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
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.
