Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Saufi, Syahril Ramadhan; Ahmad, Zair Asrar; Leong, M. Salman; Lim, Meng Hee

doi:10.1109/tii.2020.2967822

Cited by 188 publications

(67 citation statements)

References 44 publications

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“…Abid et al [12] constructed SAE to diagnose bearing faults using the extracted multidomain features of vibration signals. Saufi et al [13] designed SAE for fault recognition of gearbox using vibration signals of multi-sensors. Pan et al [14] modified the standard CNN to identify faults of motor bearing based on noisy vibration data Jia et al [15] presented new CNN for fault detection of planetary gearbox through analysis of transverse vibration and torsional vibration.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Shao

Xia

Han

et al. 2021

IEEE Trans. Ind. Inf.

334

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The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

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Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Shao

Xia

Han

et al. 2021

IEEE Trans. Ind. Inf.

334

View full text Add to dashboard Cite

show abstract

“…From these reviews, it is perceived that the application of deep learning techniques to wind turbine fault detection and diagnosis will represent a strong research and application trend for the operation of wind turbines; see, for example, refs. [27][28][29][30][31][32] published in the last two years.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

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“…In [19], an unsupervised multi-view sparse filtering method was proposed to filter the interfering electrical signals while improving the accuracy of fault diagnosis. In view of the limited data of gearbox faults and the lack of an accurate model of the actual gearbox, in [20,21], an automatic encoder model is used to process the limited sample data to achieve accurate diagnosis of gearbox faults. In [22], healthy data is used to supplement the consistency of the ordered frequency spectrum, and then the probabilistic model is used for automatic fault detection and calculation of diagnostic indicators for positioning, which reduces the requirements for fault data.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions

Wang

Sun

Liu

2021

J. Electr. Eng. Technol.

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The gearbox is an important part of the wind turbine, and it is also a part prone to failure. Most of the fault diagnosis methods for gearboxes lack the ability to adapt to changing operating conditions. Once the operating conditions change, it is necessary to relearn the object characteristics, otherwise it is prone to misjudgment. Therefore, this paper proposes a method based on a two-class non-negative matrix factorization (NMF) network to realize gearbox fault diagnosis under variable operating conditions without additional data. The method is divided into two parts: model training and fault diagnosis. The former takes the basic operating conditions as the benchmark. It extracts the local static characteristics of the fault samples, and trains them into classifier models with different diagnostic functions to construct a network. The latter uses the static distance obtained from the data input network of the new operating condition as an important indicator for detecting the occurrence of a fault and identifying the type of the fault. The experimental results based on the QPZZ-II rotating machinery vibration test stand show that compared with other methods, the algorithm proposed in this paper has a good application effect on the diagnosis under variable operating conditions.

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Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Cited by 188 publications

References 44 publications

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Deep Learning Method for Fault Detection of Wind Turbine Converter

Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions

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