Intelligent diagnosis method of bearing fault based on ICEEMDAN and Ghost-IRCNN

Zou, Xueyan; He, Deqiang; Jin, Zhenzhen; Wei, Zexian; Miao, Jianyin

doi:10.1177/09544062221144390

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…Then, CNN model was used to learn deeper features from high-frequency components for fault classification and localization. Zou et al [43] proposed the intelligent bearing fault diagnosis method based on improved ICEEMDAN and inverted residual convolution neural network integrated with Ghost module (Ghost-IRCNN). Firstly, ICEEMDAN was used to decompose the signals, and the kurtosis-correlation coefficient-margin factor index was proposed to filter and reconstruct the important modal components.…”

Section: Modal Decomposition Algorithmmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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The health condition of rolling bearings has a direct impact on the safe operation of high-speed trains. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.

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Section: Modal Decomposition Algorithmmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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show abstract

“…The results show that compared with ICEEDAN and MED, the proposed hybrid scheme has better performance in weak fault feature extraction under strong noise background. Zou et al [32] based on improved adaptive noise fully integrated Empirical Mode decomposition (ICEEMDAN) and inverse residual convolutional neural networks integrated with Ghost modules. An intelligent bearing fault diagnosis method is proposed.…”

Section: Status Of Research On Fault Classification and Identificationmentioning

confidence: 99%

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Han

Liu

et al. 2023

Mathematics

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The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

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“…DL can improve the performance and accuracy of the model and save manpower and time costs, which is widely used in many practical applications. [16][17][18][19] For example, Zhao et al 20 fused the Markov transition field (MTF) into a convolutional neural network (CNN) and extracted the fault characteristics information from the MTFencoded image of the bearing vibration signal. Cai et al 21 mined the deep features of vibration signals by improving the CNN.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of train traction motor bearing based on improved deep residual network

Sun,

He,

Lao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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As one of the important components of train traction motor, the bearing plays a key role in ensuring the safety of train operation. At present, the existing fault diagnosis methods for train traction motor bearings have the disadvantages of poor feature extraction ability, slow diagnosis speed, and low diagnosis accuracy. To solve the above problems, an improved deep residual network (IDRN) is proposed in this paper. Firstly, a new residual convolution block (RCB) is proposed to improve the extraction ability of hidden features. Secondly, the squeeze and excitation block (SE) is embedded in the residual block to increase the sensitivity of the model to features. Lastly, Adan is selected as the optimizer to increase the convergence speed of IDRN. The train traction motor bearing bench built by our research team is selected for the experiment. The results show that the test accuracy of IDRN on the bearing dataset of train traction motor is 98.72%, which is at least 2.27% higher than other comparative models. The IDRN model converges with fewer iterations in the training process, shortening the entire experiment time. At the same time, the CWRU bearing dataset and the bearing dataset with natural defects are selected for comparative verification. The experimental results show that the IDRN model exhibits strong robustness and practicability on different datasets.

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Intelligent diagnosis method of bearing fault based on ICEEMDAN and Ghost-IRCNN

Cited by 6 publications

References 55 publications

A review on convolutional neural network in rolling bearing fault diagnosis

A review on convolutional neural network in rolling bearing fault diagnosis

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Fault diagnosis of train traction motor bearing based on improved deep residual network

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