Estimation of Rotational Speeds Based on Gearbox Vibrations via Artificial Neural Networks

This work investigates the estimation of rotational speeds based on neural networks and high sampled gearbox vibration data. A total of 18 different network architectures consisting of fully connected network layers, long short-term memory cells, and different activation functions are compared. In addition, the impact of the sampling rate of the vibration data, the signal length, and the input data type is investigated. Vibration data in the time domain, frequency domain, and a combination of both are examined as input data. Finally, the number of neurons is varied to obtain the best possible rotational speed estimation. In order to generate an appropriate dataset for the training of the neural networks, an experimental setup is presented, which is used to record the vibration data of the gearboxes. It becomes apparent that especially vibration data in the frequency domain are suitable for the estimation of the rotational speed. Furthermore, it could be shown that the low sampling rates result in an inaccurate speed estimation. Likewise, signal lengths that are too short result in inaccurate estimates. Overall, the speed can be estimated with an accuracy of 7.88 rpm at an average speed of the test dataset of 202 rpm.