دانلود مقاله ISI انگلیسی شماره 54073
عنوان فارسی مقاله

استخراج اتوماتیک ویژگی خطای ناهنجاری مکانیکی بر روی بلبرینگ موتور القایی با استفاده از تبدیل موجک فوق العاده آنسامبل

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
54073 2015 24 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Mechanical Systems and Signal Processing, Volumes 54–55, March 2015, Pages 457–480

کلمات کلیدی
موتور القایی؛ استخراج ویژگی؛ تشخیص خطای بلبرینگ ؛ تبدیل موجک فوق العاده ؛ ضریب Q
پیش نمایش مقاله
پیش نمایش مقاله استخراج اتوماتیک ویژگی خطای ناهنجاری مکانیکی بر روی بلبرینگ موتور القایی با استفاده از تبدیل موجک فوق العاده آنسامبل

چکیده انگلیسی

Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.