特高压换流站测控装置作为模拟量非线性、传输转换高要求的二次设备,目前的评估和预测方法不完全适用于测控装置的健康分析。提出了一种基于小波核主元(kernel principal component analysis, KPCA)分析和双向长短期记忆网络(bi-directi...特高压换流站测控装置作为模拟量非线性、传输转换高要求的二次设备,目前的评估和预测方法不完全适用于测控装置的健康分析。提出了一种基于小波核主元(kernel principal component analysis, KPCA)分析和双向长短期记忆网络(bi-directional long short-term memory, Bi-LSTM)结合的健康评估和预测方法。通过引入小波核函数,以提高KPCA对健康状态影响因素进行特征提取的能力。通过第一核主元建立健康指数,以评估测控装置状态变化。通过构建Bi-LSTM网络模型以输入特征信息达到健康预测目的。以浙江某换流站采集到的真实数据作为样本,通过实验数据进行了对比分析。结果表明,该方法可以提升多维健康监测数据的准确评估和预测精度,为检修人员制定检修策略提供科学参考。展开更多
Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and...Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.展开更多
文摘特高压换流站测控装置作为模拟量非线性、传输转换高要求的二次设备,目前的评估和预测方法不完全适用于测控装置的健康分析。提出了一种基于小波核主元(kernel principal component analysis, KPCA)分析和双向长短期记忆网络(bi-directional long short-term memory, Bi-LSTM)结合的健康评估和预测方法。通过引入小波核函数,以提高KPCA对健康状态影响因素进行特征提取的能力。通过第一核主元建立健康指数,以评估测控装置状态变化。通过构建Bi-LSTM网络模型以输入特征信息达到健康预测目的。以浙江某换流站采集到的真实数据作为样本,通过实验数据进行了对比分析。结果表明,该方法可以提升多维健康监测数据的准确评估和预测精度,为检修人员制定检修策略提供科学参考。
基金Natural Science Fund of Gansu Province(No.1310RJZA046)
文摘Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.
