A Survey on Fault Diagnosis of Rolling Bearings
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- @Article{Bo_Peng:2022:Algorithms,
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author = "Bo Peng and Ying Bi and Bing Xue and Mengjie Zhang and
Shuting Wan",
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title = "A Survey on Fault Diagnosis of Rolling Bearings",
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journal = "Algorithms",
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year = "2022",
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volume = "15",
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pages = "article 347",
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note = "Special Issue Artificial Intelligence for Fault
Detection and Diagnosis",
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keywords = "genetic algorithms, genetic programming, rolling
bearing, diagnosis, fault detection, fault type
recognition, signal processing, machine learning",
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URL = "https://www.mdpi.com/1999-4893/15/10/347",
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DOI = "doi:10.3390/a15100347",
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size = "24 pages",
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abstract = "The failure of a rolling bearing may cause the
shut-down of mechanical equipment and even induce
catastrophic accidents, resulting in tremendous
economic losses and a severely negative impact on
society. Fault diagnosis of rolling bearings becomes an
important topic with much attention from researchers
and industrial pioneers. There are an increasing number
of publications on this topic. However, there is a lack
of a comprehensive survey of existing works from the
perspectives of fault detection and fault type
recognition in rolling bearings using vibration
signals. Therefore, this paper reviews recent fault
detection and fault type recognition methods using
vibration signals. First, it provides an overview of
fault diagnosis of rolling bearings and typical fault
types. Then, existing fault diagnosis methods are
categorized into fault detection methods and fault type
recognition methods, which are separately revised and
discussed. Finally, a summary of existing datasets,
limitations/cha",
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notes = "College of Mechanical and Electrical Engineering,
Hebei Agricultural University, Baoding 071000, China",
- }
Genetic Programming entries for
Bo Peng
Ying Bi
Bing Xue
Mengjie Zhang
Shuting Wan
Citations