Abstract
Safely introducing autonomy to trucks requires monitoring their brake systems continuously. Out-of-adjustment push rods and leakages in the air brake system are two major reasons for increased braking distances in trucks, resulting in safety violations. Air leakages can occur due to small cracks or loose/improperly fit couplings, which do not affect overall braking capacity but contribute greatly to increased braking lag and reduced maximum braking torque at the wheels. Similarly, an increased stroke of push rod leads to a larger delay in brake response and a smaller brake torque value at the wheels. Currently, an air brake system’s condition is monitored manually by measuring the push rod offset and inspecting the system’s couplings and hoses for air leakages. These inspections are highly labor intensive, subjective, time consuming, and inaccurate in quantifying adversely affected braking systems. An onboard diagnostic device that can monitor air brake health would be crucial in preventing road accidents. The focus of this report is to help develop a diagnostic system that facilitates enforcement and pre-trip inspections and continuous onboard monitoring of trucks by developing a model for its multi-chamber braking system using machine learning; this model can be used to estimate the severity of leakage and the push rod stroke using real-time brake pressure transients. The novel approach of a gradient descent model that predicts the air brake system air leakage rate using pressure transients at the brake chamber was developed and experimentally corroborated.
Project Highlights
- A laboratory test bench has been built to prototype the development of a diagnostic systems for air brakes in autonomous and non-autonomous trucks.
- A novel leak detection algorithm, using machine learning techniques, has been developed to estimate the mass rate of air leaking from the line. This estimate will help us quantify the resulting increase in stopping distance of the truck.
Final Report
EWD & T2 Products
Ganeshan, Reyshwanth(2023). Diagnostics Using Machine Learning for Air Brakes in Commercial Vehicles. Master’s thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /200049.
Soundararajan, Jaikrishna(2021). Minimum time headway and stabilizing control gains for vehicle platoons with time delay. Master’s thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969.1 /195167
Foster, Rex Allen (2021). Modelling the Pressure Transients and Pushrod Extension of a Multi-Chamber Pneumatic Braking System. Master’s thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /195847.
Student Impact Statement(pdf): Three Master students in Mechanical Engineering worked on this project (Reyshwanth Ganesan, Jaikrishna Soundararajan and Rex Allen Foster). This file contains a statement by (Reyshwanth Ganesan and Jaikrishna Soundararajan as to the impact this project had on their education and workforce development.
Experimental setup of Airbrake Systems and the leak detection measurement system in laboratory.
Created models to predict how airbrake systems will respond to brake pedal input.
Algorithm created for detection leaks in air brake systems.
Presentations/Publications
Liu, S. Rathinam and S. Darbha, “Lateral Control of an Autonomous Car with Limited Preview Information,” 2019 18th European Control Conference (ECC), Naples, Italy, 2019, pp. 3192-3197. doi: 10.23919/ECC.2019.8796007
Darbha, S. (2019). Development of a Diagnostic System for Air Brakes in Trucks, March 29, 2019, Nonlinear Analysis & Dynamical Systems Seminar Series, UT Dallas. Presentation available here.
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/9SWAHY.
Research Investigators (PI*)
Swaroop Darbha (TAMU/TTI)*
K. R. Rajapol (TAMU/TTI)
Project Information
Start Date: 2019-01-01
End Date: 2021-08-31
Status: Complete
Grant Number: 69A3551747115
Total Funding: $ $340,000
Source Organization: Safe-D National UTC
Project Number: 04-100
Safe-D Theme Areas
Automated Vehicles
Connected Vehicles
Transportation as a Service
Safe-D Application Areas
Freight and Heavy Vehicle
Planning for Safety
Vehicle Technology
More Information
Sponsor Organization
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Performing Organization
Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135
USA