Method for automatic autonomous control of a packing machine

Abstract

A method for automatic autonomous control of a packing machine (C) having a position-measuring device (WMS, GPS, 32) for precise detection of the position of the track-building machine in a track, and signal detection by actuators of working assemblies (23, bv, 18, 26) of the packing machine (C). Track ballast data are detected by sensors (23, bv, 18, 26) during the packing and the current track ballast parameters are detected therefrom and stored for a subsequent work pass and analysed by a device for machine learning (17, ML). An analysis of the track ballast state data (EF7, S9, A3) is created on the basis of machine learning methods (ML, 17) and the track ballast parameters are analysed in view of a drop in compression forces that occurs in the longitudinal track direction and work instructions (EF7, S9, A3) for an optimal work approach are ascertained therefrom and stored. In a subsequent work pass, depending on the current position in the track and on the associated work instruction data, the packing machine carries out the work instructions automatically and autonomously.

1 . A method for automatic autonomous control of a packing machine having a position-measuring device detecting a position of a track-building machine in a track, with signal detection by actuators of working assemblies of the packing machine, said method comprising:
 acquiring ballast bed data via sensors during packing; and   acquiring current ballast bed parameters from the ballast bed data;   storing the current ballast bed parameters for a subsequent work pass; and   analyzing the current ballast bed parameters with a machine learning device so as to create an analysis of ballast bed state data based on machine learning methods, wherein the current ballast bed parameters are analyzed with regard to a drop in compression forces occurring in a longitudinal direction of the track; and   determining and storing work instruction data defining work instructions of an optimum mode of operation from the current ballast bed parameters; and   wherein, in a subsequent work pass, based on a current position in the track and associated work instruction data, the packing machine carries out the work instructions of said work instruction data fully automatically and autonomously.   
     
     
         2 . The method according to  claim 1 , wherein the method further comprises supplying a control computer of the packing machine with positionally accurate work instructions for each sleeper region to be packed, and
 wherein the packing machine moves to a respective longitudinal position in the track fully automatically based on the detected corresponding longitudinal position in the track and the specified work instruction data and autonomously performs work in the sleeper area to be packed based on the work instruction data,   whereupon the next sleeper area to be processed is accessed via an automatic travel system in accordance with the work instruction data and the cycle of processing the work instruction data and moving to the next sleeper area to be processed is repeated until an intended work area has been processed.   
     
     
         3 . The method according to  claim 1 , wherein the method further comprises displaying predetermined work instructions for each sleeper area to an operator, and the operator sets and executes the predetermined work instructions. 
     
     
         4 . The method according to  claim 2 , wherein the method further comprises generating a ballast bed state record during the work and generating a ballast bed state report with the result of the analysis of the ballast bed data by the machine learning device and transmitting both the ballast bed state record and the ballast bed state report to an infrastructure manager. 
     
     
         5 . The method according to  claim 1 , wherein the analysis of the ballast bed data by the machine learning device, which runs during the packing work, provides an operator with indications for an optimum working procedure. 
     
     
         6 . The method according to  claim 3 , wherein the method further comprises generating a ballast bed state record during the work and generating a ballast bed state report with the result of the analysis of the ballast bed data by the machine learning device and transmitting both the ballast bed state record and the ballast bed state report to an infrastructure manager. 
     
     
         7 . The method according to  claim 6 , wherein the analysis of the ballast bed data by the machine learning device, which runs during the packing work, provides an operator with indications for an optimum working procedure. 
     
     
         8 . The method according to  claim 2 , wherein the analysis of the ballast bed data by the machine learning device, which runs during the packing work, provides an operator with indications for an optimum working procedure. 
     
     
         9 . The method according to  claim 3 , wherein the analysis of the ballast bed data by the machine learning device, which runs during the packing work, provides an operator with indications for an optimum working procedure. 
     
     
         10 . The method according to  claim 4 , wherein the analysis of the ballast bed data by the machine learning device, which runs during the packing work, provides an operator with indications for an optimum working procedure.