Automatic field data analyzer for closed-loop vehicle design

Rapidly increasing complexity of vehicle systems is calling for technologies to promptly analyze field problems, and effectively identify weakness in vehicle engineering design, in order to enhance product quality. Recent vehicular communication technologies allow for remote access to extensive amount of vehicle data in a cost-effective way, which enables in-depth field issue analysis. However, practical solutions are still lacking to effectively turn the massive amount of raw data into actionable design enhancement suggestions. In this paper, we propose a general framework, named Automatic Field Data Analyzer (AFDA), and related algorithms that analyze large volumes of field data, and identify root causes of faults by systematically making use of signal processing, machine learning, and statistical analysis approaches. AFDA evaluates vehicle system performance, generates feature vectors that represent different root causes of faults, and identifies the features that are most relevant to system performance fluctuation, which eventually reveals the underlying reasons for the faults. This paper presents a case study of AFDA in the application of vehicle battery, where gigabytes of real vehicle data are sifted through, and the root causes of field issues are identified. The results well match the findings from experts with years of experiences. The proposed data-based scheme and approaches can be generally applied to any vehicle systems.