Hybrid modeling based double-granularity fault detection and diagnosis for quadrotor helicopter |
| |
| Institution: | 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210017, China;1. Department of Operations Research and Financial Engineering, Princeton University, United States;2. ICTEAM Institute, Université catholique de Louvain, Belgium;1. Faculty of Mathematics and Mechanics, St. Petersburg State University, Universitetsky av. 28, Stary Peterhof, 198504, St. Petersburg, Russia;2. Department of Information Technology, Uppsala University, Lägerhyddsvägen 2, SE-751 05, Uppsala, Sweden;3. Department of Computer Science, Southwest State University, 50 Years of October Str. 94, 305040, Kursk, Russia;1. Dipartimento di Scienze Matematiche Informatiche e Fisiche, Università di Udine, 33100 Udine, Italy;2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy;3. IEIIT-CNR, 20133 Milano, Italy;4. School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW 2308, Australia;1. Priority Research Centre for Complex Dynamic Systems and Control (CDSC), School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan NSW 2308, Australia;2. Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO), Energy Flagship, Newcastle, NSW 2300, Australia;3. CIFASIS-CONICET, Departamento de Control, Facultad de Ciencias Exactas Ingeniería y Agrimensura, Universidad Nacional de Rosario, Argentina;1. Kingston University, SEC Faculty, Friars Avenue, London SW15 3DW, United Kingdom;2. Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thessaloniki 57001, Greece |
| |
| Abstract: | Fault detection and diagnosis (FDD) is an effective technology to assure the safety and reliability of quadrotor helicopters. However, there are still some unsolved problems in the existing FDD methods, such as the trade-offs between the accuracy and complexity of system models used for FDD, and the rarely explored structure faults in quadrotor helicopters. In this paper, a double-granularity FDD method is proposed based on the hybrid modeling of a quadrotor helicopter which has been developed in authors’ previous work. The hybrid model consists of a prior model and a set of non-parametric models. The coarse-granularity-level FDD is built on the prior model which can isolate the faulty channel(s); while the fine-granularity-level FDD is built on the nonparametric models which can isolate the faulty components in the faulty channel. In both coarse and fine granularity FDD procedures, principal component analysis (PCA) is adopted for online fault detection. Using such a double-granularity scheme, the proposed FDD method has inherent ability in detecting and diagnosing structure faults or failures in quadrotor helicopters. Experimental results conducted on a 3-DOF hover platform can demonstrate the feasibility and effectiveness of the proposed hybrid modeling technique and the hybrid model based FDD method. |
| |
| Keywords: | Hybrid modeling Fault detection and diagnosis Quadrotor helicopter Structure fault |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
