Vibration-based rotation speed estimation for Industrial IoT

Abstract

The Industrial Internet of Things (IIoT) uses wireless vibration sensors to predict machine health. In many cases, communication bandwidth and sensor processing resources are limited, and machine kinematics is not available at the sensor level. Consequently, only basic condition indicators, such as overall signal energy or peak-to-peak values, are computed at the sensor and transmitted to the cloud for further analysis. The behavior of these indicators is strongly influenced by machine speed, which is not available in many cases. This paper presents a novel and straightforward method for estimating machine relative speed based on vibrations, enhancing the interpretation of changes in basic condition indicators. The method does not require any prior knowledge about the machine's kinematics or expected speed changes. It was evaluated on simulated signals and wind turbine vibrations, providing a general solution for estimating varying rotating speed when no supplementary information is available. The method estimates rotating speed up to a constant factor in a set of signals (or signal segments) by optimally utilizing peak frequency statistics in the spectrum. The method demonstrates high accuracy under different operating conditions and noise and holds the potential to be used in a variety of IIoT applications.