Automatic Root Cause Analysis by Integrating Heterogeneous Data Sources

Abstract

This paper proposes a concept for automated root cause analysis, which integrates heterogeneous data sources and works in near real-time, in order to overcome the time-delay between failure occurrence and diagnosis. Such sources are (a) vehicle data, transmitted online to a backend and (b) customer service data comprising all historical diagnosed failures of a vehicle fleet and the performed repair actions. This approach focusses on the harmonization of the different granularity of the data sources, by abstracting them in a unified representation. The vehicle behavior is recorded by raw signal aggregations. These aggregations are representing the vehicle behavior in a respective time period. At discrete moments in time these aggregations are transmitted to a backend in order to build a history of the vehicle behavior. Each workshop session is used to link the historic vehicle behavior to the customer service data. The result is a root cause database. An automatic root cause analysis can be carried out by comparing the data collected for an ego-vehicle, the vehicle the failure situation occurred, with the root cause database. On the other hand, the customer service data can be analyzed by an occurred failure code and filtered by comparing the vehicle behavior. The most valid root cause is detected by weighting the patterns described above.