基于概率主成分分析(Probabilistic Principal Component Analysis,PPCA)和谱峭度方法,提出一种轴承故障诊断新方法.用主元模型对原始信号进行消噪,可完整地保留信号的有用成分,并能大幅度提高信噪比,解决故障信号易受噪声干扰的问题....基于概率主成分分析(Probabilistic Principal Component Analysis,PPCA)和谱峭度方法,提出一种轴承故障诊断新方法.用主元模型对原始信号进行消噪,可完整地保留信号的有用成分,并能大幅度提高信噪比,解决故障信号易受噪声干扰的问题.对消噪后的信号进行基于谱峭度的最优带通滤波和包络谱分析,由包络频谱诊断轴承故障.通过数值仿真和实验验证了所给方法的正确性和有效性.所给方法可用于滚动轴承的故障诊断.展开更多
In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different ...In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.展开更多
文摘基于概率主成分分析(Probabilistic Principal Component Analysis,PPCA)和谱峭度方法,提出一种轴承故障诊断新方法.用主元模型对原始信号进行消噪,可完整地保留信号的有用成分,并能大幅度提高信噪比,解决故障信号易受噪声干扰的问题.对消噪后的信号进行基于谱峭度的最优带通滤波和包络谱分析,由包络频谱诊断轴承故障.通过数值仿真和实验验证了所给方法的正确性和有效性.所给方法可用于滚动轴承的故障诊断.
基金supported by Zhejiang Provincial Natural Science Foundation of China(LY19F030003)Key Research and Development Project of Zhejiang Province(2021C04030)+1 种基金the National Natural Science Foundation of China(62003306)Educational Commission Research Program of Zhejiang Province(Y202044842)。
文摘In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.
