An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things. (February 2020)

• Record Type: Journal Article
• Authors: Wang, Cheng; Huang, Haiyang; Chen, Jianwei; Wei, Wei; Wang, Tian
• Is Part Of: International journal of distributed sensor networks. Volume 16:Number 2(2020)
• Publication Date: 2020-02
• Subjects: Fog computing -- Internet of Things -- adaptive operational modal analysis -- eigenvector recursive principal component analysis -- limited memory -- slow linear time-varying -- non-stationary random response -- online and real time; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147720903610
• Languages: English
• ISSNs: 1550-1329

Abstract

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.