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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine
Ning Li, Rui Zhou, Qinghua Hu, et al.
Mechanical Systems and Signal Processing (2011) Vol. 28, pp. 608-621
Closed Access | Times Cited: 123

Showing 1-25 of 123 citing articles:

Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei, Bin Yang, Xinwei Jiang, et al.
Mechanical Systems and Signal Processing (2020) Vol. 138, pp. 106587-106587
Open Access | Times Cited: 1948
Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liu, Boyuan Yang, Enrico Zio, et al.
Mechanical Systems and Signal Processing (2018) Vol. 108, pp. 33-47
Closed Access | Times Cited: 1737
Wavelets for fault diagnosis of rotary machines: A review with applications
Ruqiang Yan, Robert X. Gao, Xuefeng Chen
Signal Processing (2013) Vol. 96, pp. 1-15
Closed Access | Times Cited: 1257
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan, Cong Wang, Changan Zhu
Mechanical Systems and Signal Processing (2015) Vol. 72-73, pp. 92-104
Closed Access | Times Cited: 519
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Purushottam Gangsar, Rajiv Tiwari
Mechanical Systems and Signal Processing (2020) Vol. 144, pp. 106908-106908
Closed Access | Times Cited: 424
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning
Jiedi Sun, Changhong Yan, Jiangtao Wen
IEEE Transactions on Instrumentation and Measurement (2017) Vol. 67, Iss. 1, pp. 185-195
Closed Access | Times Cited: 363
Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis
Jimeng Li, Xifeng Yao, Xiangdong Wang, et al.
Measurement (2019) Vol. 153, pp. 107419-107419
Closed Access | Times Cited: 184
Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic
Hongfeng Tao, Long Cheng, Jier Qiu, et al.
Measurement Science and Technology (2022) Vol. 33, Iss. 11, pp. 115005-115005
Closed Access | Times Cited: 136
The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform
Renping Shao, Wentao Hu, Wang Ya-yun, et al.
Measurement (2014) Vol. 54, pp. 118-132
Closed Access | Times Cited: 163
A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion
Jinxiu Qu, Zhousuo Zhang, Teng Gong
Neurocomputing (2015) Vol. 171, pp. 837-853
Closed Access | Times Cited: 126
Multiwavelet transform and its applications in mechanical fault diagnosis – A review
Hailiang Sun, Zhengjia He, Yanyang Zi, et al.
Mechanical Systems and Signal Processing (2013) Vol. 43, Iss. 1-2, pp. 1-24
Closed Access | Times Cited: 120
Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method
Wei Li, Zhencai Zhu, Fan Jiang, et al.
Mechanical Systems and Signal Processing (2014) Vol. 50-51, pp. 414-426
Closed Access | Times Cited: 112
Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery
Dongyang Dou, Zhou Shishuai
Applied Soft Computing (2016) Vol. 46, pp. 459-468
Closed Access | Times Cited: 112
Adaptive stochastic resonance method for impact signal detection based on sliding window
Jimeng Li, Xuefeng Chen, Zhengjia He
Mechanical Systems and Signal Processing (2013) Vol. 36, Iss. 2, pp. 240-255
Closed Access | Times Cited: 109
Multi-frequency weak signal detection based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance
Dongying Han, Pei Li, Shujun An, et al.
Mechanical Systems and Signal Processing (2015) Vol. 70-71, pp. 995-1010
Closed Access | Times Cited: 108
A SVM framework for fault detection of the braking system in a high speed train
Jie Liu, Yan‐Fu Li, Enrico Zio
Mechanical Systems and Signal Processing (2016) Vol. 87, pp. 401-409
Open Access | Times Cited: 108
A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis
Zhijun Ren, Yongsheng Zhu, Ke Yan, et al.
Mechanical Systems and Signal Processing (2020) Vol. 138, pp. 106608-106608
Closed Access | Times Cited: 103
Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks
Cuixia Jiang, Hao Chen, Qifa Xu, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 4, pp. 1667-1681
Closed Access | Times Cited: 51
A future possibility of vibration based condition monitoring of rotating machines
Jyoti K. Sinha, Keri Elbhbah
Mechanical Systems and Signal Processing (2012) Vol. 34, Iss. 1-2, pp. 231-240
Closed Access | Times Cited: 105
Feature selection and classification of mechanical fault of an induction motor using random forest classifier
Raj Kumar Patel, V. K. Giri
Perspectives in Science (2016) Vol. 8, pp. 334-337
Open Access | Times Cited: 81
A Novel Personalized Diagnosis Methodology Using Numerical Simulation and an Intelligent Method to Detect Faults in a Shaft
Jiawei Xiang, Yongteng Zhong
Applied Sciences (2016) Vol. 6, Iss. 12, pp. 414-414
Open Access | Times Cited: 81
Classifying machinery condition using oil samples and binary logistic regression
Jeffrey S. Phillips, Edward Cripps, John W. Lau, et al.
Mechanical Systems and Signal Processing (2015) Vol. 60-61, pp. 316-325
Closed Access | Times Cited: 79
Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach
Zhi-Xin Yang, Xianbo Wang, Jianhua Zhong
Energies (2016) Vol. 9, Iss. 6, pp. 379-379
Open Access | Times Cited: 77
Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis
Wan Zhang, Minping Jia, Lin Zhu, et al.
Chinese Journal of Mechanical Engineering (2017) Vol. 30, Iss. 4, pp. 782-795
Open Access | Times Cited: 70
Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method
Peiming Shi, Shujun An, Pei Li, et al.
Measurement (2016) Vol. 90, pp. 318-328
Closed Access | Times Cited: 69
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