基于SVM-AdaBoost的道岔控制电路故障诊断方法研究

摘要：

为实现铁路信号全电子计算机联锁系统控制电路的故障诊断， 以五线制道岔全电子控制模块为例， 提出了采用轮盘赌转法选择基本分类器的样本集， 采用SVM-AdaBoost算法实现故障诊断的方法。 实验结果表明， 基本分类器样本占比影响分类准确率， 样本占比为85%时准确率最高； 轮盘赌转法选择基本分类器的样本集后故障诊断准确率普遍高于最大权重优先的方式， 准确率达96.3%； 同时该方法能更好地适应临界数据， 提高算法抗干扰能力。 因此本论文的研究内容可为全电子计算机联锁系统的故障诊断提供依据。

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.