CAJ | 학술논문

道岔故障类型识别对高速铁路设备维护具有重要的意义。在提出的基于FOA-LSSVM的高铁提速道岔故障诊断算法中，以道岔动作电流曲线监测数据为分析基础，选择5个特征指标组成道岔故障诊断模型的特征输入向量，降低了输入向量的维数，缩短训练时间，并采用果蝇优化算法，能够提高计算速度，保持良好的回归性能。通过实例分析证明：基于FOA-LSSVM的道岔故障诊断算法的分类性能好、识别准确率高，能够保证道岔故障类型测定的准确性和可靠性，缩短故障处理时间，确保高速铁路运输的安全与实效。
도차고장류형식별대고속철로설비유호구유중요적의의。재제출적기우FOA-LSSVM적고철제속도차고장진단산법중，이도차동작전류곡선감측수거위분석기출，선택5개특정지표조성도차고장진단모형적특정수입향량，강저료수입향량적유수，축단훈련시간，병채용과승우화산법，능구제고계산속도，보지량호적회귀성능。통과실례분석증명：기우FOA-LSSVM적도차고장진단산법적분류성능호、식별준학솔고，능구보증도차고장류형측정적준학성화가고성，축단고장처리시간，학보고속철로운수적안전여실효。
The railway switch failure type identification for high-speed railway signal equipment maintenance play an im?portant role. In the paper, based on FOA-LSSVM high-speed railway switch failure diagnosis algorithm, in railway switch actuating current curves based on monitoring data for the analysis, chose five characteristic index composed of railway switch failure diagnosis models characteristic input vectors, not only reduces the dimension of input vectors, shortening training time, using a fruit fly optimization algorithm to accelerate the computing speed, while maintaining a good regres?sion performance. Proved by an example:based on FOA-LSSVM railway switch failure diagnosis algorithm has strong self-learning ability and higher prediction accuracy, but also to accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction, to ensure the safety and effectiveness of high-speed rail trans?portation.