]> 2026-04-04T20:48:25+02:00 Publications:Warranty Claim Rate Prediction using Logged Vehicle Data 500 en <p>Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.</p> 19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, 3-6 September, 2019 http://dx.doi.org/10.1007/978-3-030-30241-2_55 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1376729 674 Lecture Notes in Computer Science Journal Paper 663 Warranty Claim Rate Prediction using Logged Vehicle Data 11804 2019 2019-12-10T21:23:07Z 2458828.3910532 Warranty Claim Rate Prediction using Logged Vehicle Data 500 0 plainlist list 1 [[:Publications:Warranty Claim Rate Prediction using Logged Vehicle Data]] Warranty Claim Rate Prediction using Logged Vehicle Data# QUERY9a59ac1c8b7d731cd03ec772aa19d287 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Warranty Claim Rate Prediction using Logged Vehicle Data# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Warranty Claim Rate Prediction using Logged Vehicle Data# ERRe24f17e090c98448f3de99d03182b7b6