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Performance Degradation Assessment of Rotary Machinery Based on a Multiscale Tsallis Permutation Entropy Method

Yong Chen, Mian Jiang, Kuanfang He, Yi Qin
2021 Shock and Vibration  
Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.  ...  Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery.  ...  In this section, the health indicators of the rolling bearing were constructed based on the MTPEs of the vibration signals obtained during the process of degradation.  ... 
doi:10.1155/2021/5584327 fatcat:wrzblwhaq5behfxz4ad6gskpwu

Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

Jianmin Zhou, Huijuan Guo, Long Zhang, Qingyao Xu, Hui Li
2016 Shock and Vibration  
For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD).  ...  Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime.  ...  Acknowledgments This work was funded under the Natural Science Foundation of China, Grant no. 51205130. The authors are grateful to all study participants.  ... 
doi:10.1155/2016/3086454 fatcat:rgnv3g232fhnnpxdmupz4xtz7i

A Novel Health Indicator Based on Cointegration for Rolling Bearings' Run-To-Failure Process

Hongru Li, Yaolong Li, He Yu
2019 Sensors  
The extraction of rolling bearings' degradation features has been developed for decades. However, the degradation features always present different trends of different run-to-failure data.  ...  To find a consistent indicator of different data will be helpful to establish a general model and explore the nature of bearings' degradation.  ...  Acknowledgments: We will appreciate the IEEE Reliability Society and FEMTO-ST Institute and the Centre for Intelligent Maintenance System, University of Cincinnati for providing the experimental data.  ... 
doi:10.3390/s19092151 fatcat:ycntl5g57vdfrpc7a5pjrx67oe

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Zhibin Zhao, Jingyao Wu, Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen
2021 Chinese Journal of Mechanical Engineering  
However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and  ...  AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding  ...  Acknowledgements The authors sincerely thanks to Zheng Zhou, Zuogang Shang, Chenye Hu, Hongbing Shang for all their help on this work.  ... 
doi:10.1186/s10033-021-00570-7 fatcat:rih6clm2d5fazdujdymq4jynba

A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time

Zhongjie Shen, Zhengjia He, Xuefeng Chen, Chuang Sun, Zhiwen Liu
2012 Sensors  
This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time.  ...  degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation.  ...  Pan et al. developed three indicators spectral entropy, health index and degradation indicator using information entropy, wavelet packet-support vector data description and lifting wavelet packet decomposition-fussy  ... 
doi:10.3390/s120810109 pmid:23112591 pmcid:PMC3472819 fatcat:r6am6nibkfdmjnsauzxm4u5zim

Table of Contents

2020 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan)  
), and Qizhi Chen (Southwest Jiaotong University) ix On Improving the Monotonicity-Based Evaluation Method for Selecting Features/Health Indicators for Prognostics 242 Agusmian Partogi Ompusunggu  ...  Technology), Wenzhen Yu (Nanjing Research Institute of Electronics Technology), and Jing Luo (Nanjing Research Institute of Electronics Technology) Division of Rolling Bearing Health Stage Based on Multi-domain  ... 
doi:10.1109/phm-jinan48558.2020.00004 fatcat:m2bsdulg2fhqrfj6imw4rd7h5e

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Abdenour Soualhi, Yasmine Hawwari, Kamal Medjaher, Guy Clerc, Razik Hubert, François Guillet
2020 International Journal of Prognostics and Health Management  
This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.  ...  It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring.  ...  Based on the obtained indicators, this module determines whether the state of the monitored system or component is degraded or not and identify the element responsible of this degradation. + Layer 5 -The  ... 
doi:10.36001/ijphm.2018.v9i2.2736 fatcat:7h4jdd3sqjgarf2e7tnqylxbsq

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Adnan Althubaiti, Faris Elasha, Joao Amaral Teixeira
2021 Journal of Vibroengineering  
Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented.  ...  Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new  ...  [5] use CNN as part of their method for determining RUL of rolling element bearings. Specifically, extracted features are fed into a deep CNN to construct a health indicator.  ... 
doi:10.21595/jve.2021.22100 fatcat:erdumydfzvg7bagqjfggy256my

Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics

Chenyu Liu, Alexandre Mauricio, Junyu Qi, Dandan Peng, Konstantinos Gryllias
2020 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
Traditional expert knowledge-based Prognostics and Health Management (PHM) processes can be smartened up with the assistance of various AI techniques, such as deep learning models.  ...  In order to accurately predict the Remaining Useful Life (RUL) during the degradation process, in this paper, a bearing digital twin model is constructed based on a phenomenological vibration model.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the support of the China Scholarship Council, Flanders Make the strategic research centre for the manufacturing industry in the context of DGTwin Prediction  ... 
doi:10.36001/phmconf.2020.v12i1.1294 fatcat:ikpu336defclhhi7rupncifqki

Big Data Opportunities: System Health Monitoring and Management

Kwok Leung Tsui, Yang Zhao, Dong Wang
2019 IEEE Access  
System health monitoring and management (SHMM) refers to the framework of continuous surveillance, analysis, and interpretation of relevant data for system maintenance, management, and strategic planning  ...  INDEX TERMS Active and passive data, big data, complex systems, system health monitoring and management.  ...  identify bearing defect frequencies for health monitoring and fault diagnosis of rolling element bearings.  ... 
doi:10.1109/access.2019.2917891 fatcat:ht6o7imlgvgfjjv3fnspynio3m

Complexity and entropy representation for machine component diagnostics

Srinivasan Radhakrishnan, Yung-Tsun Tina Lee, Sudarsan Rachuri, Sagar Kamarthi, Osvaldo A. Rosso
2019 PLoS ONE  
The results confirm that the CECP representation is able to detect, with high accuracy, changes in underlying dynamics of machine component degradation states.  ...  The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series.  ...  Acknowledgments Funding for this research was provided by the National Institute of Standards and Technology under sponsor award number 70NANB15H028.  ... 
doi:10.1371/journal.pone.0217919 pmid:31287818 pmcid:PMC6615599 fatcat:pekezsy45nfzbdlgiht557qhku

Structural Health Monitoring through Vibration-Based Approaches

Giosuè Boscato, Luca Zanotti Fragonara, Antonella Cecchi, Emanuele Reccia, Daniele Baraldi
2019 Shock and Vibration  
data. is special issue aims to explore structural health monitoring via vibration-based approaches, especially for complex systems.  ...  A promising method of assessing the safety of vulnerable structures is the application of vibration-based monitoring (VBM) systems. ese allow observation of the global response for a structure, including  ...  We express our sincere thanks to the Editorial Board of SHMV for their approval on this topic and continuous support in successful publication of this special issue. e Lead Guest Editor would like to thank  ... 
doi:10.1155/2019/2380616 fatcat:acl2ftofejbw7prstil6i7mmge

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Dileep Kumar Soother, Jawaid Daudpoto, Nicholas R. Harris, Majid Hussain, Sanaullah Mehran, Imtiaz Hussain Kalwar, Tanweer Hussain, Tayab Din Memon, Dao B. Wang
2021 Mathematical Problems in Engineering  
This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques.  ...  Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts  ...  Complex wavelet packet energy moment entropy as a monitoring index allows reduction in aliasing and the detection of dynamic changes in the vibration data.  ... 
doi:10.1155/2021/9927151 fatcat:l6hhkychhfhg3bsgeprcqwd5qm

New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation

Xiaoming Xue, Nan Zhang, Suqun Cao, Wei Jiang, Jianzhong Zhou, Liyan Liu
2020 Complexity  
In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings.  ...  Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function.  ...  To simulate the actual Complexity failure state, the normal rolling element bearings (6205-2RS deep groove ball bearing) installed on both ends of the motor were damaged artificially by electrical discharge  ... 
doi:10.1155/2020/7247195 fatcat:hameiyayandg5cbzero3dhirbm

A comprehensive review on convolutional neural network in machine fault diagnosis [article]

Jinyang Jiao, Ming Zhao, Jing Lin, Kaixuan Liang
2020 arXiv   pre-print
Convolutional neural network, as a typical representative of intelligent diagnostic models, has been extensively studied and applied in recent five years, and a large amount of literature has been published  ...  To fill in this gap, this work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively.  ...  Lu et al. [64] proposed a convolutional network based health state classification method for rolling bearing.  ... 
arXiv:2002.07605v1 fatcat:54w3panr35bb7app4y7dfnjeqa
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