Development of a Verification Process for Truck Pneumatic Systems

Abstract

This thesis investigates the development of a verification process for pneumatic systems in heavy-duty trucks, with a focus on air-leakage detection at the End-of-Line stage. The study was conducted at Volvo Group Trucks Technology & Industrial Division in Tuve, Sweden, and evaluates the feasibility of integrating digital pressure decay measurement equipment to improve quality assurance, reduce operator dependency, and enable data-driven decision-making. A combined methodology was applied, including a literature review, stakeholder interviews, benchmarking across production sites, and experimental testing utilizing PDCA methodology. Key process parameters were assessed through statistical hypothesis testing, including equivalence testing and paired one- and two-tailed t-tests. These were utilized to evaluate the effects of test duration, stabilization time, filling method, and subsystem activation on leakage detection accuracy. The results demonstrate that pressure decay testing can be significantly optimized. Equivalent detection accuracy was achieved with reduced test durations compared to the current 4-minute reference, enabling cycle time reductions of up to 2–3 minutes under controlled conditions. However, the inclusion of a stabilization phase was shown to be critical for reliable measurements due to transient pressure variations caused by thermodynamic effects. The required stabilization time was also found to vary across trucks, supporting the implementation of a dynamic threshold-based approach to balance measurement reliability and efficiency. Furthermore, subsystem activation was shown to influence measured pressure decay, indicating the need to adapt leakage thresholds depending on test conditions. Based on these findings, a standardized verification process with automated OK/NOK decision-making is proposed, integrating digital measurement equipment into the existing production environment. The process improves consistency, enables data collection, and reduces operator dependency. While the concept is technically feasible, further validation is required before full-scale implementation.