United States Department of Transportation
i
A Diagnostic Machine Learning Model for Air Brake Systems in Commercial Vehicles [supporting dataset]
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2023-09-12
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Alternative Title:Development of a Diagnostic System for Air Brakes in Autonomous and Connected Trucks (04-100)
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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.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.8083359). NTL staff last accessed this dataset at its repository URL on 2024-02-02. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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