کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
560956 | 875232 | 2006 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modified self-organising map for automated novelty detection applied to vibration signal monitoring
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Modified self-organising map for automated novelty detection applied to vibration signal monitoring Modified self-organising map for automated novelty detection applied to vibration signal monitoring](/preview/png/560956.png)
چکیده انگلیسی
This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 20, Issue 3, April 2006, Pages 593–610
Journal: Mechanical Systems and Signal Processing - Volume 20, Issue 3, April 2006, Pages 593–610
نویسندگان
M.L.D. Wong, L.B. Jack, A.K. Nandi,