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Project: T153700 #3263
 

Rigorous maintenance practices for overhead lines not only extend an asset’s life, but can also detect damage conditions early to avoid a premature failure, thus avoiding forced outages. Traditional time-based maintenance practices collect inspection data at a fixed time interval and execute maintenance decisions based on the processing of asset condition data, which are primarily deterministic.

This report uses a stochastic model (Markov chain model) to apply asset condition data; this model considers the uncertainty in the deterioration rate, which is determined from a transitional probability matrix (TPM) of the condition data. The optimum maintenance interval is then determined by using a survival curve derived from the deterioration rate and by balancing the cost of maintenance against the cost of replacement (cost of failure). The optimization model uses a Weibull distribution in which the two key parameters are determined from the survival curve developed from the Markov chain model. Two example problems are presented: the first illustrates the methodology while the second presents a non-dimensional chart of cost-ratio (cost of maintenance to cost of replacement/failure) against the maintenance interval. The second example problem shows not only that the maintenance activity is highly cost-effective when the cost of maintenance is considerably lower than the expected cost of failure, but also that the determined interval is in line with the industry’s best practices.

Keywords:

inspection of line, deterioration model, asset condition rating, Markov chain model, transitional probability, Weibull analysis, optimum maintenance interval