Abstract
The performance of bearing fault detection systems based on machine learning techniques largely depends on the selected features. Hence, selection of an ideal number of dominant features from a comprehensive list of features is needed to decrease the number of computations involved in fault detection. In this paper, we attempted statistical time-domain features, namely, Hjorth parameters (activity, mobility, and complexity) and normal negative log likelihood for Gaussian mixture model (GMM) for the first time in addition to 26 other established statistical features for identification of bearing fault type and severity. Two datasets are derived from a publicly available database of Case Western Reserve University to identify the capability of features in fault identification under various fault sizes and motor loads. Features have been investigated using a two-step approach—filter-based ranking with 3 metrics followed by feature subset selection with 11 search techniques. The results indicate that the set of features root mean square, geometric mean, zero crossing rate, Hjorth parameter—mobility, and normal negative log likelihood for GMM outperforms other features. We also compared the diagnostic performance of normal negative log likelihood for GMM with the established feature normal negative log likelihood for single Gaussian. The selected set of statistical features is validated using ensemble rule-based classifiers and showed an average accuracy of 96.75% with proposed statistical features subset and 99.63% with all 30 features. F-measure and G-mean scores are also calculated to investigate their performance on datasets with class imbalance. The diagnostic effectiveness of the features was further validated on a bearing dataset obtained from an operating thermal power plant. The results obtained show that our newly proposed feature subset plays a major role in achieving good classification results and has a future potential of being used in a high-dimensional dataset with multidomain features.
Highlights
Vibration-based diagnosis and prognosis of bearing faults has been an active area of research for many years [1,2,3]
When T30 was replaced by T30∗ for this dataset, we did not see any improvements in the accuracy and F-measure values. is might be due to the mostly single Gaussian nature of the newly added normal vibration signals taken from the power plant
Research in bearing fault diagnosis and severity estimation has majorly focussed on improving the performance of classification algorithms
Summary
Vibration-based diagnosis and prognosis of bearing faults has been an active area of research for many years [1,2,3]. Ese parameters are extracted from either raw or preprocessed vibration signals. A wide set of dimensionless statistical parameters such as crest factor, shape factor, impulse factor, clearance factor, skewness factor, and kurtosis factor has been used to enhance the performance of fault diagnosis systems [9,10,11,12]. For the latter, empirical mode decomposition (EMD) has been popularly used as a preprocessing technique [13].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
