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ABSTRACT Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio. 

Abstract This paper proposes a novel adaptive maintenance policy for degrading systems subject to hard failure. Compared with traditional condition‐based maintenance policies, the proposed predictive maintenance policy makes maintenance decisions adaptively based on model prognostic results. The prognostic model is continuously updated based on newly inspected data. The inspection times and preventive maintenance activities are scheduled online in a sequential manner based on the most current prediction of system reliability. A computationally efficient optimization scheme is proposed for obtaining optimal maintenance parameters. The proposed policy is demonstrated and its performance is evaluated through extensive simulations. 

The battery-powered wireless sensor network (WSN) is a promising solution for structural health monitoring (SHM) applications because of its low cost and easy installation capability. However, the long-term WSN operation suffers from various concerns related to uneven battery degradation of wireless sensors, associated battery management, and replacement requirement, and ensuring desired quality of service (QoS) of the WSN in practice. The battery life is one of the biggest limiting factors for long-term WSN operation. Considering the costly maintenance trips for battery replacement, a lack of effective battery degradation management at the system level can lead to a failure in WSN operation. Moreover, the QoS needs to be ensured under various practical uncertainties. Optimal selection with a maximal number of nodes in WSN under uncertainties is a critical task to ensure the desired QoS. This study proposes a reinforcement learning (RL) based framework for active control of the battery degradation at the WSN system level with the aim of the battery group replacement while extending the service life and ensuring the QoS of WSN. A comprehensive simulation environment was developed in a real-life WSN setup, i.e. WSN for a cable-stayed bridge SHM, considering various practical uncertainties. The RL agent was trained under a developed RL environment to learn optimal nodes and duty cycles, meanwhile managing battery health at the network level. In this study, a mode shape-based quality index is proposed for the demonstration. The training and test results showed the prominence of the proposed framework in achieving effective battery health management of the WSN for SHM.