Joshua Perkel

Equipment Failure Forecasting Based on Past Failure Performance and Development of Replacement Strategies

When only partial information is available about equipment failures (installation date and amount, as well as failure and replacement rates), data on sufficiently large number of yearly populations of the components can be combined, and estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system`s short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

New Probabilistic Method for Estimation of Equipment Failures and Development of Replacement Strategies

When large amount of statistical information about power system component failure rate is available, statistical parametric models can be developed for predictive maintenance. Often times, only partial information is available: installation date and amount, as well as failure and replacement rates. By combining sufficiently large number of yearly populations of the components, estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system’s short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

On Distribution Asset Management: Development of Replacement Strategies

The components of electricity networks are ageing. It is expected that within a horizon of 15 years, the performance will deteriorate significantly, while the costs for operating the networks will increase enormously. The main problem is that a significant part of the population of the assets is installed in the same period, resulting in a highly concentrated number of failures in a short time. The currently applied replacement strategy has to be revisited, in order to accommodate the effects of ageing assets: higher maintenance costs, high failure rates, and a steep increase of capital expenditure (CAPEX).

Generating Cable Replacement Strategies Using Monte Carlo Simulation

The paper describes a simple replacement strategy developed on the basis of statistical failure parameter identification and a predictive replacement algorithm. The proposed approach is illustrated with examples showing both the effectiveness and limitations of the method