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.

Claims

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.

Description

SUMMARY OF THE INVENTION

[0036] In the drawing, the subject matter of the invention is shown by way of example, wherein:

[0037] FIG. 1 shows a schematic side view of a packing machine,

[0038] FIG. 2 shows a schematic representation of a fully hydraulic packing unit,

[0039] FIG. 3 shows a circuit diagram of a track geometry computer with the control devices of the packing machine, and

[0040] FIG. 4 shows a ballast bed acceptance record.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0041] FIG. 1 shows a packing machine 38, C with trailer 39 which travels on track-mounted undercarriages 34, 36 on railroad tracks S. The packing machine 38, C has a packing unit 30 with fully hydraulic drive and measuring sensors 37, a lifting and straightening unit 42, 43 for introducing lifting forces FH and straightening forces FR into the track, a working measuring system aw, bw, 35 and an acceptance recorder measuring system ar, br, 35. Working measuring system aw, bw, 35 and acceptance recorder measuring system ar, br, 35 are, for example, chord measuring systems. The trailer is coupled to the packing machine by a drawbar 40. The packing unit 30 has a standard opening width B of the packing tools 29. The packing machine 38, C also has a control system 19, a track geometry guidance computer 17 with screen 20. Data is exchanged wirelessly with the infrastructure operator via an antenna 33. The working area is precisely coordinated via a GPS system 32.

[0042] FIG. 2 shows a packing unit B with fully hydraulic drive Z. Via sensors 23, the adjusting distance 31 and the compression force (via pressure sensors in the packing cylinder hydraulics) are recorded and transferred to the control computer 18, which forwards them to the track geometry computer 17 for processing. An acceleration sensor by measures the braking deceleration of the packing unit when it dips into the ballast bed. The harder this is, the higher the braking deceleration. The fully hydraulic drive can adjust the opening width of the packing arms 30 with the packing tools 29 from the normal opening B to a larger width BE. This makes it possible at points of damaged ballast to push ballast granules from the intermediate compartment under the sleeper in a compacting manner through the larger opening BE in order to supplement the partially damaged crushed ballast granules there with intact ballast granules to increase the durability of the track layer. The rails S are fastened to sleepers 41.

[0043] FIG. 3 shows a circuit diagram of the track geometry computer 17 with the control devices 19 of the machine. The sensors of the fully hydraulic packing units 18, 26 are read in and analyzed with a machine learning program ML. Via the screen 20, the machine operator is informed about the ballast bed state and can receive work instructions. At the end of the packing operation, a ballast bed report 22 and a ballast bed record 21 are generated by the track geometry computer 17 and the machine learning program ML. This data is sent wirelessly 25 to an infrastructure operator or machine owner database or to a cloud. The ballast bed parameters under each sleeper are accurately recorded via GPS and assigned to them. A distance measuring wheel WMS is used to assign the local position over the track km.

[0044] FIG. 4 schematically shows a ballast bed diagram A. Recording channel 1 shows the braking delay by of the packing units, channel 2 the track height error before work determined from preliminary measurements of the current track position and comparison with the target track position, channel 3 shows the ballast hardness and channel 4 the compression force achieved. Channel 5 is the event channel which indicates various special track conditions or track characteristics via markers 6, 7, 8, Br. Symbol 6 stands for a rail joint, symbol 7 marks a place in the track where the ballast is destroyed and therefore no satisfactory compression forces can be achieved. Symbol 8 stands for deposited pictures and Br indicates a bridge. At singular individual faults, photos are embedded in the record. If the operator activates them, the corresponding photo 8 is shown. 10 shows singular fault locations with destroyed ballast, evident on the one hand from the rapid drop in the compression forces and also from the fact that the packing unit braking delay 11 drops because the ballast does not have a high penetration resistance at these locations. Another fault location is formed by 9 which occurs at a weld joint as shown by symbol 6. Such singular fault locations can be detected and recognized relatively easily by a machine learning program (or a rule-based system). If the course of the height defects (channel 2) is compared with with the course of the ballast bed hardness (channel 3), it is recognized that they behave in approximately inverse proportion 12. At hard places, high points form in the height. Where there are soft places, settlements (troughs) are formed. Correlation functions can be used to determine how well these two channels are correlated. If the correlation is high, this influences the durability of the track level position because the ballast deformation has formed to a corresponding degree. The higher the standard deviation of the ballast bed hardness σ.sub.BH is, the stronger the stiffness variations and the higher the interacting forces between wheel and rail and the lower the durability of the track position. The mean value of ballast bed hardness 16, 17, on the other hand, indicates the degree of contamination-wear of the ballast. The more contaminated the ballast bed, the higher the ballast hardness 16, 17. The compression force (channel 4) is proportional to the ballast hardness. Very low values of the compression force indicate either a new layer 14 (new ballast) or a singular place 9,10 with defective ballast. The lower the standard deviation σ.sub.V is, the lower the stiffness variations and the better the durability of the track position. The cross lines indicate the track kilometer (76, 400, . . . ).

[0045] An example of a ballast bed analysis report is shown below.

[0046] This is preceded by a statistical evaluation that provides general statements about the processed section. The analysis with machine learning system ML provides statements about the durability of the track position and the ballast bed hardness. If there are any faults 9,10, they are indicated with their type, exact location, length and characteristic values. The transmission of these data to the infrastructure manager or a responsible work scheduler forms the basis for the specification of the work instructions for the next pass. The analysis also gives an estimate of the track deterioration rate which is essential for the timing of the next pass. This data is also easily converted into a machine-readable form and transmitted.

[0047] Ballast Bed Report:

[0048] Statistical Evaluation

TABLE-US-00002 Packing Mean value Mean value operations Compressive Bedding Number of per sleeper force (kN) hardness (Nm) sleepers 1 18.53 264.66 472 2 15.62 194.80 101 3 0.00 0.00 0 >3 0.00 0.00 0 Mean value 18.31 254.82 573 Standard deviation 5.29 64.33

[0049] Durability of the track position

[0050] The ballast bed has defects. There is a low durability of the track layer.

[0051] The estimate results in a track deterioration rate of 1.6 mm/year.

[0052] Ballast bed hardness

[0053] The mean value of the ballast bed hardness was 254 Nm.

[0054] The ballast bed is in borderline highly contaminated condition. Track bed cleaning is recommended. A critical fault (with crushed/rounded ballast was found in the tamped area).

[0055] Replacement of the ballast in area 76.580 over 11 sleepers is recommended.

[0056] Fault 1

TABLE-US-00003 Type of the Number of fault Start End Length Sleepers Minimum 76.578 76.585 6.69 m 11

TABLE-US-00004 Minimum Mean value Maximum compression compression Slope Position force (kN) force (kN) (kN/m) 76,581 22.1 23.5 6.7

TABLE-US-00005 Location Position (km) Length (m) Sleepers Minimum 76.581 1.78 4.89 3 8 compression force Place of the 76.579 2.15 4.52 3 8 maximum compression drop