A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Nguyen, Van-Cuong; Hoang, Duy-Tang; Tran, Xuan-Toa; Van, Mien; Kang, Hyejin

doi:10.3390/machines9120345

Cited by 26 publications

(15 citation statements)

References 36 publications

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“…Research on multi-domain images also exists. For example, DNN’s multi-branched structure was used to process multi-domain images for feature extraction for fault diagnosis (Van-Cuong Nguyen et al, 2021) [ 116 ].…”

Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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“…To further verify the effectiveness of the proposed method, the state-of-the-art improved CNN-based methods, multi-information flow CNN (MIF-CNN) and multibranch deep neural network (MB-DNN) presented in reference [35,36] are also compared. The parameters of MIF-CNN and MB-DNN can be found in the corresponding reference, and the comparison results listed in Table 7 are the average of ten repeated trials.…”

Section: Journal Bearing Pedestal and Oil Cup Eddy Current Displaceme...mentioning

confidence: 99%

“…Sang et al [35] presented an improved CNN model with a multi-information flow for person reidentification. Nguyen et al [36] constructed a multibranch structure deep neural network model to diagnose bearing faults using multiple-domain image representation data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

Luo

Lin

Peng³

et al. 2022

Machines

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More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, this paper further proposes a novel fault diagnosis method for a rotor–journal bearing system based on SGIF-CNN. Firstly, the time-frequency distributions of samples are obtained using the Adaptive Optimal-Kernel Time–Frequency Representation algorithm (AOK-TFR). Secondly, the time–frequency diagrams of the training samples are utilized to train the SGIF-CNN model using a shallow information fusion method, and the trained SGIF-CNN model can be tested using the time–frequency diagrams of the testing samples. Finally, the trained SGIF-CNN model is transplanted to the equipment’s online monitoring system to monitor the equipment’s operating conditions in real time. The proposed method is verified using the data from a rotor test rig and an ultra-scale air separator, and the analysis results show that the proposed SGIF-CNN improves the computing efficiency compared to the traditional CNN while ensuring the accuracy of the fault diagnosis.

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“…In view of this, fault diagnosis is a crucial element that can improve product efficacy and reduce the risk of accidental hazards in sophisticated mechanical systems [ 1 ]. The rolling element bearing is a vital component in rotating machinery fault diagnosis, where bearing failures contribute roughly 45–55% of the total mechanical equipment failures [ 2 , 3 , 4 , 5 ], while bearing faults account for 90% of small rotary machine failures [ 6 , 7 ]. The early detection of motor faults and correct diagnosis performance, particularly during complex and changeable load and working conditions, is therefore essential to avoid heavy financial loss and prevent catastrophic consequences [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain

Hakim

Omran

Inayat-Hussain

et al. 2022

Sensors

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The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to −10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.

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A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Cited by 26 publications

References 36 publications

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain

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