Analysis of pressure variation in wheel using statistical methods
To be published on November 21, 2019 by SAE International in United States
Event: NuGen Summit
Objective: The Objective of the research is to detect drop in level of pressure in the wheel with respect to nominal pressure using data obtained from speed sensors. The research discusses the standard procedure of experimentation to obtain data which eventually used to produce results. This procedure is taken from principles Design of Experiments. Statistical tools are used to analyze and give determining factors for pressure variation. Methodology: To study idea, we made use of two-wheeler platform and collected data of wheel speed sensors on both wheels. The idea is when there is any change in tire pressure the radius of the wheel also changes and usually this relation is direct. Hence, change in tire pressure changes the angular velocity of the wheel. In this approach wheel speed sensors are used to measure the angular speed for standard and reduced pressure conditions. The data obtained from the wheel speed sensor is analyzed through statistical methods and different determining values are calculated. These determining parameters are compared to see the variations in the pressure. To obtain the data certain sets of experiments are carried out. These experiments are outcome of standard principles of Design of Experiments. The data is the output of experiments and to make data independent of the rear and front wheel dynamics the ratio of speeds is taken. This ratio is called Relative Parameter (RP). Firstly, RP is filtered through moving average method and then analyzed through standard tools of mode, mean, standard deviation, and percentage population. These values are compared and result between standard and other conditions established. Results: Standard and Random Order of experiments from DOE will be presented. Statistical Data analysis results will be presented in the Paper. Limitations: One of the key limitations of this research is the dependency on the vehicle dynamics. Hence the parameters will differ with vehicle. Using the ratio of angular speed as RP makes it independent of wheel dynamics but at the same time also loosen the freedom of identifying the tire which is in inflated condition. Hence as a result we will be able to convey the reduced pressure but not be able identify in which tire. Novelty: Literature survey of this technology is mostly directed in the high-end vehicles and take use of lots of sensor. Our research only takes input from wheel speed sensors and use statistical tools to convey the information. This information has few accuracy concerns but is efficient method to detect the reduced pressure condition indirectly. Indirect detection also makes it cost effective. Conclusions: Research discussed above will be presented with appropriate proofs in the final manuscript. All the boundary conditions are defined for the idea. The key result will be determining factor of pressure variations in the wheels.