Systems and methods for detecting brake system anomalies
A method may include detecting a first pressure and a second pressure of a fluid in a brake pipe of a vehicle system that includes a plurality of vehicles and extends from a lead vehicle to an end vehicle. The first pressure may be measured in the lead vehicle and the second pressure may be measured in the end vehicle. The method may further include determining a pressure differential signature between the first pressure and the second pressure and evaluating the pressure differential signature with a machine learning model to determine whether a blockage or a leak exists in the brake pipe. A system may include one or more processors configured to detect a first pressure and a second pressure of a fluid in a brake pipe. The one or more processors may be further configured to determine a pressure differential signature between the first pressure and the second pressure and evaluate the pressure differential signature with a machine learning model to determine whether a blockage exists in the brake pipe.
1. A method, comprising:
detecting a first pressure and a second pressure of a fluid in a brake pipe of a vehicle system that includes a plurality of vehicles, the brake pipe extending from a lead vehicle of the plurality of vehicles to an end vehicle of the plurality of vehicles, the brake pipe including a plurality of sections, and the first pressure being measured in the lead vehicle and the second pressure being measured in the end vehicle;
determining a pressure differential signature between the first pressure and the second pressure;
evaluating the pressure differential signature with a machine learning model including a plurality of hidden layers, each hidden layer of the plurality of hidden layers corresponding to a different section of the plurality of sections, to determine whether a blockage or a leak exists in the brake pipe and which section or sections of the plurality of sections the blockage or the leak exists; and
responsive to determining a blockage or a leak exists, adjusting a pressure of the fluid in the brake pipe at a location past the section or sections for which the blockage or the leak is determined.
2. The method of claim 1 , further comprising:
detecting a flow rate of the fluid in the brake pipe; and
evaluating the flow rate of the fluid with the machine learning model to determine whether one or more of the blockage or the leak exists in the brake pipe.
3. The method of claim 2 , further comprising:
increasing a pressure of the fluid in the brake pipe at a location past a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
4. The method of claim 2 , further comprising:
detecting one or more third pressures in the brake pipe at one or more locations between the first pressure and the second pressure; and
determining a plurality of pressure differential signatures between one or more of (a) the first pressure and one or more of the third pressures, (b) the second pressure and one or more of the third pressures, or (c) a plurality of the third pressures; and
evaluating the plurality of the pressure differential signatures with the machine learning model to determine whether the blockage or the leak exists in the brake pipe.
5. The method of claim 4 , further comprising:
detecting one or more flow rates of the fluid in the brake pipe between (d) one or more of the first pressure and one or more of the third pressures, (e) the second pressure and the one or more of the third pressures, or (f) the two or more of the third pressures; and
evaluating one or more of the flow rates of the fluid with the machine learning model to determine whether one or more of the blockage or the leak exists in the brake pipe.
6. The method of claim 5 , further comprising:
reducing a fourth pressure of the fluid in the brake pipe at a first location from the first pressure to the second pressure:
determining a decreasing pressure differential signature from the first location to a second location; and
evaluating the decreasing pressure differential signature from the first location to the second location with the machine learning model to determine whether the blockage or the leak exists in the brake pipe between the first location and the second location.
7. The method of claim 6 , further comprising:
raising the fourth pressure of the fluid in the brake pipe from the second pressure to the first pressure from the first location to the second location;
determining an increasing pressure differential signature from the first location to the second location; and
evaluating the increasing pressure differential signature with the machine learning model to determine whether one or more of the leak or the blockage exists between the first location and the second location.
8. The method of claim 7 , wherein the first location is in the lead vehicle and the second location is in the end vehicle.
9. The method of claim 1 , further comprising:
increasing or decreasing a third pressure of the fluid in the brake pipe at a location past a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
10. The method of claim 1 , further comprising:
pulsing a pressure of the fluid in the brake pipe at allocation before a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
11. The method of claim 1 , further comprising:
providing a notice to an operator of the vehicle system responsive to the blockage or the leak being detected.
12. The method of claim 11 , further comprising:
applying a penalty brake to one or more of the vehicles of the vehicle system in an absence of a response from the operator from the notice that is provided.
