Journal Article10.1016/j.jobe.2023.106338
Imbalanced data based fault diagnosis of the chiller via integrating a new resampling technique with an improved ensemble extreme learning machine
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About: This article is published in Journal of building engineering. The article was published on 01 Mar 2023. The article focuses on the topics: Resampling & Chiller.
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Citations
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Highly imbalanced fault classification of wind turbines using data resampling and hybrid ensemble method approach
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A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine
Wenxin Yang,Hanyuan Zhang,Jit Bing Lim,Yuyu Zhang,Huanhuan Meng +4 more
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An enhanced feature extraction based long short-term memory neural network for wind power forecasting via considering the missing data reconstruction
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References
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
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Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
None Fitri S. Kasim,Tianci Zhang,Jinglong Chen,Fudong Li,Kaiyu Zhang,Haixin Lv,Shuilong He,Enyong Xu +7 more
TL;DR: In this paper, a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
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Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review
TL;DR: The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones.
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Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction
TL;DR: An integrated framework combining fault classification and location is proposed by applying an innovative machine-learning algorithm: the summation-wavelet extreme learning machine (SW-ELM) that integrates feature extraction in the learning process and is successfully applied to transmission line fault diagnosis.
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An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm
TL;DR: The results suggest that the proposed enhanced selective ensemble deep learning method with beetle antennae search algorithm can more accurately and robustly recognize different kind of faults than both the individual base models and other existing ensemble learning methods.
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