Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning

Liu, Shucong; Wang, Hongjun; Tang, Jianjun; Zhang, Xiang

doi:10.1016/j.measurement.2022.111174

Cited by 40 publications

(4 citation statements)

References 45 publications

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“…Transfer learning, as a technique for utilizing diagnostic knowledge from known datasets to address less strongly related fault diagnosis tasks, is highly beneficial for most current domain adaptation methods [ 28 ]. For instance, Liu et al [ 29 ] proposed a transfer learning network based on confrontational discriminative domain adaptation to address the fault problem of gas turbines. The approach involves transferring the model trained in the source domain to target domain data, followed by adversarial training that adaptively optimizes model parameters using information from both domains.…”

Section: Methodsmentioning

confidence: 99%

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Yan,

Sun,

Zhang

et al. 2023

Sensors

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Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.

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Section: Methodsmentioning

confidence: 99%

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Yan,

Sun,

Zhang

et al. 2023

Sensors

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“…This method can not only separate the shock components in the early fault signal of the gas turbine rotor system, but also effectively suppress the impact of noise on the sparse decomposition process. Interference has a good noise reduction effect, and better preserves high-amplitude amplitude components [36,37].…”

Section: Recognition and Analysis Of Gas Turbine Rotor System Impact ...mentioning

confidence: 99%

Impact Load Sparse Recognition Method Based on Mc Penalty Function

et al. 2022

Self Cite

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The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor.

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“…The main techniques for domain adaptation include mean maximum discrepancy (transfer component adaptation [27] and joint distribution adaptation [] etc), sample], reconstruction (deep reconstruction-classification networks [29] etc), and adversarial learning (domain-adversarial neural networks [30], and adversarial discriminative domain adaptation [31], etc). Currently, numerous researchers have explored the applications of domain adaptation on the cross-domain fault recognition of rotating machinery including gearboxes [32], bearings [33], motors [34], and gas turbines [35], and some researchers in the field of axial piston pump fault recognition have also paid attention to adopting domain adaptation methods.…”

Section: Introductionmentioning

confidence: 99%

Fault severity recognition in axial piston pumps using attention-based adversarial discriminative domain adaptation neural network

Shao,

Chao,

Xia

et al. 2024

Phys. Scr.

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Axial piston pumps are the ‘hearts’ of hydraulic systems whose fault recognition is necessary for the safety and reliability of hydraulic equipment. These pumps operate under different operating conditions and the fault recognition model trained at one operating point cannot be applicable at another operating point due to the problem of domain shifts. This paper proposes a transfer learning method for the fault severity recognition of axial piston pumps based on adversarial discriminative domain adaptation fused with a convolutional channel attention module. First, a convolutional neural network is pre-trained with labeled vibration data from the source domain, and a convolutional channel attention module is added to assign weights to different convolution kernels. Second, the trained source model is transferred to the target domain, and its parameters are updated by an adversarial training process between the labeled source data and the unlabeled target data. Finally, vibration data are collected from an axial piston pump at different fault levels under various operating conditions to validate the proposed method. Experimental results indicate that the proposed method achieves an average recognition accuracy of 98.3% and outperforms some other transfer learning methods by a large margin.

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Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning

Cited by 40 publications

References 45 publications

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Impact Load Sparse Recognition Method Based on Mc Penalty Function

Fault severity recognition in axial piston pumps using attention-based adversarial discriminative domain adaptation neural network

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