Diagnostics of Intermittent Errors

Abstract: Intermittent faults/errors are infamous for being among the most challenging errors to diagnose. It is estimated that more than 80% of the total number of errors in real systems are intermittent errors. Previous research on intermittent errors suggests that they are the prelude to permanent faults. There seems to be a vast knowledge gap in general regarding intermittent errors, both in academia and industry. The term "No Fault Found" might have ingrained a culture of acceptance regarding faults that intermittent errors might cause. This master thesis aims to develop a generic algorithm for diagnostics of intermittent errors that allows for the early isolation of failing sensors, especially at the end of their life spans. It is desirable that Scania can identify intermittent errors efficiently to save maintenance costs and keep customer satisfaction high. Multiple intermittent error detection and diagnostics methods have been produced and tested through simulations in MATLAB. The results suggest that the most important factors when introducing algorithms for intermittent error detection are the sensors' self-diagnostic capabilities and their communication protocol. The developed algorithms can be used for efficient fault isolation, obtaining valuable data for research, and triggering Diagnostic Trouble Codes (DTCs) when the impact of the errors is too significant, which allows for proactive replacement. If the algorithms are introduced as suggested in this master thesis, the knowledge gap can be filled. Consequently, Scania can use the increased knowledge to further improve the algorithms for better detection of intermittent errors and increase the overall performance of Scania vehicles.