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

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.

Industry 4.0: technology spillover impact on digital manufacturing industry

“…The main content of industry 4.0 includes the three aspects: firstly, the physical information system should be possessed in the process of realizing industry 4.0, which can play the role of technical support and help the integration of physical and network systems. Several technologies have been proposed to unfold the Industry 4.0 umbrella: big data and analytics (Cai et al , 2019; Zhang et al , 2018), autonomous robots, simulation (Buzys et al , 2018; Wei et al , 2017), Internet of Things (IoT) (Han et al , 2020; Lin et al , 2015), cybersecurity (Wei-Gang et al , 2013; Wu et al , 2013), cloud computing (Chen et al ., 2016a, b), additive manufacturing and augmented reality (Shi et al , 2016), Radio Frequency Identification (RFID) (Li et al , 2018; Wang et al , 2018) and real-time location system (Wang et al , 2020; Yang et al , 2020) technologies. Secondly, intelligent production refers to manufacturing with the help of new intelligent manufacturing technology, similar to 3D printing, human–computer interaction, etc.…”