Reliability analysis of heavy duty vehicle components based on long term warranty data

In this work a big automotive company truck's warranty data is used to perform reliability analysis of heavy duty vehicle components. Automotive manufacturers produce and sale the vehicle to the customer. Warranty period of vehicle starts when customer buy a new vehicle. Every manufacturer has different warranty policies. Warranty data occurs while the customer brings their vehicles to the automotive dealer service to fix their problems during the warranty period. Reliability analysis was completed on the problems within 24 months which is the warranty period of the truck. Manufacturers spend more money per year to resolve warranty related issues. To reduce this huge amount of warranty related costs, they have always been interested in knowing their product reliability and failure causes. The knowledge of product reliability will help them to designing warranty policies. Firstly, keywords of thesis definition are completed such as reliability, failure, failure rate. Then functions, statistical distributions and types of analysis which is used in reliability analysis are described in details. Secondly, mentioned that importance, definition and types of warranty data. Warran-ty data collection and warranty data analysis are explained as well. In results & conclusions, five components have been selected to perform analysis via Weibull distribution which is the best fit distribution according to the actual data. Components cumulative hazard curves determined to define failure rates such as increasing failure rate, constant failure rate and decreasing failure rate according to the Weibull shape parameter. These provide information about how the product can be developed. If the result is decreasing failure rate, we can comment that manufac-turing issues which is called also early life failures. If the result is constant failure rate, we can comment that accident or random failure events (e.g. stones pecking against vehicle body). If the result is increasing failure rate, we can comment that wear out or degradation of a part due to fatigue, corrosion, wear, creep. As a continuation of cumulative hazard curve, components reliability and failure probability curves have been determined. Secondly, service life of each component has been compared based on different model year. This provides to discover burn issues and components life situation over the years. Lastly, components reliability and failure probability performance metrics have been compared according to the vehicle types. We saw that an analysis based on vehicle types is very important to hit the right notes to fix the problems.