An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

Lei, Zihao; Wen, Guangrui; Dong, Shuzhi; Huang, Xin; Zhou, Haoxuan; Zhang, Zhifen; Chen, Xuefeng

doi:10.1109/tim.2020.3041105

Cited by 59 publications

(22 citation statements)

References 54 publications

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“…In recent years, most of researches focus on the fault of the rolling bearing outer race and inner race, and these methods are very effective, while the faults of the rolling elements are less studied [8][9][10][11][12][13]. Because the rolling elements are located inside the rolling bearings, the fault signal of the rolling elements is easily interfered by the external environment during the transmission [14][15][16][17][18]. Therefore, it is necessary to diagnose the faults of rolling elements of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

134

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In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

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Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

134

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“…In the last several years, intelligent fault diagnosis field has received many studies of traditional-machine-learning-(TML-) based framework, deep-learning (DL-) based framework, and transfer-learning-(TL-) based framework, which can achieve automatically fault recognition and classification by analysing massive signals collected from mechanical equipment [1][2][3][4]. TML-based framework is often constructed by using traditional machine learning algorithms that mainly include k-nearest neighbour (KNN) [5], support vector machine (SVM) [6], artificial neural network (ANN) [7], extreme learning machine (ELM) [8], decision tree (DT) [9], and some variations of them.…”

Section: Introductionmentioning

confidence: 99%

“…e above-mentioned time-frequency analysis method can effectively help to extract fault features, but it often leads to a high dimensional feature set which contains interference and redundancy features. us, feature reduction and selection are a crucial step before the fault patterns classification [3,6,10,18]. In [3], the extreme gradient promotion is used for the dimensional reduction and sensitive features selection, which applies the importance of features to refine a high quality feature subset.…”

Section: Introductionmentioning

confidence: 99%

“…us, feature reduction and selection are a crucial step before the fault patterns classification [3,6,10,18]. In [3], the extreme gradient promotion is used for the dimensional reduction and sensitive features selection, which applies the importance of features to refine a high quality feature subset. In [6], an ant colony algorithm is applied to select features, and the selected feature subset is combined with parameter optimized SVM for enhancing the generalization of the fault diagnosis model.…”

Section: Introductionmentioning

confidence: 99%

“…In references [13,19,39,41], convolutional neural network (CNN) [13], deep belief network (DBN) [19], deep neural networks (DNN) [39], and deep autoencoders (DAE) [41] are used for constructing fault diagnosis models, respectively. However, some main limitations exist in most DL-based frameworks [3,4,40,42,43]. (1) Most conventional DL-based frameworks have insufficient generalization ability towards the engineering practical scenes; the reason is that an assumption of having the same distribution between the training and testing sets was widely used.…”

Section: Introductionmentioning

confidence: 99%

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A New Transferable Fault Diagnosis Approach of Rotating Machinery Based on Deep Autoencoder and Dominant Features Selection under Different Operating Conditions

Dong

Shi

et al. 2021

Shock and Vibration

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In the actual industrial scenarios, most existing fault diagnosis approaches are faced with two challenges, insufficient labeled training data and distribution divergences between training and testing datasets. For the above issues, a new transferable fault diagnosis approach of rotating machinery based on deep autoencoder and dominant features selection is proposed in this article. First, maximal overlap discrete wavelet packet transform is applied for signals processing and mix-domains statistical feature extraction. Second, dominant features selection by importance score and differences between domains is proposed to select dominant features with high fault-discriminative ability and domain invariance. Then, selected dominant features are used for pretraining deep autoencoder (source model), which helps in enhancing the fault representative ability of deep features. The parameters of the source model are transferred to the target model, and normal state features from target domain are adopted for fine-tuning the target model. Finally, the target model is applied for fault patterns classification. Motor and bearing fault datasets are used for a series of experiments, and the results verify that the proposed methods have better cross-domain diagnosis performance than comparative models.

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Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few‐Shot Learning With Data Scaling

Lin,

Kang,

Yao

2024

Adaptive Control & Signal

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This article presents a novel approach for fault diagnosis in rotating machinery using the few‐shot learning‐based unknown recognition and classification (FSLB‐UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in‐house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB‐UR&C model with data scaling exhibits markedly improved performance.

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An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

Cited by 59 publications

References 54 publications

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

A New Transferable Fault Diagnosis Approach of Rotating Machinery Based on Deep Autoencoder and Dominant Features Selection under Different Operating Conditions

Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few‐Shot Learning With Data Scaling

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