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Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting
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Qingyun WU, Yingqi MENG, Jinghui GAO, Xinlin HE, Kui GAO, Hui ZHAO, Xiangshuai TAN, Yunfei GUO, Litao NIU, Ruyu ZHAO, Zhao LI, Zhi YAO, Yicun LIN
Thermal Power Generation | 2024, 53(1) : 145 - 153
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Thermal Power Generation | 2024, 53(1): 145-153
Power generation technology forum
Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting
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Qingyun WU, Yingqi MENG, Jinghui GAO, Xinlin HE, Kui GAO, Hui ZHAO, Xiangshuai TAN, Yunfei GUO, Litao NIU, Ruyu ZHAO, Zhao LI, Zhi YAO, Yicun LIN
Affiliations
  • Xi’an Thermal Power Research Institute Co, Ltd, Xi’an 710054, China
Published: 2024-01-25 doi: 10.19666/j.rlfd.202306103
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To enhance the whole process safety of fan operations and ensure accurate fault diagnosis and long-term production income of thermal power plants, predicting these risk issues is crucial to enhance the safety of the unit. In this paper, we proposed a fan fault diagnosis model of big data platform that integrates multilayer perceptron and polynomial fitting. The fan early warning model was established by multilayer perceptron and polynomial fitting modeling technology, and integrated into the big data platform to find abnormalities which were difficult to find manually during the operation of the fan. By combining data mining with mechanism analysis and feature value knowledge base, the parameters boundary information of fan stall could be excavated, the stall boundary conditions of the fan were accurately configured under various working conditions, and a stall boundary condition diagram was created. By combining those informations with normal operating conditions, the early stall zone can be obtained. Finally, a fault diagnosis model that covers the entire working condition of the fan can be established. Utilizing the comprehensive big data platform that covers, circulates, and maintains fan operation data, a system of intelligent fan patrol model was constructed. The intelligent patrol disk model which replaces the operator was then used to monitor and diagnose the fan running state regularly, which can achieve accurate and safe diagnosis of fan faults, minimize the fault incidence and maximize the personnel reuse rate.

big data platform  /  fan  /  fault diagnosis  /  multilayer perceptron  /  polynomial fitting
Qingyun WU, Yingqi MENG, Jinghui GAO, Xinlin HE, Kui GAO, Hui ZHAO, Xiangshuai TAN, Yunfei GUO, Litao NIU, Ruyu ZHAO, Zhao LI, Zhi YAO, Yicun LIN. Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting[J]. Thermal Power Generation, 2024 , 53 (1) : 145 -153 . DOI: 10.19666/j.rlfd.202306103
  • Standard Project of China Huaneng Group Co., Ltd.(HNBZ22-Q023)
Year 2024 volume 53 Issue 1
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doi: 10.19666/j.rlfd.202306103
  • Receive Date:2023-06-28
  • Online Date:2025-12-25
  • Published:2024-01-25
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  • Received:2023-06-28
Funding
Standard Project of China Huaneng Group Co., Ltd.(HNBZ22-Q023)
Affiliations
    Xi’an Thermal Power Research Institute Co, Ltd, Xi’an 710054, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202306103
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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