13. The method of claim 1 , further comprising:
adding the first pressure, the second pressure, and the pressure differential signature to the machine learning model.
14. The method of claim 2 , further comprising:
adding the flow rate to the machine learning model.
15. A system, comprising:
one or more processors configured to:
detect a first pressure and a second pressure of a fluid in a brake pipe of a vehicle system that includes a plurality of vehicles, the brake pipe extending from a lead vehicle of the plurality of vehicles to an end vehicle of the plurality of vehicles, the brake pipe including a plurality of sections, and the first pressure being measured in the lead vehicle and the second pressure being measured in the end vehicle;
determine a pressure differential signature between the first pressure and the second pressure;
evaluate the pressure differential signature with a machine learning model including a plurality of hidden layers, each hidden layer of the plurality of hidden layers corresponding to a different section of the plurality of sections, to determine whether a blockage or a leak exists in the brake pipe and which section or sections of the plurality of sections the blockage or the leak exists; and
responsive to determining a blockage or a leak exists, adjusting a pressure of the fluid in the brake pipe at a location past the section or sections for which the blockage or the leak is determined.
16. The system of claim 15 , wherein the one or more processors are further configured to:
detect a flow rate of the fluid in the brake pipe; and
evaluate the flow rate of the fluid with the machine learning model to determine whether one or more of the blockage or the leak exists in the brake pipe.
17. The system of claim 16 , wherein the one or more processors are further configured to:
increase a pressure of the fluid in the brake pipe at a location past a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
18. The system of claim 16 , wherein the one or more processors are further configured to:
detect one or more third pressures in the brake pipe at one or more locations between the first pressure and the second pressure; and
determine a plurality of pressure differential signatures between one or more of (a) the first pressure and one or more of the third pressures, (b) the second pressure and one or more of the third pressures, or (c) a plurality of the third pressures; and
evaluate the plurality of the pressure differential signatures with the machine learning model to determine whether the blockage or the leak exists in the brake pipe.
19. The system of claim 18 , wherein the one or more processors are further configured to:
detect one or more flow rates of the fluid in the brake pipe between (d) one or more of the first pressure and one or more of the third pressures, (e) the second pressure and the one or more of the third pressures, or (f) the two or more of the third pressures; and
evaluate one or more of the flow rates of the fluid with the machine learning model to determine whether one or more of the blockage or the leak exists in the brake pipe.
20. The system of claim 19 , wherein the one or more processors are further configured to:
reduce a fourth pressure of the fluid in the brake pipe at a first location from the first pressure to the second pressure;
determine a decreasing pressure differential signature from the first location to a second location; and
evaluate the decreasing pressure differential signature from the first location to the second location with the machine learning model to determine whether the blockage or the leak exists in the brake pipe between the first location and the second location.
21. The system of claim 20 , wherein the one or more processors are further configured to:
raise the fourth pressure of the fluid in the brake pipe from the second pressure to the first pressure from the first location to the second location;
determine an increasing pressure differential signature from the first location to the second location; and
evaluate the increasing pressure differential signature with the machine learning model to determine whether one or more of the leak or the blockage exists between the first location and the second location.
22. The system of claim 21 , wherein the first location is in the lead vehicle and the second location is in the end vehicle.
23. The system of claim 15 , wherein the one or more processors are further configured to:
increase or decrease a third pressure of the fluid in the brake pipe at a location past a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
24. The system of claim 15 , wherein the one or more processors are further configured to:
pulse a pressure of the fluid in the brake pipe at a location before a detected blockage or a detected leak in a direction from the lead vehicle to the end vehicle.
25. The system of claim 15 , wherein the one or more processors are further configured to:
provide a notice to an operator of the vehicle system responsive to the blockage or the leak being detected.
26. The system of claim 25 , wherein the one or more processors are further configured to:
apply a penalty brake to one or more of the vehicles of the vehicle system in an absence of a response from the operator to the notice that is provided.
27. The system of claim 15 , wherein the one or more processors are further configured to:
add the first pressure, the second pressure, and the pressure differential signature to the machine learning model.
28. The system of claim 16 , wherein the one or more processors are further configured to:
add the flow rate to the machine learning model.
