Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios.

Monitoring rotating machines is essential for evaluating operations, preventing failures, and improving efficiency. Traditional rotational measurement methods, like magnetic induction or optical sensors, typically require dedicated hardware for each rotating part, making them costly and complex to implement, especially in industrial scenarios. The challenge intensifies when multiple rotations must be measured simultaneously. This study introduces a novel method using a single event-based vision sensor, or dynamic vision sensor (DVS), which offers asynchronous pixel activation, high temporal resolution, and a wide dynamic range. Unlike traditional frame-based sensors, which require high frame rates and generate large data volumes, the DVS analyzes event streams and processes signals to identify crossing edges and time intervals. This enables simultaneous measurement of multiple rotating parts. The method was tested on a fan propeller, a CNC lathe, and drone propellers, demonstrating a relative mean absolute error of around 0.5%. This method allows safe distance measurements without altering the original production line due to its non-contact nature.