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Entropy
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Information Theory and Its Application in Machine Condition Monitoring II
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Information Theory and Its Application in Machine Condition Monitoring II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 1844

Special Issue Editors

Dr. Yongbo Li

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Guest Editor
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modelling of mechanical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aijing Lin

E-Mail Website
Guest Editor
School of Science, Beijing Jiaotong University, Beijing 100044, China
Interests: time series analysis; data analysis and mining; complex system analysis; network physiology
Dr. Yuqing Li

E-Mail Website
Guest Editor
Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China
Interests: condition monitoring; signal processing; anomaly detection; fault diagnosis; task optimization; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fengshou Gu

E-Mail Website
Guest Editor
Centre for Efficiency and Performance Engineering (CEPE), Department of Engineering and Technology, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: vibro-impact modelling; machine modelling and fault simulation; neural network modelling; time–frequency and time–scale analysis; modulation and demodulation analysis; complex vibro-acoustic source identification; acoustic condition monitoring; intelligent monitoring system; powerless and wireless data sensing and transfer; tribological dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Xihui (Larry) Liang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: nonlinear dynamics; condition monitoring; predictive maintenance; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) techniques have been rapidly advanced in recent years for promoting the productivity and reliability of large-scale engineering systems. This advancement is greatly impacted by the progress in information theory and computing technologies, as evidenced by many published works in CM fields dealing with topics such as Shannon entropy, Lempel-ziv complexity, and permutation entropy. As a statistical measure, information theory can be used to quantify complexity and detect the dynamic change by taking into account the nonlinear behavior of time series. The information theory can be served as a promising tool to extract the dynamic characteristics of machines, which is useful to develop effective condition monitoring techniques.

The last decade has witnessed an increasingly growing research interest in information theory. This Special Issue aims to provide a platform to present high-quality original research as well as review articles on the latest developments in information theory and its application to machine condition monitoring.

Dr. Yongbo Li
Prof. Dr. Aijing Lin
Prof. Dr. Yuqing Li
Prof. Dr. Fengshou Gu
Dr. Xihui (Larry) Liang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information theory
  • condition monitoring
  • complexity measure
  • symbolic dynamic analysis
  • fault detection, diagnosis and prognosis
  • dynamic change detection
  • machine learning
  • physics driven digital modeling
  • multi-objective optimizations
  • intelligent maintenance

Published Papers (1 paper)

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Research

17 pages, 3763 KiB  
Article
Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis
by Xiaorong Zheng, Zhaojian Gu, Caiming Liu, Jiahao Jiang, Zhiwei He and Mingyu Gao
Entropy 2022, 24(8), 1122; https://doi.org/10.3390/e24081122 - 15 Aug 2022
Viewed by 1170
Abstract
Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space [...] Read more.
Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model. Full article
(This article belongs to the Special Issue Information Theory and Its Application in Machine Condition Monitoring II)
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