Extending electronic shot counters with machine learning-based ammunition discrimination for targeted maintenance

Purpose Weapon fleets degrade differently depending on usage. Electronic shot counters provide armorers with accurate tracking and allow more efficient preventive maintenance practices. We propose a machine learning technique for general-purpose ammunition type discrimination, thereby enhancing targeted maintenance. Design/methodology/approach We study an experimental shot counter deployment to understand its impact on maintenance practices. We then extend the existing EDGAR machine learning technique to solve the discrimination problem in a generic way on a weakly-labelled dataset, requiring only the total counts for each shot type. By repurposing intermediate neural network activations, we simplify training and minimize computational overhead. We evaluate our approach against a widely used live/blank discrimination algorithm. Findings We show a 94% improvement in instance-level error rate and perfect burst-firing performance. This generalizes across weapon platforms without hyperparameter adjustment. The feature incurs as little as an 8% overhead (2.8 ms on a 64 MHz ARM Cortex-M4F). Originality/value Compared to existing techniques, it promises applicability to a broader range of weapon configuration discrimination tasks, including platforms previously deemed too complex or constrained. Additionally, we examine how performance scales with dataset size to offer practical data collection guidelines, a major challenge in this field. This technique supports a new generation of shot counters and targeted maintenance, thereby reducing costs, preventing incidents and increasing operational availability.