Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes
| dc.contributor.author | Forsman, Mathias | |
| dc.contributor.author | Yang, Yihan | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Strüber, Daniel | |
| dc.contributor.supervisor | Heyn, Hans-Martin | |
| dc.date.accessioned | 2024-03-05T11:32:38Z | |
| dc.date.available | 2024-03-05T11:32:38Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | In the current automotive industry, problem identification is a reactive process. It starts when the customer experiences a vehicle problem and goes to the workshop. Subsequently, all the problem-related data will be collected from the workshop and forwarded to the vehicle manufacturer. After that, the engineers will start looking into the problem and figuring out the root cause with the cooperation from internal and external departments. It is a case-sensitive procedure and each unforeseen factor may further prolong the process and affect customer satisfaction. This study cooperates with Volvo Cars to explore the possibility of providing a proactive data-driven insight into the problem identification process in the automotive system using the Diagnostic Troubleshooting Code (DTC). The purpose is to identify the most affected group before the problem scales and affects most of the customers. This study involves two case studies and one laboratory experiment. The first-round case study helps to gain a better understanding of the current problem identification process. Also, some challenges and limitations encountered in this process have been identified. Other than these, five cases, including three different car parts: the car part A unit, the climatization system, and an add-on system, have been collected to conduct the following laboratory experiment. In total, four models are constructed and refined using several basic and machine learning techniques, including Group-by, Linear Regression, and K-means Clustering. This process evaluates different models’ capabilities to provide early warnings and the corresponding correctness. It further assesses each technique’s strengths and limitations in predicting the most affected group. The last case study serves as an evaluation action to receive feedback from the industrial experts about model performance and discuss the potential solution to integrate the model construction into the current workflow. In the end, a data-driven approach has been proposed and comprehensively described. The influencing factors, advantages, and limitations of the research have also been discussed, leading to various interesting directions for future research. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307598 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Automotive Industry | |
| dc.subject | Early Problem Identification | |
| dc.subject | Diagnostic Trouble Code | |
| dc.subject | Case Study | |
| dc.subject | Laboratory Experiment | |
| dc.subject | Machine Learning | |
| dc.subject | Linear Regression | |
| dc.subject | K-means Clustering | |
| dc.title | Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Software engineering and technology (MPSOF), MSc |