针对目前铁路道岔故障率高，维护质量低等问题，以S700K型转辙机功率曲线为研究对象，提出一种补偿距离评估技术(Compensation Distance Evaluation Technique, CDET)结合改进的粒子群算法( Modified Particle Swarm Optimization, MPSO)优化支持向量机(Support Vector Machine, SVM)的道岔智能故障诊断方法.首先，通过分析S700K转辙机动作机理，将功率曲线分成启动、解锁、转换、锁闭、构通表示5个阶段，分别提取各阶段道岔功率曲线相应的特征集；然后，利用补偿距离评估技术对提取的特征候选集进行降维，选出敏感特征；最后，引入扰动项和动量项对粒子群算法进行改进并优化SVM相关参数，作为分类器对道岔故障进行预测，并与基于PSO-SVM，SVM等分类算法进行比较.仿真验证表明：该方法诊断正确率达到97%以上，能有效地识别道岔故障类型.

Aiming at the problems of high failure rate and low maintenance quality of railway turnout, this paper took the power curve of S700K switch machine as the research object, and a Compensation Distance Evaluation Technique (CDET) combined with an Modified Particle Swarm Optimization (MPSO) optimized Support Vector Machine (SVM) intelligent turnout fault diagnosis method is proposed. Firstly, the action mechanism of S700K switch machine is analyzed, and the power curve is divided into five stages: start, unlock, transfer, lock and structured representation, then the corresponding feature sets of switch power curve in each stage are extracted respectively. Secondly, the CDET is used to reduce the dimension of the extract feature candidate and select the sensitive feature. Finally, the disturbance and momentum terms are introduced to modify the particle swarm optimization algorithm, which is used to optimize the SVM parameters. Then SVM as a classifier to predict the turnout faults, and is compared with PSO-SVM, SVM and other algorithms. The simulations show that the method diagnosis accuracy is more than 97%, which can accurately and effectively identify the type of turnout fault.