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Left-Modal Curves
Also referred to as an L-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:

Attributes
Listed below are some of the key attributes of left-modal curves:
  • Front-end Loading - These are assets in which the greatest frequency of retirements is prior to the average service life. Though a minority of the assets will be in service for a long time, the majority of the assets are retired prior to the average life of the group. see: front-end loading.
  • Positive Skewness - A probability distribution curve with a positive skewness
  • The mean is smaller than the median and mode of the curve.
  • Negative aging - The longer the component has been in service, the less likely it is to fail. Here, the hazard function decreases for larger values of t. This makes sense when individual components vary in quality: poorly made components usually fail early, so anything that has been in service for a long time is likely to be particularly robust and will usually survive even longer. function decreases for larger values of t. 

Examples
Listed below are some examples of some assets that are generally considered to be left-modal:
  • Wood siding, which is heavily dependent on exposure conditions and regular protective recoating cycles.


Generic left-modal curve (with positive skewness).
Fig. Generic left-modal curve (with positive skewness).


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


Comparison of right-modal and left-modal survivor curves
Fig. Comparison of right-modal ("green") and left-modal ("blue") survivor curves.


1
Fig. Example of a left modal curve applied to an asset.

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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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