کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4924624 | 1430851 | 2016 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Detection of faults in rotating machinery using periodic time-frequency sparsity
ترجمه فارسی عنوان
تشخیص گسل ها در ماشین آلات چرخش با استفاده از اسپارتی فرکانس زمانی
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کلمات کلیدی
ماشین آلات دوار، تشخیص گسل، انزوا زدن گروهی، بهینه سازی غیر محدب،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
چکیده انگلیسی
This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature manifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization-minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show that the proposed approach can effectively detect and extract the periodical oscillatory features.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Sound and Vibration - Volume 382, 10 November 2016, Pages 357-378
Journal: Journal of Sound and Vibration - Volume 382, 10 November 2016, Pages 357-378
نویسندگان
Yin Ding, Wangpeng He, Binqiang Chen, Yanyang Zi, Ivan W. Selesnick,