期刊
SENSORS
卷 21, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/s21155009
关键词
rotating machine; bearing; digital twin; gaussian process regression; Laguerre filter; fuzzy logic; proportional integral observer; Lyapunov robust algorithm; adaptive technique; support vector machine; fault diagnosis
资金
- Korea Technology and Information Promotion Agency (TIPA) - Korea government (SMEs) [S3126818]
- Korea Technology & Information Promotion Agency for SMEs (TIPA) [S3126818] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The research aims to design an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings, using a combination of mathematical and data-driven techniques to improve reliability and accuracy. The proposed technique involves modeling and estimating signals, with the use of adaptive observer and support vector machine for fault diagnosis and crack size identification. Testing on the Case Western Reserve University Bearing Dataset showed high average detection accuracies for fault diagnosis and crack size identification.
In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.
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