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首页> 外文期刊>Metalurgia >DIAGNOSIS ON ROTATING MACHINERY FAILURE USING MULTI-SYMPTOM COMPREHENSIVE DIAGNOSTIC NETWORK MODEL BASED ON QUANTUM NEURAL NETWORK
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DIAGNOSIS ON ROTATING MACHINERY FAILURE USING MULTI-SYMPTOM COMPREHENSIVE DIAGNOSTIC NETWORK MODEL BASED ON QUANTUM NEURAL NETWORK

机译:基于量子神经网络的多症状综合诊断网络模型的旋转机械故障诊断

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摘要

The rotating machinery failure is found with the level, relevance, ambiguity and diversity features, for which based on the Quantum Neural Network, a comprehensive diagnostic network model involving multiple symptoms is proposed. This model incorporates multi-universe quantum neural network diagnosis with the regular backward reasoning strategies, on which the diagnosis on rotating machinery failures is divided into crude diagnosis, detailed diagnosis and accurate diagnosis. Such organic combination of the three diagnosis levels were verified through the instance failure analysis, and it is proven to be effective in improving the correct rate of the rotating machinery failure diagnosis.
机译:发现旋转机械故障具有水平,相关性,歧义性和多样性的特征,为此,基于量子神经网络,提出了一种包含多种症状的综合诊断网络模型。该模型将多宇宙量子神经网络诊断与常规的后向推理策略相结合,将旋转机械故障的诊断分为粗略诊断,详细诊断和准确诊断。通过实例故障分析验证了三种诊断水平的这种有机结合,并被证明可以有效提高旋转机械故障诊断的正确率。

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