A review on machinery diagnostics and prognostics implementing condition-based maintenance

Infrared thermography for condition monitoring – A review

Determining whether power transformers are suffering from internal arcing or dangerous levels of partial discharge (PD) is important because failure without warning can result in damage to neighboring equipment, customer dissatisfaction, disruption to economic activity, and the imposition of regulatory fines. This paper provides an overview of the excitation of UHF signals by PD inside transformers. The use of externally mounted sensors has been outlined, and the structure of a dielectric window that can be constructed on an inspection hatch has been outlined. Attenuation of UHF signals propagating inside a transformer tank has been shown to be relatively low. A new approach to locating PD sources in three dimensions has been presented, based on using a numerical model of the transformer materials, which defines electromagnetic propagation velocities on a mesh of 5-cm sub-cells. The concepts of the propagation-velocity matrix (PVM) and the propagation-time matrix (PTM) were thereby introduced. Once the PD source has been located to a specific region of the transformer, the PVM might be used to index a database of physical descriptions of the transformer that could provide additional information to assist with diagnosis.

Partial discharge monitoring of power transformers using UHF sensors. Part I: sensors and signal interpretation

The Health Index represents a practical tool that combines the results of operating observations, field inspections, and site and laboratory testing to manage the asset and prioritize investments in capital and maintenance plans.

An approach to power transformer asset management using health index

The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust, i.e., it may be less accurate when the real data acquired after the deployment differs from the testing data; (ii) it wastes the resources for constructing the algorithms that are discarded in the deployment; (iii) it requires the testing data in addition to the training data, which increases the overall expenses for the algorithm selection. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely, the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms for data-driven prognostics. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

As transformers age, their internal condition degrades, which increases the risk of failure. To prevent these failures and to maintain transformers in good operating condition is a very important issue for utilities. Traditionally, routine preventative maintenance programs combined with regular testing were used. The change to condition-based maintenance has resulted in the reduction, or even elimination, of routine time-based maintenance. Instead of doing maintenance at a regular interval, maintenance is only carried out if the condition of the equipment requires it. Hence, there is an increasing need for better nonintrusive diagnostic and monitoring tools to assess the internal condition of the transformers. If there is a problem, the transformer can then be repaired or replaced before it fails. An extensive review is given of diagnostic and monitoring tests, and equipment available that assess the condition of power transformers and provide an early warning of potential failure.

A.J. Vandermaar

Papers

Review of condition assessment of power transformers in service