کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4976752 | 1451836 | 2018 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A deviation based assessment methodology for multiple machine health patterns classification and fault detection
ترجمه فارسی عنوان
یک روش ارزیابی مبتنی بر انحراف برای طبقه بندی الگوهای چندگانه ماشین و تشخیص خطا
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کلمات کلیدی
پیشگیری و مدیریت سلامت، نیمه هادی، یاتاقان، توربین بادی، تجزیه و تحلیل مولفه اصلی، نقشه نفوذ،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 99, 15 January 2018, Pages 244-261
Journal: Mechanical Systems and Signal Processing - Volume 99, 15 January 2018, Pages 244-261
نویسندگان
Xiaodong Jia, Chao Jin, Matt Buzza, Yuan Di, David Siegel, Jay Lee,