This report looks at the origins and roles of Asset Health Indices (AHIs) and their use in the electric supply industry. It includes examples of systems in use, identifies the shortcomings associated with many AHIs, and suggests how to develop an AHI in a way which preserves calibrated timescales and urgency as well as allows for a derivation of an asset probability of failure (PoF).
The report also includes statistics of failures and failure modes, along with background information on the asset management framework and regulatory drivers associated with the origin and application of asset health indices.
The literature review discusses the problems generated by weighted systems not having a monotonic relationship between the score and actual condition, and thus the AHI not having a clear relationship with the PoF; the AHI must address a specific problem clearly. Furthermore, age should be an indicator of operational stress rather than a direct cause of deterioration. The use of age as a factor in condition calculations, however, is self-fulfilling: if older assets are assumed to be in worse condition and get replaced, the overall population is improved, even if replacements are made at random.
Examples of systems in practice show the influence of weighted system development, particularly using versions of a popular approach: a normalized 1–100 scoring system, where 100 is “as new”. Weighted systems have a tendency to dilute any urgent condition and result in a system that is not monotonic. A worse score is not necessarily a worse asset condition, and any sense of urgency is usually lost in the calculation. The assumption that the weighted score represents an average condition does not take into account that failure does not necessarily occur on average, but rather via particular failure modes. Other systems tend to be more focused on a particular application. Many systems confuse asset condition with consequence of failure and thus become a more general indicator of a risk, as they do not supply either independent PoF or consequence of failure.
A few systems do claim to have a formulaic approach, which moves from Asset Health Index to Probability of Failure. The math and process of these systems are clear, but they are not, in the authors’ opinion, valid or reasonable: this is because there are many unjustified values in the calculations; there are numerous untenable assumptions; and there are many unreferenced statements that lack authority.
An approach that uses the statistics of a population to monotonically rank assets in order of condition may be married to a known and slowly changing population to allow for categories of assets with bounds on their probability of failure, supported by history and industry statistics.
Health Index, Asset, Probability of Failure