Abstract
The reliability of power transformers is subject to service age and health condition. This paper proposes a practical model for the evaluation of two reliability indices: survival function (SF) and mean residual life (MRL). In the proposed model, the periodical modeling of power transformers are considered for collecting the information on health conditions. The corresponding health condition is assumed to follow a continuous semi-Markov process for representing a state transition. The proportional hazard model (PHM) is introduced to incorporate service age and health condition into hazard rate. In addition, the proposed model derives the analytical formulas for and offers the analytical evaluation of SF and MRL. SF and MRL are calculated for new components and old components, respectively. In both cases, the proposed model offers rational results which are compared with those obtained from comparative models. The results obtained by the contrast of the proposed analytical method and the Monte Carlo method. The impact of different model parameters and the coefficient of variation (CV) on reliability indices are discussed in the case studies.
Highlights
The equipment reliability is subject to degradation and influencing factors which are referred to as covariates
We develop analytical formulas based on a more realistic DMCT model for evaluating the equipment reliability of deteriorating systems
The minimal errors between analytical formulas and Monte Carlo results imply the accuracy of the proposed method
Summary
The equipment reliability is subject to degradation and influencing factors which are referred to as covariates. The MRL and conditional/unconditional SFs are calculated in [12] by a discrete Markov process and PHM for obtaining the additional insight on interactions between time-varying failure rates and reliability indices. The above models are based on the assumption that the condition information of covariates is inspected at discrete points and every state transition happens only at the end of inspection interval, exactly before the inspection instant, to make the calculation tractable within every interval. The online condition monitoring of power transformers, such as dissolved gas analysis, is discrete (periodical) while the state transition could happen at any time [20, 21]. The main contributions of this paper offered by our model are summarized below: 1) The proposed model is based on more practical assumptions in which the condition is discretely inspected but the state transition is continuous. The effectiveness of the proposed formulas is shown in our numerical studies
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