A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data

Hou, Wenbo; Zhang, Chunlin; Jiang, Yunqian; Cai, Keshen; Wang, Yanfeng; Li, Ni

doi:10.1016/j.measurement.2023.112879

Cited by 28 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In real industrial scenarios, the training and test datasets are not independent and identically distributed, which often results in deep learning models losing diagnostic performance [42][43][44][45]. To address the aforementioned limitations, transfer diagnosis has gained prominence [46][47][48][49][50]. This section uses the proposed method in conjunction with a transfer learning module and focuses on optimizing the feature extractor to improve the overall performance of transfer learning-based diagnosis.…”

Section: Case Two: Application In Transfer Diagnosismentioning

confidence: 99%

Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

Chang,

Yang,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder (MODAE) is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

show abstract

Section: Case Two: Application In Transfer Diagnosismentioning

confidence: 99%

Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

Chang,

Yang,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Rolling bearings are an important part of rotating machinery and equipment, and they are easily damaged under long time operation and bad working conditions. Sudden failure will affect the normal operation of the equipment, resulting in economic losses and even casualties [ 1 ]. Therefore, it is of great importance to monitor and diagnose the operating condition of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum

Zhai,

Wang,

et al. 2024

Sensors

View full text Add to dashboard Cite

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

show abstract

“…The above research reduces the domain discrepancy between the source domain and target domain through parameter transfer, domain adaptation, or CORAL loss and adversarial mechanism simulation, aiming to maintain higher robustness in more complex environments and improve the accuracy of fault diagnosis after transfer [18][19][20]. However, the actual operation of large-scale mechanical equipment does not provide access to a large amount of fault data, making transfer learning fault diagnosis a significant challenge that requires a method to obtain more fault data [21,22].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

Liu,

Qi,

Gao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

With the advancement of complex system diagnosis, prediction, and health management technologies, digital twin technology has become a prominent research area in the fields of intelligent manufacturing and system operation and maintenance. However, due to the high complexity of practical systems, the difficulty of data acquisition, and the low accuracy of modeling techniques, current digital twin modeling suffers from low accuracy, and the generalization ability of models is poor when applied in model transfer. To address this issue, a novel fault diagnosis method is proposed, which integrates a digital twin model based on transfer learning. The framework introduces an innovative approach to construct multiple digital twin models using both mechanistic and data-driven models. The mechanism twin constructs a universal simulation model based on physical equipment and updates it with system response measurement data. The data twin consists of a high-dimensional fully connected GAN (HDFC-GAN) twin for extracting deep features from data and an LSTM twin for extracting time series features. Subsequently, transfer learning is introduced to achieve deep fusion in the multiple digital twins system. The mechanism twin is used to obtain source domain samples to construct a diagnostic network, and the data twin is used to extract target domain features to correct the diagnostic network, thereby improving the accuracy and reliability of fault diagnosis. Finally, the proposed framework is applied to the fault diagnosis of triplex pump equipment. The accuracy of diagnosis continuously improves as the system is updated and ultimately reaches 89.28%, demonstrating the effectiveness of the algorithm and providing a novel solution for the generalization limitations of current digital twin models.

show abstract

A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data

Cited by 28 publications

References 31 publications

Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

Contact Info

Product

Resources

About