Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals

作者：

Highlights：

• A structural health monitoring (SHM) strategy is presented for analysis of delayed signals.

• An expert system using a data mining method is developed for data-driven SHM.

• Machine learning is merged with pattern recognition and a probabilistic approach.

• A probabilistic approach is developed to reconstruct delayed and noisy signals.

• The developed data mining method is evaluated for a plate through experimental tests.

摘要

•A structural health monitoring (SHM) strategy is presented for analysis of delayed signals.•An expert system using a data mining method is developed for data-driven SHM.•Machine learning is merged with pattern recognition and a probabilistic approach.•A probabilistic approach is developed to reconstruct delayed and noisy signals.•The developed data mining method is evaluated for a plate through experimental tests.

论文关键词：Structural health monitoring,Data mining,Artificial intelligence,Probabilistic approach,Signal time delay

论文评审过程：Received 14 August 2018, Revised 7 April 2019, Accepted 29 May 2019, Available online 31 May 2019, Version of Record 14 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.051