Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data

TL;DR

This paper presents a vibration dataset for unbalance detection in rotating shafts and evaluates machine learning models, achieving 98.6% accuracy with a neural network using FFT features.

Contribution

It provides a publicly available dataset for unbalance detection and compares multiple machine learning methods for fault diagnosis.

Findings

Fully connected neural network achieved 98.6% accuracy.

FFT-transformed vibration data improved model performance.

Dataset enables development of early fault detection algorithms.

Abstract

Fault detection at rotating machinery with the help of vibration sensors offers the possibility to detect damage to machines at an early stage and to prevent production downtimes by taking appropriate measures. The analysis of the vibration data using methods of machine learning promises a significant reduction in the associated analysis effort and a further improvement in diagnostic accuracy. Here we publish a dataset which is used as a basis for the development and evaluation of algorithms for unbalance detection. For this purpose, unbalances of various sizes were attached to a rotating shaft using a 3D-printed holder. In a speed range from approx. 630 RPM to 2330 RPM, three sensors were used to record vibrations on the rotating shaft at a sampling rate of 4096 values per second. A development and an evaluation dataset are available for each unbalance strength. Using the dataset recorded in this way, fully connected and convolutional neural networks, Hidden Markov Models and Random Forest classifications on the basis of automatically extracted time series features were tested. With a prediction accuracy of 98.6 % on the evaluation dataset, the best result could be achieved with a fully-connected neural network that receives the scaled FFT-transformed vibration data as input.