Jose Ignacio Aizpurua

TL;DR: This paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty and results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the predictions accuracy among a number of forecasting approaches.

TL;DR: Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy, as well as integrating deterministic and stochastic operation through piecewise deterministic Markov processes.

TL;DR: An improved approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and stochastic dependencies among events, is proposed.

TL;DR: This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing, and is shown to have a maximum diagnosis accuracy of 88.9%.

TL;DR: This paper presents an online system maintenance method that takes into account the system dynamics and employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets.