  Vibration-based condition monitoring systems are receiving increasing
attention due to their ability to accurately identify different conditions by
capturing dynamic features over a broad frequency range. However, there is
little research on clustering approaches in vibration data and the resulting
solutions are often optimized for a single data set. In this work, we present
an extensive comparison of the clustering algorithms K-means clustering,
OPTICS, and Gaussian mixture model clustering (GMM) applied to statistical
features extracted from the time and frequency domains of vibration data sets.
Furthermore, we investigate the influence of feature combinations, feature
selection using principal component analysis (PCA), and the specified number of
clusters on the performance of the clustering algorithms. We conducted this
comparison in terms of a grid search using three different benchmark data sets.
Our work showed that averaging (Mean, Median) and variance-based features
(Standard Deviation, Interquartile Range) performed significantly better than
shape-based features (Skewness, Kurtosis). In addition, K-means outperformed
GMM slightly for these data sets, whereas OPTICS performed significantly worse.
We were also able to show that feature combinations as well as PCA feature
selection did not result in any significant performance improvements. With an
increase in the specified number of clusters, clustering algorithms performed
better, although there were some specific algorithmic restrictions.
