Automatic field data analyzer for closed-loop vehicle design

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.