کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1888522 1533646 2015 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets
ترجمه فارسی عنوان
شناسایی خطای اولیه ماشین آلات چرخش با استفاده از ویژگی های ارتعاشی هرج و مرج استخراج مجموعه داده های تجربی
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم فیزیک آماری و غیرخطی
چکیده انگلیسی

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chaos, Solitons & Fractals - Volume 78, September 2015, Pages 61–75
نویسندگان
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