Crack Size Identification for Bearings Using an Adaptive Digital Twin

Abstract

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.

Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

“…the combination of ALSGL-SB and SVM and by 5.5% compared with the combination of ALSGL-SBI and SVM. To test the stability and robustness of the fault type diagnosis, the experiment was repeated 20 times with a random selection of samples to form the train and test sets each time [43,44]. Figure 16 shows the robustness of the combined ALSGL-SB and SVM, ALSGL-SBI and SVM, and PDT and SVM approaches.…”

“…Thus, the combination of PDT and SVM improved the average accuracy for crack size diagnosis by 13.8% compared with the combined ALSGL-SB and SVM method and by 7.2% compared with the combined ALSGL-SBI and SVM method. To test the stability and robustness of crack size identification, the experiment was repeated 20 times with a random selection of samples to form the train and test sets each time [43,44]. Figure 27 shows the robustness of the combined ALSGL-SB and SVM, ALSGL-SBI and SVM, and PDT and SVM methods.…”

Application of Life Cycle of Aeroengine Mainshaft Bearing Based on Digital Twin

“…Then, an acoustic emission signal estimation method based on a strict feedback backstep observer, integral term, support vector regression and the fuzzy logic algorithm was proposed [62]. The effectiveness of the algorithm was verified with a bearing dataset containing normal states and seven fault levels [63]. The self-adaptive technology method in this paper used the support vector machine to classify the faults of the eight states of the bearing, and then used the support vector machine to classify the bearing fault signals of the 3 mm and 6 mm crack sizes in the seven fault levels of the bearing fault signals to carry out fault classification.…”

Personalized fault diagnosis of rolling bearings in trains based on digital twin

“…Luo et al [40] used a particle filter algorithm to mix simulation and measurement data to form a DT dataset and used it to predict the tool remaining useful life. Piltan and Kim [41] applied support vector machines to classify the residual features after calculating the residuals of the measured and simulated signals. Xia et al [42] used DT generated data as training data for deep learning.…”