A self-oscillating resonant inverter uses current-transformer feedback to avoid manual tuning and costly precision inductors in axle counters.
Continuous resonance tracking and transimpedance analysis improve rail vehicle detection despite interference and vehicle variability.
An optical fiber and spring-based rail contact element detects mounting state through reflected or absorbed light, reducing complexity and fiber damage risk.
Adaptive current monitoring helps track circuits distinguish occupied sections from broken rails despite resistance changes from weather and rail conditions.
Threshold-triggered vibration sensing drives flashing LEDs and audible alerts to warn of unintended cargo or railcar movement in noisy settings.
Prior activation and time-limited masking use a virtual track section to separate transient counting-point faults from persistent failures.
Axle spacing patterns from axle counters classify passenger and freight trains without extra sensors, supporting safer mixed-traffic control.
Single-stage deep learning detects and classifies axle counter time-series signals in one step to cut false counts from interference and speed variation.
Electrical signals sent through rails use time-domain reflectometry to detect train position, speed, rail breaks, and ballast issues without exposed cabling.