Opening the Black Box


A new approach to utility asset management.

A new approach to utility asset management.

Fortnightly Magazine - January 2014
Figure 5 - State-Dependent Probability of Asset Failure

Figure 2. However, it turns out that Figure 2 illustrates the flaws of commonly applied methods. The reason is that Figures 1 and 2 can’t be used in practice to find the optimal retirement age for an asset. In other words, one can’t simply construct the two curves and read off the optimal retirement age. Yet, this is commonly done, based on four incorrect assumptions: 1) the time interval between replacements is always the same; 2) all replacement life-cycles cost the same; 3) the actual timing of asset replacements within each replacement life cycle is always the same; and 4) the actual capital costs of asset replacement due to unplanned failures aren’t considered, leading to underestimates of actual capital costs. 

Some Common Mistakes

We have also identified at least six common types of errors present in many commonly applied asset management methodologies (sometimes also called “repair or replace”) that lead to inferior solutions. These common errors include: 1) ignoring or wrongly defining the initial conditions of assets being evaluated; 2) using a misleading concept of “asset health” to lump different classes of assets together; 3) applying a static method ( i.e., one that doesn’t recognize how the condition of an asset changes over time), based on asset health, to determine how to treat an asset; 4) conflating asset condition with the consequences of asset failure; 5) failing to account for all of the costs of asset failure; and 6) failing to integrate testing policies into an overall asset management strategy. 

Any method that fails to assess the initial condition of assets, or assesses them incorrectly, can’t possibly identify an appropriate management strategy and, as a consequence, will be ad hoc.

Figure 6 - Policy Model

Consider, for example, wooden utility poles. Unless a pole has fallen over or is leaning precipitously, it’s difficult to determine its condition. A pole might look fine on the outside, but be rotten inside, awaiting the next windstorm or errant automobile to knock it over. A pole replacement strategy based on whether the pole looks “good” on the outside, regardless of its true internal condition, will lead to excessive pole failures, more outages, and higher costs.

And a wooden utility pole tested and found rotten is far more likely to fail at any given time. That is, asset condition determines the likelihood of future failure. This likelihood is known as a “condition-dependent hazard rate.” Although a common-sense way to characterize the so-called “health” of an asset is to measure its remaining life, this straightforward idea has been expanded to include many other aspects of an asset into a single measure called “asset health.” However, it turns out that the optimal repair-replace strategies for assets having the same health can be quite different. 

Typically, asset health measures combine several distinct attributes, such as age and near-term failure likelihood, into a single measure. However, such a single measure can be misleading because different assets with different attributes could have the same asset health. For example, an older, well-maintained transformer, for example, might have a much lower hazard rate than a younger, poorly-maintained one. Thus,

Figure 7 - Simplified Decision Tree: Transformer Strategy