Working condition monitoring and fault early warning method for coal mine frequency conversion local ventilator

The invention relates to a working condition monitoring and fault early warning method for a coal mine frequency conversion local ventilator, which comprises fault diagnosis and fault early warning, and is used for researching the monitoring, diagnosis and early warning of the running state of a min...

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Bibliographic Details
Main Authors WAN XIANG, SHANG XINMANG, WANG MIN, SONG JINQUAN, SHI GANG, GUO WENFANG, XUE XUSHENG, ZHANG XUHUI
Format Patent
LanguageChinese
English
Published 04.03.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:The invention relates to a working condition monitoring and fault early warning method for a coal mine frequency conversion local ventilator, which comprises fault diagnosis and fault early warning, and is used for researching the monitoring, diagnosis and early warning of the running state of a mine main ventilator. By analyzing common fault mechanisms and characteristics of the mine main ventilator, a mine main ventilator equipment operation state monitoring scheme is formulated, and on the basis of a traditional BP neural network, a BP algorithm is optimized by using a particle swarm optimization (PSO) algorithm, so that the accuracy of fault diagnosis is improved. In motor fault type identification through PSO-BPNN, the algorithm convergence speed and the diagnosis precision are obviously better than those of BPNN, and the method can better adapt to motor fault diagnosis under actual working conditions. Through simulation experiment analysis, it can be obtained that the PSO-BPNN prediction method combined
Bibliography:Application Number: CN202111469203