Detecting faulty edges of complex dynamical networks based on compressive sensing |
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| Institution: | 1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China;2. Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot), Nanjing 210023, P.R.China;1. MOE-LCSM, CHP-LCOCS, School of Mathematical Sciences and Statistics, Hunan Normal Univerity, Changsha, 410081, China;2. The Key Laboratory of Control and Optimization of Complex Systems, College of Hunan Province, Hunan Normal University, Changsha 410081, China;1. Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran;2. School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, China;1. Universidad Autónoma de Baja California, Facultad de Ciencias de la Ingeniería, Administrativas y Sociales, Tecate, C.P. 21460, Baja California, Mexico;2. Universidad Nacional Autónoma de México, Instituto de Ingeniería, Coyoacán, Ciudad de México, C.P. 04510, Mexico;3. Instituto Politécnico Nacional, Sección de Estudios de Investigación y Posgrado, ESIME-UPT, Ciudad de México, C.P. 07430, Mexico |
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| Abstract: | As an important technology to improve network reliability, fault diagnosis has gained wide attention in complex dynamical networks. However, few studies focused on detecting the structure of broken edges when faults occur. In this paper, due to the natural sparsity of complex dynamical networks, a completely data-driven method based on compressive sensing is established to detect the structure of faulty edges from limited measurements. The least absolute shrinkage and selection operator algorithm is applied to solve the reconstruction problem. In addition, the method is also applicable to multilayer networks. The faulty edges in both the intralayer network and the interlayer network can be fully identified. Compared with other methods, the main advantages of the proposed method lie in two aspects. First, the structure of faulty edges can be obtained directly with limited measurements. Second, the proposed method is less time consuming and more efficient due to less data processing. Numerical simulations involving single-layer, multilayer and real-world complex dynamical networks are given to demonstrate the accuracy of detecting the structure of faulty edges from the proposed method. |
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