| Abstract
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<p>The heavy vehicle industry has to … <p>The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions.</p> upcoming maintenance decisions.</p>
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| Author
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Simon Thelin +
, Oskar Svensson +
, Stefan Byttner +
, Yuantao Fan +
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| Conference
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CAR 2017 - The International Congress of Automotive and Transport Engineering
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| DOI
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http://dx.doi.org/10.1088/1757-899X/252/1/012018 +
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| Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1161107
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| EndPage
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018 +
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| HostPublication
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IOP Conference Series: Materials Science and Engineering +
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| PublicationType
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Conference Paper +
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| StartPage
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012 +
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| Title
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Indirect Tire Monitoring System - Machine Learning Approach +
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| Volume
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252 +
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| Year
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2017 +
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| Has queryThis property is a special property in this wiki.
|
Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
, Publications:Indirect Tire Monitoring System - Machine Learning Approach +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
|
29 November 2017 21:21:57 +
|
