Performance comparison of various Decision-Based Neural Networks ingearbox fault diagnosis
- سال انتشار: 1392
- محل انتشار: کنفرانس دو سالانه بین المللی مکانیک جامدات تجربی
- کد COI اختصاصی: WMECH02_166
- زبان مقاله: انگلیسی
- تعداد مشاهده: 783
نویسندگان
MSc Student
Associate Professor
Professor, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polythchnic), Hafez Ave., ۱۵۸۷۵-۴۴۱۳, Tehran, Iran,
MSc graduate, Acoustics Research Laboratory, Department of Mechanical Engineering, Amirkabir University of Technology (Tehran Polythchnic), Hafez Ave., ۱۵۸۷۵-۴۴۱۳, Tehran, Iran.
چکیده
Condition monitoring and fault diagnosis using intelligent systems has been of interest to researchers in recent decades. This paper studies the discrimination of faulty and healthy gears using artificial intelligence. Worn and chipped gears are considered for faulty data. Diagnosis process can be divided into three main phases: Empirical data collection, feature extraction and classification. Vibration signal obtained from the three accelerometers embedded in certain parts of the gearbox test setup, are inputs of the model. The experiments are done on a Samand 5-speed manual gearbox test setup in Acoustics Research Lab of Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic). In feature extraction phase, statistical properties of wavelet coefficients are the most prominent characteristic being used. Performance of different artificial neural network configurations such as Linear Perceptron and hierarchical Decision Based Neural Networks (DBNN) is compared in the classification phase. The effect of Linear Basis Function (LBF) and Radial Basis Function (RBF) in different structures of neural network is also considered. The judgment is based on different point of views, such as rate of convergence, correct classification percentage and so on. It’s been shown that Subcluster structure has better classification capability than Linear Perceptron Networks.کلیدواژه ها
Fault diagnosis; Intelligent systems; Wavelet Packet Decomposition; Decision Based Neural Network; Gearboxمقالات مرتبط جدید
- Analytical determination of flexural rigidity of thin-walled pultruded composite I-beams
- Matrix-Dominated Damage Process in CFRP Composite Laminates underFlexural Loading Condition
- Indentation Size Scale Effect on Mechanical Properties of Cold SprayCoating
- Determination of Forming Limit Diagrams for Tailor Welded Blanks
- Control of dynamic building façade: design, analysis and experiments
اطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.