Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearin...Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.展开更多
The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit ...The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).展开更多
布里渊光时域分析(BOTDA)系统中的布里渊增益谱(BGS)可能存在噪声,造成布里渊频移等重要信息难以提取的问题,故需对BGS降噪。现有BGS降噪方法分为基于模型的方法(如BM3D)和基于学习方法(如Dn CNN)两大类,分别存在降噪速度慢和可解释性...布里渊光时域分析(BOTDA)系统中的布里渊增益谱(BGS)可能存在噪声,造成布里渊频移等重要信息难以提取的问题,故需对BGS降噪。现有BGS降噪方法分为基于模型的方法(如BM3D)和基于学习方法(如Dn CNN)两大类,分别存在降噪速度慢和可解释性差的问题。对此提出基于多尺度深度展开网络(MSDUN)的BGS降噪方法,具有降噪效果好、降噪速度快、可解释性好的优点。MSDUN通过将输入图像经过一系列参数可学习的降噪模块实现降噪,卷积神经网络是隐含在每个降噪模块中的,因此MSDUN结构层次清楚,具有明晰的可解释性。由于在单个降噪模块中使用了卷积神经网络,因此降噪速度相比BM3D这类基于模型的方法更快。仿真和实验结果表明,MSDUN可以将三维BGS灰度图信噪比增强8.14 d B,降噪效果上优于BM3D的3.92 d B和Dn CNN的2.23 d B;降噪速度上,MSDUN只需4.8 s,比BM3D快了近30倍;相比Dn CNN,MSDUN算法层次结构更加清晰,可解释性好。展开更多
基金supported by the National Natural Science Foundation of China(62020106003,61873122,62303217)Aero Engine Corporation of China Industry-university-research Cooperation Project(HFZL2020CXY011)the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(MCMS-I-0121G03).
文摘Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.
基金supported by the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20210347)。
文摘The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).
文摘布里渊光时域分析(BOTDA)系统中的布里渊增益谱(BGS)可能存在噪声,造成布里渊频移等重要信息难以提取的问题,故需对BGS降噪。现有BGS降噪方法分为基于模型的方法(如BM3D)和基于学习方法(如Dn CNN)两大类,分别存在降噪速度慢和可解释性差的问题。对此提出基于多尺度深度展开网络(MSDUN)的BGS降噪方法,具有降噪效果好、降噪速度快、可解释性好的优点。MSDUN通过将输入图像经过一系列参数可学习的降噪模块实现降噪,卷积神经网络是隐含在每个降噪模块中的,因此MSDUN结构层次清楚,具有明晰的可解释性。由于在单个降噪模块中使用了卷积神经网络,因此降噪速度相比BM3D这类基于模型的方法更快。仿真和实验结果表明,MSDUN可以将三维BGS灰度图信噪比增强8.14 d B,降噪效果上优于BM3D的3.92 d B和Dn CNN的2.23 d B;降噪速度上,MSDUN只需4.8 s,比BM3D快了近30倍;相比Dn CNN,MSDUN算法层次结构更加清晰,可解释性好。
