Sensor Placement with Multiple Objectives for Structural Health Monitoring

Abstract

Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop "connectivity trees" in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

An Entropy Based Bayesian Network Framework for System Health Monitoring

“…As can be seen, higher budgets permit higher reliability (Model 3 rather than 2 or 1). (3,3), (4, 2), (5, 2), (6, 3), (7,3), (8,3), (9,3), (10,1), (11,3), (12,3), (13,3) 0.285 Figure 8 illustrates the selected optimal sensor locations (from Figure 7 and Table 6) with respect to the BN. It can be seen that the optimization methodology preferences sensors at the third layer.…”

“…However, practicality and cost limits sensing and monitoring, which in turn restricts data availability for health monitoring. This presents itself as a multi-objective sensor selection optimization problem involving the number, location, and type of sensors for a given pipeline network [1]. This paper outlines a sensor selection optimization methodology that leverages the concept of information entropy within a Bayesian framework for system modeling and health monitoring.…”

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

“…Wireless sensor networks (WSNs) in diverse applications generate huge volumes of dynamic, geographically distributed and heterogeneous data [17,5]. Data mining techniques may play a vital role in efficiently extracting and analysing usable information from the raw data to facilitate automated or human induced decision making.…”

Energy-Efficient Heterogeneous Wireless Sensor Deployment with Multiple Objectives for Structural Health Monitoring

“…In addition to these existing OSD techniques designed for modal parameter estimation in wired SHM systems, a handful of works have attempted to address the OSD issues for wireless sensor networks. For instance, Bhuiyan et al [18,19] proposed a three-phase sensor placement method for SHM that addressed the quality of sensor placements, communication efficiency, and fault tolerance. Onoufriou et al [20] presented a two-step strategy to optimize the number of sensors and their locations to satisfy both specific structural engineering requirements and energy constraint imposed by a WSN.…”