Air Brake Warning System for Commercial Vehicles

This investigation was completed as part of the ITS-IDEA Program which is one of three IDEA programs managed by the Transportation Research Board (TRB) to foster innovations in surface transportation. It focuses on products and result for the development and deployment of intelligent transportation systems (ITS), in support of the U.S. Department of Transportation's national ITS program plan. The other two IDEA programs areas are Transit-IDEA, which focuses on products and results for transit practice in support of the Transit Cooperative Research Program (TCRP), and NCHRP-IDEA, which focuses on products and results for highway construction, operation, and maintenance in support of the National Cooperative Highway Research Program (NCHRP). The three IDEA program areas are integrated to achieve the development and testing of nontraditional and innovative concepts, methods and technologies, including conversion technologies from the defense, aerospace, computer, and communication sectors that are new to highway, transit, intelligent, and intermodal surface transportation systems. The publication of this report does not necessarily indicate approval or endorsement of the findings, technical opinions, conclusions, or recommendations, either inferred or specifically expressed therein, by the National Academy of Sciences or the sponsors of the IDEA program from the United States Government or from the American Association of State Highway and Transportation Officials or its member states. Malfunctioning brakes represent the most common safety violation for commercial vehicles [l]. The objectives of this project were to research on-board measurement of a few brake-related parameters in order to monitor the effectiveness of air brakes, and to develop an algorithm for warning drivers and/or informing authorities of impending brake failure. The principal parameters that describe braking performance were identified as application pressure, weight, response pressures, temperature, adjustment, and speed. Initial testing quantified and confirmed the importance of these parameters. Multiple regression modeling techniques were developed to predict braking performance without the necessity to measure or monitor brake stroke or temperature. This was done by omitting stroke and temperature as independent variables from the model and " training " the vehicle model at optimum strokes and temperatures. Two models showed promise in preliminary testing and analysis: deceleration and brake lag. Further analysis after a second phase of full-scale testing under more realistic conditions showed that brake lag could not be reliably determined for a vehicle in service. Further, resolution of brake deterioration from the deceleration model was usually not sufficient at low brake temperatures, However, noticeable performance losses were …