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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There are several important silent assumptions of bearing reliability. However, before I address these assumptions, some even more basic assumptions and statements of fact must be established.

While it might be a bit of a leap, I’m going to assume that the bearing is well-designed, well-manufactured, properly handled and stored, and finally, correctly selected for the intended application. With that said, we’re now ready to talk about those silent assumptions that are in the maintenance function’s domain.

These assumptions relate to the internal environment and duty cycle to which a bearing is exposed. Bearing manufacturers will frequently report that only a small percentage of bearings reach their fatigue limit (catalog life). According to one major supplier, typically only 10 percent.

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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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I want bad news fast. Why? Problems tend to compound. Rarely do they heal themselves. Instead, the worse things get, the faster they get worse. As time passes, the cost of repair and lost production can soar exponentially.

Maintenance resources need focus and preemptive timing. The longer you wait to respond to what causes failure, the more machine conditions take over control of your schedule and budget. Fix the roof while the sun is shining. It’s not just about keeping machines running but rather keeping them running at the lowest possible cost.

Condition monitoring is an essential troubleshooting tool. It provides examination skills to find the cause or source of an impending problem. When issues are caught early, you have the luxury of convenience and the option of simple remedies, usually with minimal (if any) business interruption.

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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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I first met Ron Moore in the early 1990s. He is known as an icon in the reliability community and is the author of an excellent book entitled What Tool? When? This book tackles a delicate subject that is both difficult and controversial.

Moore examines and contrasts the world’s most notorious and respected philosophies in the field of maintenance and reliability. These include lean manufacturing, kaizen, total productive maintenance (TPM), Six Sigma, reliability-centered maintenance (RCM), root cause analysis (RCA), predictive maintenance (PdM) and others.

Which of these philosophies does a user organization really need? Is there a priority order or logical sequence to their use? Which produces the greater benefit or return for the lowest risk or investment? How sustainable are they? These are all great questions that require an answer, especially for those seeking a major transformation in their maintenance and reliability programs.

For those of you in the reliability field, this book is a must read. Lectures and interviews with Moore can also be found on YouTube and in the “Rooted in Reliability” podcasts for an abridged understanding of his main themes.

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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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These days, reliability professionals are faced with diverse options related to technologies and methods to detect, troubleshoot and remediate problems. Figure 1 is a simple example of the available options to collect data and arrive at decisions regarding the health of machinery and machine components.

The logical starting point is always to carefully rank failure modes by both criticality and probability of occurrence. For more information on this topic, see my previous column titled “A New Look at Criticality Analysis for Machinery Lubrication.” This method is known as failure modes and effects analysis (FMEA), and has been extensively documented.

The failure mode ranking sets into motion the critical-path process in reaching optimized decisions related to condition monitoring followed by the prescribed response or remedy. This response should not simply be corrective but also incorporate proactive measures to prevent or restrict recurrence. The emphasis is on optimized decisions and actions.

It’s easy to go cheap (penny wise, pound foolish), but there also can be temptation at the other extreme (a state of reliability excess), often driven by fear of the unknown. The optimum reference state is an activity of seeking balanced decisions. After all, you are not trying to maximize reliability. There is no greater source to find this balance than knowledge and education.

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The field of maintenance technology is going through a revolution of change. Gone are the days when a machine had a predictable service life, after which it was replaced, continuing the cycle. Today, machinery and equipment can be maintained to achieve useful operating lives many times that attainable just a few years ago.

Historically we’ve relied on the popular practice of predictive maintenance and condition monitoring to combat rising maintenance costs. Today we implement predictive maintenance with greater confidence and precision. These early warnings have proven effective at reducing the magnitude of failure and amount of unscheduled downtime; however, no real progress has been made in reducing frequency. Any maintenance strategy targeting the reduction of failure frequency must address lhe fundamental causes of failure. Such a strategy of eliminating causes would be “proactive” to failure, not “reactive” to failure.

This approach, known as proactive maintenance, is the latest and most promising wave of new maintenance technology. Its fundamental and logical approach seems likely to gamer deeply entrenched inroads into the cost-driven maintenance industry this decade. Case studies documenting cost reductions exceeding 90% have provided a solid lead for others to follow seeking similar savings.

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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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