Modeling the pneumatic subsystem of a S-cam air brake system

The air brake system is one of the critical components in ensuring the safe operation of any commercial vehicle. This work is directed towards the development of a fault-free model of the pneumatic subsystem of the air brake system. This model can be used in brake control and diagnostic applications. Current enforcement inspections are done manually and hence are time consuming and subjective. The long-term objective is to develop a model-based, performance-based diagnostic system that will automate enforcement inspections and help in monitoring the condition of the air brake system. Such a diagnostic system can update the driver on the performance of the brake system during travel and with recent advancements in communication technology, this information can be remotely transferred to the brake inspection teams. Since this system is performance-based, it will eliminate the subjective nature of visual inspections. The first step in the development of such a diagnostic system is to obtain a fault-free model of the air brake system. The model of the pneumatic subsystem correlates the pressure transients in the brake chamber with the brake pedal actuation force and the brake valve plunger displacement. An experimental test bench was set up at Texas A&M University and the experimental data is used to corroborate the results obtained from the model

A diagnostic system for air brakes in commercial vehicles

This dissertation deals with the development of a model-based diagnostic system for air brake systems that are widely used in commercial vehicles, such as trucks, tractor-trailers, buses, etc. The performance of these brake systems is sensitive to maintenance and hence they require frequent inspections. Current inspection techniques require an inspector to go underneath a vehicle to check the brake system for possible faults, such as leaks, worn brake pads, out-of-adjustment of push rods, etc. Such inspections are time consuming, labor intensive and difficult to perform on vehicles with a low ground clearance. In this context, the development of an onboard/ handheld diagnostic tool for air brakes would be of significant value. Such a tool would automate the brake inspection process, thereby reducing the inspection time and improving the safety of operation of commercial vehicles. In this dissertation, diagnostic schemes are developed to automatically detect two important and prevalent faults that can occur in air brake systems â leaks and out-of-adjustment of push rods. These diagnostic schemes are developed based on a nonlinear model for the pneumatic subsystem of the air brake system that correlates the pressure transients in the brake chamber with the supply pressure to the treadle valve and the displacement of the treadle valve plunger. These diagnostic schemes have been corroborated with data obtained from the experimental facility at Texas A&M University and the results are presented. The response of the pneumatic subsystem of the air brake system is such that it can be classified as what is known as a âÂÂSequential Hybrid SystemâÂÂ. In this dissertation, the term âÂÂhybrid systemsâ is used to denote those systems whose mathematical representation involves a finite set of governing ordinary differential equations corresponding to a finite set of modes of operation. The problem of estimating the push rod stroke is posed as a parameter estimation problem and a transition detection problem involving the hybrid model of the pneumatic subsystem of the air brake system. Also, parameter estimation schemes for a class of sequential hybrid systems are developed. The efficacy of these schemes is illustrated with some examples

Integration of exponential smoothing with state space formulation for bus travel time and arrival time prediction

In recent years, the problem of bus travel time prediction is becoming more important for applications such as informing passengers regarding the expected bus arrival time in order to make public transit more attractive to the urban commuters. One of the popular techniques reported for such prediction is the use of time series analysis. Most of the studies on the application of time series techniques for bus arrival time prediction used Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) models, which are presently not suited for real time implementation. This is mainly due to the necessity and dependence of ARIMA models on a time series modelling software to execute. Moreover, the ARIMA model building process is time consuming, making it difficult to use for real-time implementations. Alternatively, Exponential Smoothing (ES) methods can be used, as they are easy to understand and implement when compared to ARIMA models. The present study is an attempt in this direction, where the basic equation of ES is used, as the state equation with Kalman filtering to recursively update the travel time estimate as the new observation becomes available. The proposed algorithm of state space formulation of ES with Kalman filtering for bus travel time and arrival time prediction was field tested using 105 actual bus trips data along a particular bus route from Chennai, India. The results are promising and a comparison of the proposed algorithm with ES alone without state space formulation and Kalman filtering has also been performed. An information system based on a webpage for real-time display of bus arrival times has been designed and developed using the proposed algorithm. First published online: 12 Oct 201