Performance-based optimal sensor placement method for single-layer reticulated shells considering modal observability and damage identifiability

Guo, Xiaonong; Guo, Xiaonong; Zeng, Qiang; Zhu, Shaojun; Zong, Shaohan

doi:10.1016/j.tws.2023.110809

Cited by 5 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In tackling the challenge of the optimal sensor placement, researchers have harnessed a diverse array of algorithms and techniques for sensor configuration, encompassing genetic algorithms [7,8], heuristic algorithms [9], optimization algorithms [10], information theory [11,12], and machine learning [13]. This study is motivated by the objective of exploring the optimal positioning of temperature and humidity sensors within pig houses, with the overarching aim of orchestrating their layout in a manner that is both effective and cost-efficient for comprehensive indoor environment monitoring.…”

Section: Introductionmentioning

confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

View full text Add to dashboard Cite

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

show abstract