Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learningbased approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a deterministic value and hence cannot convey uncertainty in the RUL prediction. This practice usually provides overly confident predictions that might cause severe consequences in safetycritical industries. To address this issue, this paper proposes a probabilistic Bayesian recurrent neural network (RNN) for RUL prognostics considering epistemic and aleatory uncertainties. The epistemic uncertainty is handled by Bayesian RNN layers as extensions from the Frequentist RNN layers using the Flipout method. The aleatory uncertainty is covered by a probabilistic output that follows a Gaussian distribution parameterized by the two neurons in the output layer. The network is trained using Bayes by backprop with the Flipout method. The proposed model is demonstrated by the open-access Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engines and a comparative study of the Frequentist RNN counterparts, the Monte Carlo Dropout-based RNN, and the state-of-the-art models for C-MAPSS datasets. The results demonstrate the promising performance and robustness of the proposed model in RUL prognostics.

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Esta investigación fue presentada en 2014 como tesis para el programa de Magíster en Ética Social y Desarrollo Humano de la Universidad Alberto Hurtado de Chile. Toma como ejes de desarrollo temático la articulación entre derechos humanos y políticas públicas, planteando la pregunta por el fundamento ético de la práctica social implicada en la implementación de los programas del Estado hacia la infancia. Expone la experiencia de un equipo psicosocial que se dio a la tarea de cuestionar su coherencia ética entre discurso y práctica, y el resultado al que llevo este ejercicio de reflexión crítica, el cual no es otro que un modelo de acción social fundamentado auténticamente en el paradigma de derechos humanos.