[Objective]Difficulty in easily obtaining fault data of various rail transit equipment leads to insufficient data to support the research on machine intelligence algorithms such as fault diagnosis and prediction.In order to meet the urgent need of intelligent rail transit operation and maintenance for a large amount of training data,it is necessary to design and ver-ify the simulation data generator(hereinafter abbreviated as SD generator)of rail transit switch machine.[Method]The char-acteristics of S700K type switch machine power curves under normal operation and gradual fault conditions are analyzed,and causes of the faults are discussed.By comparing two simula-tion data generation methods,a switch machine SD generator is designed based on the Borderline-Smote algorithm.Through building a platform for SD generator,and using the time series features of learning the power data by LSTM(long short-term memory)prediction model,three characteristics of the genera-ted gradual fault power data such as the crest factor,standard deviation,and variance are tested.[Result & Conclusion]The LSTM prediction model trained on the power data genera-ted by SD generator can predict the power change trend of the S700K type switch machine.The root mean square errors of crest factor,standard deviation,and variance calculated by the LSTM prediction model and the periodic replication method are 0.335 5,0.023 9,and 0.024 1 respectively.The relatively small errors prove the authenticity and feasibility of SD genera-tor.
关键词
轨道交通/转辙机/模拟数据生成器/智能运维
Key words
rail transit/switch machine/simulation data generator/intelligent operation and maintenance
维普
万方数据