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Right-Modal Curve

Also referred to as an "R-Curve".

A form of deterioration model that uses probability distribution curves to make asset management forecasts. One of four broad classes of Iowa Curves, as follows:

Listed below are some the key attributes of Right-Modal curves:
  • Back-end Loading - These are assets in which the greatest frequency of retirements is after the life term is reached, which causes the retirement frequency curve to be skewed to the right of the mean. The majority of the assets in this group will last longer than the average life, but most of them will be retired in a short period of time after the average life is reached. see: back-end loading.
  • Negative Skewness - The probability distribution has negative skewness with a long tail to the left of the mean.
  • Positive Aging -  The longer the asset has been in service, the more likely it is to fail. In other words, the hazard function increases for larger values of t. This makes intuitive sense, because the longer stuff is used, the more it wears down. Thus, something that has been in use for a long time will be approaching its breaking point.
Five of the Iowa Curves are labelled as R-Curves.

Listed below are some examples of some assets that are generally considered to be right-modal:
  • Fire alarm control panels.

Generic right-modal curve (negative skewness)
Fig. Generic right-modal survivor curve (negative skewness).

Three performance classes along a right-modal survivor curve.  
Right modal curves to order-1, order-2 and order-3.
Fig. Three performance classes along a right-modal survivor curve (left) and right-modal curves to order-1, order-2 and order-3 (right).

Comparison of right-modal and left-modal survivor curves.
Fig. Comparison of a right-modal survivor curve (shown in "green') and a left-modal survivor curve (shown in "blue").

Critical year on a right modal curve.

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I. Care is trying to connect the dots in order to reveal the patterns in the data that underpins his assets, including trends.
Fig. I. Care is trying to connect the dots to reveal the patterns in the data that underpin his assets.

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