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.

The post The Power of Root Cause Pre-failure Analysis appeared first on Tesibis.

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.

The post Detection Zone Coding for More Efficient Condition Monitoring appeared first on Tesibis.

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.

The post The Richness of Machine Failure appeared first on Tesibis.