Recent advances in data-driven dynamics and control
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Recent advances in data‑driven dynamics and control Zhi‑Sai Ma1,2 · Xiang Li3 · Meng‑Xin He1,2 · Su Jia1,2 · Qiang Yin1,2 · Qian Ding1,2 Received: 1 July 2020 / Revised: 29 July 2020 / Accepted: 1 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the rapid development of artificial intelligence, the data-driven methods have been extensively applied in the fields of machinery, power, civil engineering, transportation and other industries, such as fault diagnosis and prognosis. Whereas the data-driven based accurate dynamic modeling, system identification, and their applications in the design of engineering equipment and structures and health management for life-cycle process are still in the early stage. Currently, researches on nonlinear dynamics depend mainly on traditional methods of analysis. For the further advancement of the field of dynamics and control in the era of artificial intelligence, it is urgent to discover new knowledge representations and new predictions of complex dynamic laws from the observed data. The future research of dynamics and control must be driven by both the physical principles as well as data. This paper introduces some recent research work by our group in structural optimization, active vibration control, system identification, fault diagnosis and prognosis, and state identification of heart rate variability signal, by using data-driven methods. Keywords Data-driven dynamics and control · Structural optimization · Active vibration control · System identification · Fault diagnosis and prognosis · Heart rate variability analysis
1 Introduction Generally, data driven refers to a process or activity that is spurred on by data, as opposed to being driven by mere intuition or personal experience, or model-based analyses in science and technology community. With the advent of communication and sensor technologies, it is quite convenient to measure input–output data of engineering systems operated in complex environment. Because data is now easier to gather and inexpensive to store, big data analytics is gaining more ground as the best tool for decision making in the business, science and technology fields. This paper reports recent advances of data-driven engineering research in dynamics and control using various methods of machine learning. * Qian Ding [email protected] 1
Department of Mechanics, Tianjin University, Tianjin 300350, China
2
Tianjin Key Laboratory of Nonlinear Dynamics and Control, Tianjin 300350, China
3
College of Sciences, Northeastern University, Shenyang 110819, China
Machine learning (ML), also known as statistical machine learning, is a branch of data science and artificial intelligence. Its basic idea is to build statistical model based on data, and use the model to analyze and predict data. Similar to artificial intelligence, ML is an interdisciplinary field, involving many basic disciplines including statistics, linear algebra and numerical calculation. ML can be divided into supervised learning, unsupervised
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