidence.

We know that when critical failures occur, every effort should be made to prevent repeat performances. Yet, without an intervention to remove the underlying root cause, a recurrence is almost guaranteed.

It stands to reason that maintenance organizations should consider failure investigations as seriously as they do the repair activities needed to return a machine to service. Yet all too often, once production has been restored, the urgency and memory of the failure begins to fade.

We’ve published extensively on the importance of root cause analysis (RCA) and the steps needed to carry out an RCA. This column will not address these well-documented procedures but instead focuses on the equally important task of preserving and collecting evidence.

After all, it is this evidence that serves as the essential raw material used in the RCA processes. The quality and completeness of this evidence (raw material) is arguably the central factor that determines the precision of the delivered result (the root cause and RCA end product).

Sadly, by the time I get a phone call to participate in an RCA, there is usually only a scintilla of evidence remaining. Perhaps there are a few fragments of a broken bearing or the shelled-out remains of a failed pump. In other cases, there might be photos of the crime scene taken by an alert technician. Of course, there is plenty of anecdotal evidence and personal theories from people who arrived first on the scene. But when it comes to collecting quality data and preserving physical evidence, what’s available is usually pretty skimpy.

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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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A proactive maintenance approach has been particularly successful in reducing or eliminating one of the most serious equipment maintenance problems: contamination of lubricant or hydraulic fluid systems. According to the bearings division of 1RW, “contamination is the number one cause of bearing damage that leads to premature removal.” Caterpillar states that “dirt and contamination are by far the number one cause of hydraulic system failures.” Similarly, J.I. Case states that “systems must be kept clean, spotlessly clean, in order to achieve the productivity they are capable of.” Finally, Oklahoma State University reports that when fluid is maintained 10 times cleaner, hydraulic pump life can be extended by 50 times (Fig. 1 ).

The most common types of contaminant induced failures in machinery are wear, sticking, seizure, erosion and corrosion. Contaminants can include solid particles, moisture, air, chemicals and other foreign materials. Figure 1 – Cleaner hydraulic fluid extends pump life.

The rate at which contamination enters a system is typically underestimated, and the effectiveness of filters in removing this contamination is often overstated. According to a study of hydraulic equipment at Oklahoma State University, “it has been demonstrated that apparent ingression rates of 10-100 million particles greater than 10 microns (per minute) characterize field systems (Figure 2).” Filters often have great difficulty removing these high contamination levels since they are subject to frequent changes in temperature, fluid viscosity, pressure, and flow; plus the effects of shock, vibration and fatigue. Other common problems are filter bypass valves that are stuck open, damaged or missing filter gaskets, and filters that are installed crooked or backwards.

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The definition of an asset is something that delivers future value (such as a machine that produces your widgets year after year). Conversely, a liability is something that can consume your assets in the future (such as negligent or faulty reliability practices). Like an endless string of mortgage payments, the long-term cost of such liabilities can be enormous as they chisel away at a company’s assets and profits.

Knowledge is often referred to as intellectual capital. It is an asset that is usually acquired at some expense (college tuition, for example). Like a wise old teacher, machine failure is a bountiful source of knowledge. It comes at an expense (downtime and repair bills), but when we learn from it, then it offers the opportunity to produce sizable returns. This is why it is often said that intrinsic machine reliability is 99 percent failure. Phrased another way, we need failure in order to attain and deploy the knowledge that insures reliability and greater future profitability.

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