Abstract
Time-varying operational modal analysis (OMA) can identify the transient modal parameters for the linear time-varying (LTV) structures only from the time domain nonstationary vibration response signal measured by vibration response sensors. However, because large-scale sensors data poses significant problems for data processing and storage, methods with excessive computation time and memory requirements are unsuitable for online, real-time health monitoring and fault diagnosis. Recently, the emergence of sensor-cloud greatly improves the computing power and storage capacity of traditional wireless sensor networks by combining cloud computing. Therefore, sensor-cloud can be used to deal with data problems in OMA: the wireless sensor networks layer is used to collect data and the calculations are performed on the cloud computing platform. Furthermore, a limited memory eigenvector recursive principal component analysis (LMERPCA) based OMA method is designed to reduce the runtime and memory requirements and facilitate online process in conjunction with the cloud computing. This approach combines moving window technology and eigenvector recursive principal component analysis method and can identify the transient natural frequencies and modal shapes of slow LTV structures online and in real time. Finally, modal identification results from a cantilever beam with weakly damped and slowly time-varying density show that the LMERPCA-based OMA can identify the transient modal parameters online. Compared with limited memory principal component analysis (LMPCA)-based OMA, the LMERPCA-based approach has a faster runtime, lower memory space requirements, higher identification accuracy, and greater stability.
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This work has been financially supported by the National Natural Science Foundation of China (Grant Nos. 51305142, 51305143).
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Wang, C., Huang, H., Zhang, T., Chen, J. (2019). Limited Memory Eigenvector Recursive Principal Component Analysis in Sensor-Cloud Based Adaptive Operational Modal Online Identification. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_10
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