Abstract:Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, a… Show more
“…The output of many such trees is analyzed, and a final prediction model is obtained by pooling the trees together. 38–40 Scheme S1† shows an illustration of the procedure followed by the sample code used in the model. When the number of features compared to the number of data points is relatively small, RF has prediction accuracy typically far superior to that of multiple linear regression modeling.…”
Machine learning is used across many disciplines to identify complex relations between outcomes and several potential predictors. In the case of air quality research in heavily populated urban centers, such...
“…The output of many such trees is analyzed, and a final prediction model is obtained by pooling the trees together. 38–40 Scheme S1† shows an illustration of the procedure followed by the sample code used in the model. When the number of features compared to the number of data points is relatively small, RF has prediction accuracy typically far superior to that of multiple linear regression modeling.…”
Machine learning is used across many disciplines to identify complex relations between outcomes and several potential predictors. In the case of air quality research in heavily populated urban centers, such...
“…With the wide application of six-axis robotic arms in production, it has become a common way for the rear axle assembly process to use the robotic arm to drive the tightening gun to output assembly torque [ 35 ]. However, due to the accumulation of errors in the attitude, output angle, and other factors of the tightening gun during the assembly process, the assembly torque cannot meet the quality requirements of the body, and the assembly efficiency is affected.…”
During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
“…Jain et al [28] presented a digital twin-based fault diagnosis method for distributed photovoltaic system, which demonstrated higher fault sensitivity. Guo et al [29] simulated a large amount of fault data of a production line through digital twin and trained a reliable fault diagnosis model based on an improved random forest. However, few works have been reported to apply digital twin to bearing fault diagnosis.…”
The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery’s performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis framework based on digital twin is proposed. In the case of fault data available, self-organizing maps with minimum quantization error and support vector machine are employed to analyze the data. Where fault data is unavailable, a bearing digital twin model is first constructed to simulate the data, and the convolutional neural network combined with transfer learning is utilized to diagnose the bearing faults. Then, the law of bearing performance degradation is investigated. The effectiveness of the proposed method is verified using bearing vibration data.
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