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
A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is pre-sented. By distinguishing the different patterns of the PQ components in the PQ plane,the rotor broken ba...A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is pre-sented. By distinguishing the different patterns of the PQ components in the PQ plane,the rotor broken bar fault can be detected. The magnitude of power component directly resulted from rotor fault is used as the fault indicator and the distance between the point of no-load condition and the center of the ellipse as its normalization value. Based on these,the fault severity factor which is completely independent of the inertia and load level is defined. Moreover,a method to reliably discriminate between rotor faults and periodic load fluctuation is presented. Experimental results from a 4 kW induction motor demonstrated the validity of the proposed method.展开更多
In order to realize the fault diagnosis of the control circuit of all-electronic computer interlocking system(ACIS)for railway signals,taking a five-wire switch electronic control module as an research object,we propo...In order to realize the fault diagnosis of the control circuit of all-electronic computer interlocking system(ACIS)for railway signals,taking a five-wire switch electronic control module as an research object,we propose a method of selecting the sample set of the basic classifier by roulette method and realizing fault diagnosis by using SVM-AdaBoost.The experimental results show that the proportion of basic classifier samples affects classification accuracy,which reaches the highest when the proportion is 85%.When selecting the sample set of basic classifier by roulette method,the fault diagnosis accuracy is generally higher than that of the maximum weight priority method.When the optimal proportion 85%is taken,the accuracy is highest up to 96.3%.More importantly,this way can better adapt to the critical data and improve the anti-interference ability of the algorithm,and therefore it provides a basis for fault diagnosis of ACIS.展开更多
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.
基金Project (No. 50677060) supported by the National Natural ScienceFoundation of China
文摘A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is pre-sented. By distinguishing the different patterns of the PQ components in the PQ plane,the rotor broken bar fault can be detected. The magnitude of power component directly resulted from rotor fault is used as the fault indicator and the distance between the point of no-load condition and the center of the ellipse as its normalization value. Based on these,the fault severity factor which is completely independent of the inertia and load level is defined. Moreover,a method to reliably discriminate between rotor faults and periodic load fluctuation is presented. Experimental results from a 4 kW induction motor demonstrated the validity of the proposed method.
基金Natural Science Foundation of Gansu Province(Nos.18JR3RA130,2018C-11,2018A-022)Science Fund of Lanzhou Jiaotong University(No.2017022)。
文摘In order to realize the fault diagnosis of the control circuit of all-electronic computer interlocking system(ACIS)for railway signals,taking a five-wire switch electronic control module as an research object,we propose a method of selecting the sample set of the basic classifier by roulette method and realizing fault diagnosis by using SVM-AdaBoost.The experimental results show that the proportion of basic classifier samples affects classification accuracy,which reaches the highest when the proportion is 85%.When selecting the sample set of basic classifier by roulette method,the fault diagnosis accuracy is generally higher than that of the maximum weight priority method.When the optimal proportion 85%is taken,the accuracy is highest up to 96.3%.More importantly,this way can better adapt to the critical data and improve the anti-interference ability of the algorithm,and therefore it provides a basis for fault diagnosis of ACIS.