29. A brake system, comprising:
a brake pipe extending along a plurality of vehicles in a vehicle system that includes a lead vehicle and an end vehicle, the brake pipe extending from the lead vehicle to the end vehicle, and the brake pipe including a plurality of sections; and
one or more processors configured to:
detect a flow rate of fluid in the brake pipe;
evaluate the flow rate with a machine learning model including a plurality of hidden layers, each hidden layer of the plurality of hidden layers corresponding to a different section of the plurality of sections, to determine whether one or more of a blockage or a leak exists in the brake pipe and which section or sections of the plurality of sections the one or more of the blockage or the leak exists; and
responsive to determining a blockage or a leak exists, adjusting a pressure of the fluid in the brake pipe at a location past the section or sections for which the blockage or the leak is determined.
30. The brake system of claim 29 , wherein the one or more processors are further configured to:
increase a pressure of the fluid in the brake pipe at a location past the blockage or the leak that is detected in a direction from the lead vehicle to the end vehicle.
31. The brake system of claim 29 , wherein the one or more processors are further configured to:
detect a first pressure and a second pressure;
detect one or more third pressures in the brake pipe at one or more locations between the first pressure and the second pressure;
determine a plurality of pressure differential signatures between one or more of (a) the first pressure and one or more of the third pressures, (b) the second pressure and one or more of the third pressures, or (c) a plurality of third pressures; and
evaluate the plurality of the pressure differential signatures with the machine learning model to determine whether the blockage or the leak exists in the brake pipe.
32. The brake system of claim 31 , wherein the one or more processors are further configured to:
detect the flow rate of the fluid in the brake pipe between (d) one or more of the first pressure and one or more of the third pressures, (e) the second pressure and the one or more of the third pressures, or (f) the two or more of the third pressures; and
evaluate the flow rate of the fluid with the machine learning model to determine whether one or more of the blockage or the leak exists in the brake pipe.
33. The brake system of claim 32 , wherein the one or more processors are further configured to:
reduce a fourth pressure of the fluid in the brake pipe at a first location from the first pressure to the second pressure;
determine a decreasing pressure differential signature from the first location to a second location; and
evaluate the decreasing pressure differential signature from the first location to the second location with the machine learning model to determine whether the blockage or the leak exists in the brake pipe between the first location and the second location.
34. The brake system of claim 33 , wherein the one or more processors are further configured to:
raise the fourth pressure of the fluid in the brake pipe from the second pressure to the first pressure form the first location to the second location;
determine an increasing pressure differential signature from the first location to the second location; and
evaluate the increasing pressure differential signature with the machine learning model to determine whether one or more of the leak or the blockage exists between the first location and the second location.
35. The brake system of claim 34 , wherein the first location is in the lead vehicle and the second location is in the end vehicle.
36. The brake system of claim 29 , wherein the one or more processors are further configured to:
increase or decrease a third pressure of the fluid in the brake pipe at a location past the blockage or the leak that is detected in a direction from the lead vehicle to the end vehicle.
37. The brake system of claim 29 , wherein the one or more processors are further configured to:
pulse a pressure of the fluid in the brake pipe at allocation before the blockage or the leak that is detected in a direction from the lead vehicle to the end vehicle.
38. The brake system of claim 29 , wherein the one or more processors are further configured to:
provide a notice to an operator of the vehicle system responsive to the blockage or the leak being detected.
39. The brake system of claim 38 , wherein the one or more processors are further configured to:
apply a penalty brake to one or more of the vehicles of the vehicle system in an absence of a response from the operator to the notice that is provided.
40. The brake system of claim 31 , wherein the one or more processors are further configured to:
add one or more of the first pressure, the second pressure, the plurality of pressure differential signatures, or the flow rate to the machine learning model.
41. The method of claim 1 , wherein the blockage is at least one of a partial blockage or a total blockage, and wherein the blockage is defined by a kink.
42. The system of claim 15 , wherein the blockage is at least one of a partial blockage or a total blockage, and wherein the blockage is defined by a kink.
43. The brake system of claim 29 , wherein the blockage is at least one of a partial blockage or a total blockage, and wherein the blockage is defined by a kink.