Data-Driven Requirement Development - From Field Data to Reliability Requirements: Identifying and Analyzing High-Performing Automotive ECUs

Abstract

This thesis investigates a data-driven methodology for reliability-oriented requirement development in automotive electronics. Traditional automotive reliability engineering is primarily failure-focused, relying on warranty analysis and reactive investigation of defective components. However, such approaches provide limited understanding of why certain Electronic Control Units (ECUs) consistently demonstrate strong field reliability performance. The research initially aimed to analyze relationships between ECU operational conditions and field reliability behavior through classical data-driven analysis. However, the required centralized operational-condition dataset was not available within the industrial data environment. Consequently, the study evolved toward a practical industrial screening methodology based on available enterprise engineering and field-quality data resources. The proposed framework integrates multiple industrial datasets, including the KDP Engineering Database (KDP), Quality Follow-Up (QFU) warranty repair records, the Early Warning System (EWS), and procurement-related production volume data. By combining field repair occurrence with market exposure normalization, ECU populations with exceptionally low repair occurrence relative to deployment volume were identified. Selected ECUs subsequently underwent hardware-oriented engineering investigation, including open-lid assessment, Printed Circuit Board (PCB)-level visual inspection, and review of available Design Verification (DV) and Product Validation (PV) documentation. The investigation focused on identifying recurring robustness-related engineering characteristics rather than performing direct failure analysis. Observed features included reinforced PCB mechanical support structures, controlled PCB cleanliness, environmental protection strategies, and evidence of robustness-oriented validation practices. The work demonstrates how multiple industrial engineering and field-quality data sources can be systematically combined to support evidence-based reliability investigation and practical reliability requirement development in automotive electronics.