At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to an...At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to analyze the data.Therefore,we introduce kernel principal component analysis and stacked auto-encoder network(KPCA-SAD)into the fault diagnosis of ZPW-2000 track circuit.According to the working principle and fault characteristics of track circuit,a fault diagnosis model of KPCA-SAE network is established.The relevant parameters of key components recorded in the data collected by field staff are used as the fault feature parameters.The KPCA method is used to reduce the dimension and noise of fault document matrix to avoid information redundancy.The SAE network is trained by the processed fault data.The model parameters are optimized overall by using back propagation(BP)algorithm.The KPCA-SAE model is simulated in Matlab platform and is finally proved to be effective and feasible.Compared with the traditional method of artificially analyzing fault data and other intelligent algorithms,the KPCA-SAE based classifier has higher fault identification accuracy.展开更多
This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measureme...This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.展开更多
基金National Natural Science Foundation of China(No.61763023)。
文摘At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to analyze the data.Therefore,we introduce kernel principal component analysis and stacked auto-encoder network(KPCA-SAD)into the fault diagnosis of ZPW-2000 track circuit.According to the working principle and fault characteristics of track circuit,a fault diagnosis model of KPCA-SAE network is established.The relevant parameters of key components recorded in the data collected by field staff are used as the fault feature parameters.The KPCA method is used to reduce the dimension and noise of fault document matrix to avoid information redundancy.The SAE network is trained by the processed fault data.The model parameters are optimized overall by using back propagation(BP)algorithm.The KPCA-SAE model is simulated in Matlab platform and is finally proved to be effective and feasible.Compared with the traditional method of artificially analyzing fault data and other intelligent algorithms,the KPCA-SAE based classifier has higher fault identification accuracy.
文摘This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.
