Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions

Bibliographic Details
Main Author:	Xu, Su
Language:	English
Published:	University of Cincinnati / OhioLINK 2011
Subjects:	Mechanical Engineering tire pressure TPMS prognostics real time dynamic operating conditions
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1318607916

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-ucin13186079162021-08-03T06:15:05Z Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions Xu, Su Mechanical Engineering tire pressure TPMS prognostics real time dynamic operating conditions Since maintaining normal tire inflation would dramatically reduce the tire maintenance cost, which is the largest one for fleets, many researchers have invested in means of monitoring and controlling tire inflation. One of the systems is Tire Pressure Monitoring System (TPMS) which usually has the functionalities of storing the temperature and pressure data and setting up an alarm when either type of data achieves a preset threshold. This system achieves a simple condition based monitoring (CBM) by monitoring the pressure on a real-time basis. However, all tires work under dynamic operating conditions causing pressure data to fluctuate with operating conditions in a big range, so the single pressure threshold faces a tradeoff between low sensitivity and high false alarm rate. To conquer this dilemma and achieve a higher-level CBM, either a dynamic threshold or a true tire condition extracted from its superficial operating condition is needed. In this research, prognostic health management (PHM) techniques were employed to develop a systematic real-time methodology for tire health assessment and prediction. The proposed methodology could extract the current real tire condition out of its operating condition and predict its future trend based on the historical tire condition. The raw TPMS data which includes pressure and temperature was taken as the input to the model. The ideal gas law was applied on the data to reduce the temperature effect in pressure on all the tires. Then health assessment which includes individual tire self-comparison and peer based comparison was conducted. Three models (Logistic Regression (LR), Self-Organizing Map – Mean Quantization Error (SOM-MQE), Distance Based Assessment (DBA)) were employed and compared during self-tire comparison to eliminate the data variance between tires from different positions; and peer based comparison was applied to eliminate the fluctuation within each tire. Meanwhile another anomaly detection algorithm was developed to identify system changes (tire inflation, tire fixation), after which health assessment models are retrained to obtain robust outputs from health assessment models. When an initial drop was seen from the current tire health, a prediction would be triggered to infer the future health trend. Different prediction algorithms were compared in this step and no single model was accurate enough. Thus a model fusion was employed to combine single prediction models together to achieved better prediction results. Real fleet TPMS data was used in the case study to validate the proposed methodology. Two conclusions were drawn: 1. The proposed research methodology is able to evaluate the actual tire health condition on a real-time basis. 2. The tire health generated from this model was improved greatly over the outputs from previous methodologies; and the improvement could be seen from both shortened leakage detection time and higher prediction accuracy. 2011 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1318607916 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1318607916 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Mechanical Engineering tire pressure TPMS prognostics real time dynamic operating conditions
spellingShingle	Mechanical Engineering tire pressure TPMS prognostics real time dynamic operating conditions Xu, Su Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
author	Xu, Su
author_facet	Xu, Su
author_sort	Xu, Su
title	Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
title_short	Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
title_full	Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
title_fullStr	Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
title_full_unstemmed	Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions
title_sort	techniques for real-time tire health assessment and prognostics under dynamic operating conditions
publisher	University of Cincinnati / OhioLINK
publishDate	2011
url	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1318607916
work_keys_str_mv	AT xusu techniquesforrealtimetirehealthassessmentandprognosticsunderdynamicoperatingconditions
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Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating Conditions

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