Publications:Warranty Claim Rate Prediction using Logged Vehicle Data
From ISLAB/CAISR
| Title | Warranty Claim Rate Prediction using Logged Vehicle Data |
|---|---|
| Author | Reza Khoshkangini and Sepideh Pashami and Sławomir Nowaczyk |
| Year | 2019 |
| PublicationType | Journal Paper |
| Journal | Lecture Notes in Computer Science |
| HostPublication | |
| Conference | 19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, 3-6 September, 2019 |
| DOI | http://dx.doi.org/10.1007/978-3-030-30241-2_55 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1376729 |
| Abstract | 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. |
