Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

Piltan, Farzin; Kim, Jong‐Myon

doi:10.3390/app11104602

Cited by 43 publications

(30 citation statements)

References 40 publications

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“…There are also some studies about bearing fault diagnosis with different methods. Piltan [39] proposed an algorithm of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification. Although this method achieves a good result for fault state classification with a more than 98.7% accuracy rate, this paper uses a machine learning method combined with the intelligent digital twin model, and expert knowledge is needed to build the model.…”

Section: Comparison With Other Research and Efficiency Evaluation Of ...mentioning

confidence: 99%

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

Liu

Sun

et al. 2022

Energies

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The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

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Section: Comparison With Other Research and Efficiency Evaluation Of ...mentioning

confidence: 99%

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

Liu

Sun

et al. 2022

Energies

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“…Rojek et al [32] presented the results of research on the development of digital twins of technical objects. Piltan et al [33] used intelligent DT combined with machine learning for bearing-anomaly detection and crack-size identification. Resman et al [34] proposed a five-step approach to planning data-driven digital twins of manufacturing systems and their processes.…”

Section: Digital-twin-assisted Transfer Learningmentioning

confidence: 99%

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

et al. 2021

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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, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields.

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“…The electric torque is transferred from the shaft to the control system using a dynamometer. This electric motor is provided with 4-different speeds to rotate the roller bearings 6205-2RS JEM SKF including 1797-rotation per minute (RPM), 1772-RPM, 1750-RPM, and 1730-RPM [27,28]. To collect the data, the vibration sensor is suggested.…”

Section: Datasetmentioning

confidence: 99%

“…The data were recorded at a 48 kHz sampling rate under four different motor loads from 0 to 3 hp. The basic information about the CWRUBD is listed in Table 1 [27,28]. Furthermore, Table 2 shows the CWRUBD signal condition test information.…”

Section: Datasetmentioning

confidence: 99%

Crack Size Identification for Bearings Using an Adaptive Digital Twin

Piltan

Kim

2021

Sensors

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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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Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

Cited by 43 publications

References 40 publications

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

Crack Size Identification for Bearings Using an Adaptive Digital Twin

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