Informatics and Engineering Systems Faculty Publications and Presentations
Document Type
Article
Publication Date
1-27-2025
Abstract
Many research studies are performed in extracting non-stationary signal properties such as instantaneous frequency (IF) and envelope to diagnose machinery faults under time-varying speed conditions. But accurate IF extraction is difficult from such complex fluctuating signals to identify the faults efficiently. Therefore, this paper proposes the Statistical Complexity Measure (SCM) as a feature that evaluates the randomness and complexity without any additional requirement, providing an outstanding result in multi-component fault detection under speed fluctuations. Primarily, the influence of SCM is proved by ranking the features with the help of the Random Forest (RF) algorithm. The fault classification of individual signal characteristics confirms its ranking order as well. Then, it is targeted towards the efficacy of the signal fusion approach in multicomponent fault diagnosis. Focusing on the essence of SCM, it is merged with the other three signal properties based on the ranking technique to investigate which one of the feature compositions is more advantageous. The machine learning classifier, SVM-RBF, achieves 94.7%, which proves the potential effects of SCM features in the industrial rotational machinery component under variable speed operating conditions.
Recommended Citation
Sowmya, S., M. Saimurugan, Kannan N, and Immanuel Edinbarough. "Potential Effects of Statistical complexity measure in improving the fault diagnosis performance under speed fluctuations." Measurement Science and Technology (2025). http://doi.org/10.1088/1361-6501/adaeda
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Publication Title
Measurement Science and Technology
DOI
10.1088/1361-6501/adaeda
Comments
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