Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM
Nima, Rezazadeh and Mario, de Oliveira and Donato, Perfetto and Alessandro, De Luca and Francesco, Caputo (2023) Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM. Applied Sciences, 13 (12). p. 6861. ISSN 2076-3417
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Abstract
A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/app13126861 | ||||||
| Dates: |
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| Uncontrolled Keywords: | rotary machines, unbalancing, shaft bow, FE analysis, wavelet time scattering, long short-term memory, support vector machine | ||||||
| Subjects: | CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific) | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment > Dept. of Engineering | ||||||
| Depositing User: | Mario Deoliveira | ||||||
| Date Deposited: | 07 Jun 2023 11:41 | ||||||
| Last Modified: | 07 Jun 2023 11:41 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14435 |
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