Measurement method of metro rail corrugation based on the SEQ2SEQ model and the fusion data of vibration and noise

A metro rail corrugation measurement method based on a Seq2Seq model and vibration and noise data fusion, comprising a non-transitory computer readable medium operable on a computer with memory for the metro rail corrugation measurement method, and comprising program instructions for executing the following steps of:

• constructing a time series data set corresponding to floor longitudinal acceleration and train speed based on historical data of a metro train, the time series data set comprising a longitudinal acceleration time series and a train speed time series;
• wherein constructing the time series data set corresponding to the floor longitudinal acceleration and the metro train based on the historical data of the metro train comprises:
• acquiring a vertical and longitudinal vibration acceleration signals from an acceleration sensor arranged on a floor surface above a bogie of the metro train based on the historical data of the metro train, and obtaining vertical and longitudinal vibration time domain information of the floor in the metro train based on the vertical and longitudinal vibration acceleration signals;
• acquiring a measured sound pressure signal from a sound pressure sensor arranged in a carriage above the bogie of the metro train, and obtaining a sound pressure time domain information in the metro train accordingly;
• acquiring a measured speed signal from a speed measuring wheelset sensor arranged on an axle of the metro train, and obtaining a train speed signal accordingly;
• and
• dividing the longitudinal vibration acceleration signal and the train speed signal into segmented longitudinal acceleration signals based on a preset window length, to generate the time series data set corresponding to the longitudinal vibration acceleration signal and the train speed, wherein the dividing comprises:
• the longitudinal vibration acceleration signal and the train speed signal are resampled to ensure that a length of the longitudinal acceleration time series is equal to a length of the train speed time series, setting the length as Ls;
• determining a sample number N of the time series data set according to formula (1), wherein the longitudinal acceleration time series is denoted as xi (i=0, 1, . . . , xn), and the train speed time series is denoted as yj (j=0, 1, . . . , yn), and a time step number is set to K:
• N
• =
• L
• s
• -
• K
• +
• 1
• ;
• (
• 1
• )
• and
• generating the time series data set corresponding to the longitudinal vibration acceleration signal and the train speed according to formula (2);
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• ;
• constructing a metro train mileage matching prediction model based on the Seq2Seq model, which is trained by the time series data set corresponding to the longitudinal acceleration;
• inputting a longitudinal acceleration signal of a floor inside of the metro train to be detected into the trained metro train mileage matching prediction model based on the Seq2Seq model to obtain a running speed of the metro train to be detected;
• calculating a vibration and noise composite index of the metro rail corrugation to be detected based on a preset wavelength distribution range of the metro rail corrugation, the running speed of the metro train to be detected, a vertical vibration acceleration of the floor, and a sound pressure signal in the metro train;
• determining a specific position of the metro rail corrugation, a corresponding characteristic wavelength, and an amplitude based on the rail corrugation vibration and noise composite index;
• and
• outputting the determined specific position of the metro rail corrugation on a train rail, the characteristic wavelength, and the amplitude determined by the metro rail corrugation measurement method, to mitigate the metro rail corrugation and to reduce vibration and noise.