کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495803 862839 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture
چکیده انگلیسی


• A new intelligent bearing fault diagnosis method is proposed.
• Sparsity measurement is used as objective cost function for optimization.
• Parameters of a complex Morlet wavelet filter are optimized by simplex-simulated annealing.
• Sparse wavelet coefficients are extracted by optimal complex Morlet wavelet filtering.
• A new adaptive local maximum selection method is proposed to enhance cyclic impulsive features.

Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 10, October 2013, Pages 4097–4104
نویسندگان
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