Advanced health monitoring of unpowered heavy haul and general freight railway wagons is still an emerging field, limited by the lack of electrical power on-board the vehicles and the cost of instrumenting massive fleets. This paper presents a dynamic verification of an on-board wheel flat detection technique that, using analogue signal processing, reduces the power consumption and hardware costs of condition monitoring sensor nodes. A 1:4 scale bogie test rig was used to record bearing adapter acceleration signals of healthy and defective wheelsets. The data were then used to verify the wheel flat detection technique, which effectively distinguished between healthy and defective acceleration signals, using analogue computing only, without the need for software or a complex algorithm and corresponding hardware. This technique is promising for further development of low-cost and ultra-low power sensor nodes systems that require numerous sensor nodes, such as heavy haul and general freight railway applications.