An intelligent diagnosis method using fault feature regions for untrained compound faults of rolling bearings

Tang, Jiahui; Wu, Jimei; Hu, Bingbing; Liu, Jie

doi:10.1016/j.measurement.2022.112100

Cited by 22 publications

(6 citation statements)

References 37 publications

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“…performance of mechanical equipment and can pose a serious threat to the safety of personnel. Therefore, the health condition monitoring and fault diagnosis of bearings are significant for the safe and stable operation of industrial equipment [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Dong,

Jiang,

et al. 2024

Meas. Sci. Technol.

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Research on transfer learning in rolling bearing fault diagnosis can help overcome challenges such as different data distributions and limited fault samples. However, most existing methods still struggle to address the zero-shot cross-domain problem within the same equipment and the few-shot cross-machine problem. In response to these challenges, this paper introduces a transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment (FDCATL). The model consists of two stages. In the first stage, the original vibration signals undergo continuous wavelet transform to obtain time-frequency diagram. Subsequently, a convolutional neural network extracts features from the diagram. The obtained deep features are decomposed into four types: uncertain features, domain-shared features, domain-specific features and category features. Multiple loss functions are then employed to remove extraneous features beyond the category features. In the second stage, category features are further extracted, and Convolutional Block Attention Module (CBAM) is introduced to further reduce the potential interference of unexcluded irrelevant information within the category features with classification results. Simultaneously applying a class-level alignment strategy effectively alleviates inter-domain class distribution discrepancies. Experimental validation was conducted on three distinct datasets, revealing a significant improvement in the classification performance of the proposed method over alternative methods. Furthermore, the model demonstrated robustness and noise resistance.

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Section: Introductionmentioning

confidence: 99%

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Dong,

Jiang,

et al. 2024

Meas. Sci. Technol.

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“…These methods require manual feature extraction and have certain limitations. Deep learning was first proposed by Hinton et al [15], and it has excellent feature extraction capabilities and has been widely used in the field of fault diagnosis [16][17][18]. Convolutional neural networks (CNN) are one of them and have been extensively employed in the field of bearing fault diagnosis [19].…”

Section: Introductionmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

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Bearing fault diagnosis is of great significance to the normal operation of machinery, and its performance and life span directly affect the operational efficiency and safety of the whole equipment. For existing image coding methods which detecting the bearing fault with a large number of training samples and complex neural networks to achieve the desired detection performance, the Multiscale Permutation Entropy Gray Image Coding (MPEGIC) method is proposed. In order to fully extract the feature information of the time series signal, this paper uses the Multiscale Permutation Entropy (MPE) method to construct a new image coding method by calculating the alignment information of the time series to reflect the complexity and randomness of the time series at different scales, and dividing the obtained feature matrix and mapping it to the gray-scale image domain. And it is experimentally verified by Case Western Reserve University (CWRU) bearing dataset and self-made rotor experimental platform bearing dataset. The results show that the method in this paper effectively reduces the number of training samples and the number of model parameters, and maintains a better detection performance even in a strong noise background.

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“…These methods utilize various intelligent model algorithms to autonomously learn fault features from operation and maintenance data, and make classification decisions on the presence and type of faults, significantly enhancing the automation and intelligence of diagnosis [3]. Rolling bearings, as crucial elements in rotating machinery for transmitting torque and force [4], find extensive applications in various industries such as aerospace, machine tools, and metallurgy. However, prolonged operation in a complex and unpredictable external environment can lead to various failures, including pitting, cracking, and fatigue spalling.…”

Section: Introductionmentioning

confidence: 99%

“…(3) Under the conditions of multiple loads and noise interference, there are distribution differences in vibration signals, and the feature extraction ability of the hybrid fault diagnosis model decreases, leading to a decrease in diagnostic accuracy. (4) After training the network model on one dataset and transferring it to datasets under other working conditions, the diagnostic accuracy of the model also decreases, making it difficult to apply in practical engineering environments.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

Sun,

Zhu,

Xiao

et al. 2024

Meas. Sci. Technol.

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The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network(GGRU), one-dimensional convolutional neural network(1DCNN), and adaptive batch normalization(AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer (1D-GAP) to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network's feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model's domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University (CWRU), aero-engine bearings, Xi'an Jiaotong University, and the Changxing Sumyoung Technology (XJTU-SY). The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

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An intelligent diagnosis method using fault feature regions for untrained compound faults of rolling bearings

Cited by 22 publications

References 37 publications

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

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