Asset management is a control strategy

Guest columnist Dr. Carlos Talbott discusses managing process variation in this installment of OEM Insight.

By Dr. Carlos Talbott

In my view, the typical role of control engineering has been to design and maintain stable production processes. The idea is to manage in-work process variation in order to produce quality products and services. By reacting to signal perturbations, we can center on the setpoint and stabilize variation. Detection of process drift and out-of-control conditions, however, is often the role of quality control. They use statistical process control analyses to detect abnormal behavior and assign causes. One assignable cause can be deteriorating performance of failing machines. Most machines die over a period of time and present an array of declining health symptoms (e.g., vibration, temperature, etc.). Maintenance has had the role of monitoring machine condition.

With today’s environment of downsized organizations and outsourced functions, it is only a matter of time before each of these three distinct roles (process control, quality control, and machine condition monitoring) are combined under the umbrella of “asset management.”

Asset management involves continuous improvement of production capacity using process control, quality control, and condition-monitoring technologies coupled with process design innovations to achieve four key goals:

Eliminate unscheduled downtime and slowtime. Many continuous, automated production processes lose output potential due to unscheduled downtime. More subtle is the lost output when production processes run slower than their maximum rate. Some discrete product manufacturing processes lose 25% of their theoretical capacity due to downtime and slowtime.

Eliminate waste. Most businesses track waste of raw materials and finished goods but few can tie these costs to process downtime. Moreover, in the rush to avoid unscheduled failures, we often discard (waste) machine life and incur unnecessary maintenance expenses with no idea of the value of wasted machine life.

Eliminate idle resources. Millions of nonproductive dollars can be tied up in emergency repair spare-part inventories that are unnecessary if there are no unscheduled downtime or slowtime events. Redundant machinery (and the labor to run them) is another source of “idle” resources.

Eliminate safety and environmental risk. Some unplanned machine failures can result in injury or damage to finished goods, facilities, or the environment. The cost of such a failure can even involve loss of brand-name equity.

Innovations are in place that will revolutionize asset management. One is the integration of condition-based sensor hardware and time/frequency data capture into electric motors, pumps, gearboxes, etc. This inexpensive, online technology will replace the standalone condition data collection systems of today. Even though condition signals can be treated as just another process control signal, alarming of imminent failure is not sufficient for asset management; prognosis of remaining machine life is also needed.

Another innovation is the advent of the Machinery Information Management Open Systems Alliance (MIMOSA). This not-for-profit group of asset management advocates (www.mimosa.org) has developed condition monitoring data exchange standards for organizations to freely exchange data. Several key ideas inherent to MIMOSA standards are date-time stamping of data points so that multivariate data can be combined into multivariate time series; machine serial number identification detail to ensure apples-to-apples comparisons; and operating environment details such as ambient temperature, pressure, power quality, and load.

Finally, there is emerging interest in the prognosis of remaining machine life. Here, prognosis is a reliable and accurate estimate of the remaining life of a machine based on quantifiable assessment of condition. A reliable and accurate prognosis should provide an understanding (and audit trail) of the confidence level and error bounds of any point estimate of remaining useful life, e.g. four months plus or minus four weeks at 90% confidence.

As these asset management innovations develop, now is the time for control engineers to step forward and become involved in applications that promise to be the next economic frontier in manufacturing.
 


  About the Author
Dr. Carlos M. Talbott is a reliability strategist, statistician, and educator in world-class manufacturing, machine failure prediction, root-cause analysis, process quality improvement, and time series forecasting. He received his Doctor of Science in Operations Research from George Washington University. He is a member of IEEE’s Reliability Society and ASQ’s Reliability Engineering division, and is on the Board of Directors for the Society for Machinery Failure Prevention Technology.
 
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