In my previous blog, Plant Asset Performance Management is for everyone, I talked about some of the challenges that manufacturing plants face in their day-to-day operations. In the second installment of my series on plant Asset Performance Management (APM), I’d like to take a look at why assets fail and how this information can help you choose an appropriate asset management program to suit the specific needs of your plant.
Why do assets fail?
Understanding the pattern of asset failure is the first step towards defining effective strategies to improve their reliability and availability. Most reliability engineers are familiar with the traditional failure profile represented by the bathtub curve, as shown below.
As you can see in this view, an asset’s life cycle can be divided into three distinct zones:
- Zone 1: High infant mortality, characterized by a high but rapidly decreasing failure rate as latent defects are identified and eliminated, and as assembly, installation, and commissioning issues are resolved
- Zone 2: The infant mortality period is followed by a useful life period. It is during this phase that the failure rate is relatively constant and normally at its lowest. Failures are usually random
- Zone 3: Finally, a wear-out zone, characterized by a rapidly increasing failure rate as components begin to approach the end of their operational lifetime
While the bathtub curve is a useful way to illustrate the age-related failure rate of an asset throughout its operational life cycle, recent studies1,2,3,4,5 have shown that age-related failures only account for about 20% of all failures. Instead, random failure patterns for assets and components were found to be much more prevalent, accounting for roughly 80% of all failures.
With age-related failures, the amount of time an asset is in operation (in-service duration) contributes to its eventual failure. Other factors include stress fatigue, erosion/corrosion, wear out etc. With random failures, as their name suggests, the failures occur randomly and are not influenced by the length of time the asset is in operation.
Preventative vs predictive maintenance
For a time-based preventative maintenance task to be applicable, two conditions should exist: (1) the failure mode must be wear or age related, and (2) the probability of failure must increase at an identifiable age. Since we’ve established that most failures are not, in fact, age related, time-based preventative maintenance would not be the most effective strategy for preventing failures. The most effective approach would be a predictive maintenance strategy that is based on condition monitoring to detect potential failure conditions and allow a known and sufficient time period for adequate correction.
The advantages of predictive maintenance are many. A well-executed predictive maintenance program will all but eliminate catastrophic equipment failures, reduce O&M costs, and minimize or erase overtime costs.
The table below illustrates the typical failure patterns, of which type D was found to be the most common:
The majority of current intelligent automation assets are designed to power predictive maintenance strategies. By implementing a comprehensive automation APM program, you can leverage all this built-in predictive intelligence to monitor the health status of your assets in real time, detect abnormal conditions, and automatically generate data on your assets’ health, along with potential failure causes and possible maintenance actions, in an easy-to-understand way. Mobility and decision support tools, like workflow functionality, can further improve the productivity of the team managing the plant’s O&M.
In my next blog, I will take you through what needs to be done in order to implement an automation APM program in your plant.
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 Jorge E. Núñez Mc Leod, Pedro Baziuk, Romina D. Calvo and Selva S. Rivera et. Al , World Congress on Engineering, U.K, 2015.
 F.S. Nowlan and H.F. Heap, Reliability-Centered Maintenance, United Airlines Report, USA, 1978.
 Broberg, Broberg´s report, USA, 1973, cited in Failure Diagnosis & Performance Monitoring, Vol. 11 edited by L.F. Pau, published by Marcel-Dekker, 1981.
 MSDP Studies, Age Reliability Analysis Prototype Study, American Management Systems, U.S. Naval Sea Systems Command Surface Warship Directorate, USA, 1993.
 T.M. Allen, U.S. Navy of Submarine Maintenance Data and the Development of Age and Reliability Profiles, Departament of Defense, USA, 2001.
 SSMD report, USA, 1993, cited in Reliability-Centered Maintenance Handbook, United State Navy, USA, 2007.