Remaining useful life (RUL) estimations obtained from a prognostics and health monitoring (PHM) system can be used to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The proposed methodology combines system architecture information and RUL estimations for all components in the system under study, allowing the estimation of an overall system-level RUL (S-RUL). The S-RUL information can be used to support maintenance decisions regarding the replacement of multiple components. For this purpose, the decision problem can be cast into an optimization framework involving the minimization of the component replacement cost under a safety constraint. Two case studies are used to illustrate the S-RUL concept, as well as the proposed optimization methodology.

In this paper, we develop an adaptive scheme based on reinforcement learning (RL) for planning the construction tasks using a quadrotor. Moreover, an autonomous construction system to assemble user-specified 3-D structures is proposed. Nowadays, complex construction tasks using mobile robots are characterized by three fundamental problems: assembly planning, motion planning, and path tracking control. The high-level plan to perform the construction task consists of assembly mode algorithms that are derived offline in a simulation environment through learning and heuristic search. A promising approach to design and optimize the path tracking controllers for a quadrotor as well as the attitude controllers using RL is presented. This paper describes a comprehensive validation framework that enables an aerial robot to build structures in a robust and safe manner. The experimental trials for building the 3-D structures using the designed high-level plans and path tracking controllers have provided encouraging results.

In this work a simple neural network with asymmetric basis functions is proposed as a feature extractor for P waves in electrocardiographic signals (ECG). The neural network is trained using the classical backward-error-propagation algorithm. The performance of the proposed network was tested using actual ECG signals and compared with other types of neural feature extractors.