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.
Highlights
In Internet of Things (IoT), enormous sensors and smart devices are embedded in hospitals, railways, bridges, tunnels, roads, buildings, dams, and other objects to collect data, allowing the connection between objects
We propose a framework to combine fog computing with limited memory eigenvector recursive principal component analysis (LMERPCA)-based Operational modal analysis (OMA)
After the eigenvectors and principal components (PCs) are obtained by the LMERPCA algorithm, the modal parameters can be limited memory principal component analysis (LMPCA)-based OMA limited memory recursive principal component analysis (LMRPCA)-based OMA LMERPCA-based OMA
Summary
In Internet of Things (IoT), enormous sensors and smart devices are embedded in hospitals, railways, bridges, tunnels, roads, buildings, dams, and other objects to collect data, allowing the connection between objects. To reduce the time and space complexity of the algorithm, a limited memory eigenvector recursive principal component analysis (LMERPCA)-based approach is designed. This OMA method uses a rank 1 matrix for eigenvector recursion to identify modal parameters for LTV structure and achieves the time and space complexity of O(N).[38] An earlier version combined LMERPCA with cloud computing and was presented at the SCS2019 conference.[39] This article designs a new architecture to combine LMERPCA with fog computing. ERPCA uses the rank-matrix to recursively obtain new eigenvectors and principal components (PCs) that correspond modal parameters.[39] This recursive way greatly reduces the complexity of time and space. After the eigenvectors and PCs are obtained by the LMERPCA algorithm, the modal parameters can be
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