A Diagnostic Machine Learning Algorithm for Air Brakes in Commercial Vehicles

To safely introduce any level of autonomy to trucks, the health of their brake systems needs to be monitored continuously. Out-of-adjustment push rods and leakages in the air brake system are two major reasons for increased braking distances in trucks, and result in safety violations. Air leakages can occur due to small cracks or loose/improperly fit couplings which do not affect the overall braking capacity but contribute greatly to increasing the braking lag and reducing the maximum braking torque at the wheels. Similarly, an increased stroke of push rod leads to a larger delay in brake response and a smaller value of the brake torque at the wheels. Currently, the condition of an air brake system is inspected manually by measuring the push rod offset, checking the couplings and hoses of the system for air leakages. These inspections are highly labor intensive, subjective, time consuming and do not accurately quantify how adversely the braking system is affected. Having an on-board diagnostic device that can monitor the health of air brakes would be crucial in the prevention of road accidents, especially when considering any level of automation and comply with FMSCA safety requirements. The focus of this paper is to aid the development of such a diagnostic system that facilitates enforcement and pre-trip inspections and continuous on-board monitoring of trucks through the development of a model for its multi-chamber braking system; this model can be used to estimate the severity of leakage and the push rod stroke using real time brake pressure transients. A machine learning algorithm for estimating the air leakage in the air brake system and its experimental corroboration is presented in this paper. Copyright (c) 2024 The Authors.