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作 者:Xukai Ren Huanwei Yu Xianfeng Chen Yantong Tang Guobiao Wang Xiyong Du
机构地区:[1]School of Mechanical Engineering,Tianjin University,Tianjin,300354,China [2]Quality Department,Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation,Shaoxing,312071,China
出 处:《Computer Modeling in Engineering & Sciences》2024年第7期647-663,共17页工程与科学中的计算机建模(英文)
基 金:supported by the China Postdoctoral Science Foundation(Grant Number 2023M742598).
摘 要:Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.
关 键 词:Stirred reactor fault diagnosis vibration signal CatBoost
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