مقاله انگلیسی رایگان در مورد تشخیص خطا دستگاه های چرخشی – IEEE 2019

IEEE

 

مشخصات مقاله
ترجمه عنوان 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
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۴ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۴۱ در سال ۲۰۱۸
شاخص H_index ۵۶ در سال ۲۰۱۹
شاخص SJR ۰٫۶۰۹ در سال ۲۰۱۸
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q2 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر، مهندسی فناوری اطلاعات، مهندسی برق
گرایش های مرتبط هوش مصنوعی، شبکه های کامپیوتری، مهندسی کنترل
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
کلمات کلیدی 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
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2953490
کد محصول  E14028
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I. Introduction
II. Basic Principle of the Proposed Method
III. Experimental Results
IV. Conclusion
Authors
Figures
References

 

بخشی از متن 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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