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基于改进的堆叠降噪自动编码器深度模型的转子-转轴系统故障诊断方法
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国家自然科学基金项目(51875498);河北省自然科学基金重点项目(E2018203339)


Fault Diagnosis Method of Rotor-shaft System Based on the Improved Stacked Denoising Auto Encoder Depth Model
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    摘要:

    旋转机械转子-转轴系统故障诊断方法中大多采用传统浅层模型,对于数量较大的样本其处理能力有限。为解决此问题,提出一种利用改进的堆叠降噪自动编码器(SDAE)深度模型的故障诊断方法,并对转子-转轴系统的典型故障进行诊断。利用某机械故障综合模拟实验台,结合基于LabVIEW开发的信号采集系统模拟并采集转子-转轴系统的10类单一故障和7类复合故障振动信号。在训练SDAE模型时引入Dropout机制对模型进行改进,并结合Softmax分类器进行网络训练与诊断。与传统BP网络、自动编码器(AE)、无Dropout机制的SDAE和卷积神经网络(CNN)进行对比,结果表明:改进的SDAE方法对于转子-转轴系统故障的正确识别率最高,特别是对复合故障的诊断效果比其他模型更理想,充分验证了改进的SDAE深度模型的优越性

    Abstract:

    Traditional shallow model is adopted in most fault diagnosis methods for rotorshaft systems, but it is difficult to deal with a large sample.In order to solve this problem, a fault diagnosis method based on the improved Stacked Denoising Auto Encoder (SDAE) depth model was proposed, and it was used to diagnose the typical faults of the rotorshaft system.Using a mechanical fault comprehensive simulation platform, combined with the signal acquisition system developed based on LabVIEW, the 10 types of single fault signal and 7 types of composite failure signal of the rotorshaft system were simulated and collected. Dropout mechanism was introduced to improve the performance of SDAE model, and Softmax classifier was combined for network training and diagnosis. Compared with traditional BP network, automatic encoder (AE), SDAE without Dropout mechanism and convolutional neural network (CNN), the results show that the improved SDAE method has the highest correct identification rate for rotorshaft system faults, especially the diagnosis effect to composite faults is better than the other models, which fully verifies the superiority of the improved SDAE depth model

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姜万录,李金虎,李振宝,姜安琦.基于改进的堆叠降噪自动编码器深度模型的转子-转轴系统故障诊断方法[J].机床与液压,2020,48(21):182-188.
JIANG Wanlu, LI Jinhu, LI Zhenbao, JIANG Anqi. Fault Diagnosis Method of Rotor-shaft System Based on the Improved Stacked Denoising Auto Encoder Depth Model[J]. Machine Tool & Hydraulics,2020,48(21):182-188

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  • 在线发布日期: 2021-02-20
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