تشخیص خطا دستگاه های چرخشی
ترجمه نشده

تشخیص خطا دستگاه های چرخشی

عنوان فارسی مقاله: تشخیص خطا دستگاه های چرخشی بر اساس ترکیبی از شبکه باور عمیق و شبکه عصبی پیچشی تک بعدی
عنوان انگلیسی مقاله: Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات، مهندسی برق
گرایش های تحصیلی مرتبط: هوش مصنوعی، شبکه های کامپیوتری، مهندسی کنترل
کلمات کلیدی فارسی: شبکه باور عمیق، شبکه عصبی پیچشی تک بعدی، دستگاه های چرخشی، استخراج ویژگی، تشخیص خطا هوشمند
کلمات کلیدی انگلیسی: Deep belief network (DBN), one-dimensional convolutional neural network (1D-CNN), rotating machinery, feature extraction, intelligent fault diagnosis
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2953490
دانشگاه: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 14
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14028
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Basic Principle of the Proposed Method


III. Experimental Results


IV. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


The traditional intelligent diagnosis methods of rotating machinery generally require feature extraction of the raw signals in advance. However, it is a very time-consuming and laborious process for extracting the sensitive feature information to improve classification performance. Deep learning method, as a novel machine learning approach, can simultaneously achieve feature extraction and pattern classification. With the characteristics of Deep Belief Network (DBN) and one-dimensional Convolutional Neural Network (1D-CNN) (e.g. learning complex nonlinear, sparse connection and weight sharing), a precise diagnosis method based on the combination of DBN and 1D-CNN is proposed. Firstly, the DBN composed of three pre-trained restricted Boltzmann machines (RBMs) is constructed to achieve feature extraction and dimensionality reduction of the high-dimensional raw data. Secondly, the low-dimensional features extracted by DBN are fed into 1D-CNN for further extracting the abstract features. Finally, Soft-max classifier is employed to identify different operating conditions of rotating machinery. The superiority of the proposed method is validated by comparison with several state-of-the art fault diagnosis methods on two experimental cases. Meanwhile, the proposed method is tested in different background noises and on the imbalanced datasets. The results show that it has higher efficiency and accuracy than the state-of-the art fault diagnosis methods.


Introduction


With the rapid development of science and technology, rotating machinery in modern industry has been moving toward high speed, super precision and high efficiency [1], [2]. After a long-term operating in the complex working environment, the core components of rotating machinery, including gears and bearings, are prone to cause various unperceivable faults. If not detected and managed, these failures may affect the operation of the whole rotating machinery and cause huge economic losses to enterprises [3]–[۵]. Therefore, It’s urgent for us to develop some advanced diagnosis methods, which can accurately and efficiently detect the potential faults of the key components of rotating machinery [6], [7]. At present, there are many methods used in fault diagnosis of rotating machinery, including oil debris analysis, electrical signature analysis, acoustic emission detection, vibration signal analysis, temperature analysis and so on [8]. In contrast with the other approaches, the vibration signal analysis is more common, and the relevant researches are more mature [9], [10]. Additionally, the vibration signals of rotating machinery usually carry more valuable information. A complete fault diagnosis method based on pattern recognition consists of three steps: signal preprocessing [11], feature extraction [12] and pattern classification [13]. Each step has a critical impact on the final recognition accuracies of the model [14].

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