A Survey on Fault Diagnosis of Rolling Bearings

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@Article{Bo_Peng:2022:Algorithms,

• author = "Bo Peng and Ying Bi and Bing Xue and Mengjie Zhang and Shuting Wan",
• title = "A Survey on Fault Diagnosis of Rolling Bearings",
• journal = "Algorithms",
• year = "2022",
• volume = "15",
• pages = "article 347",
• note = "Special Issue Artificial Intelligence for Fault Detection and Diagnosis",
• keywords = "genetic algorithms, genetic programming, rolling bearing, diagnosis, fault detection, fault type recognition, signal processing, machine learning",
• URL = "https://www.mdpi.com/1999-4893/15/10/347",
• DOI = "doi:10.3390/a15100347",
• size = "24 pages",
• 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",
• 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

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