کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11024192 | 1701176 | 2019 | 44 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel weak-fault detection technique for rolling element bearing based on vibrational resonance
ترجمه فارسی عنوان
یک تکنیک تشخیص خطای ضعیف بر اساس بلبرینگ بر اساس رزونانس ارتعاش
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
چکیده انگلیسی
Weak fault detection, as a key step in the condition-based maintenance, is a signiï¬cant but diï¬cult issue because the fault signals are usually submerged in strong background noise. Contrary to traditional denoising and ï¬ltering methods, vibrational resonance (VR), as well as stochastic resonance (SR), is an eï¬ective way to detect weak signals by utilizing high-frequency interferences or random noise on purpose. In this paper, we investigate the application of VR to weak bearing fault detection. In order to enhance the detection performance, we construct an array of bistable systems based on VR by injecting diï¬erent high-frequency sinusoidal interferences. Considering the frequency of fault signal which is usually greater than 1â¯Hz in practice, the frequency-shifted and rescaling transform method is adopted. Levenberg-Marquardt algorithm is utilized to optimize the system parameters, which is diï¬erent from the most existing evolutionary algorithms. The proposed VR-based method is validated by simulation data, bearing data with single implanted fault and bearing data with multiple naturally-developed faults. The experimental results show that, compared with bistable SR system, this method by using an array of bistable systems based on VR is more practical to enhance the detection performance of bearing weak faults.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Sound and Vibration - Volume 438, 6 January 2019, Pages 490-505
Journal: Journal of Sound and Vibration - Volume 438, 6 January 2019, Pages 490-505
نویسندگان
Lei Xiao, Xinghui Zhang, Siliang Lu, Tangbin Xia, Lifeng Xi,