Fault diagnosis in rotating machines based on transfer learning: Literature review

Misbah, Iqbal; LEE, C.K.M.; KEUNG, K.L.

doi:10.1016/j.knosys.2023.111158

Cited by 14 publications

(7 citation statements)

References 186 publications

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“…In terms of technical tools, transfer learning can be categorized into instance-based transfer learning, feature-based transfer learning, association-based transfer learning and parameter-based transfer learning [22]. Instance-based transfer learning improves the effectiveness and robustness of transfer learning by adjusting the weights of the parts of the source domain that are more similar to the target domain.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Parameter-based transfer learning reduces the differences between domains with the idea of using a large amount of source-domain data to train the model under the transfer learning strategy of shallow parameter freezing and deep parameter learning, and then use a small amount of target-domain data to determine the depth parameters of the model so that the parameters of the deep network layer are more in line with the classification features of the target-domain data. In terms of technical tools, transfer learning can be categorized into instance-based transfer learning, feature-based transfer learning, association-based transfer learning and parameter-based transfer learning [22]. Instance-based transfer learning improves the effectiveness and robustness of transfer learning by adjusting the weights of the parts of the source domain that are more similar to the target domain.…”

Section: Transfer Learningmentioning

confidence: 99%

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Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Li,

Liu,

Yan

et al. 2024

Processes

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Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition.

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Section: Transfer Learningmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Li,

Liu,

Yan

et al. 2024

Processes

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“…In the field of machine learning, TL can be divided into sample-based TL, featurebased TL and model-based TL [41]. Sample-based TL helps target domain learning by adjusting the use of source domain samples in the target domain, but it needs to ensure that there is a certain similarity between the source domain and the target domain; otherwise, the effect is limited.…”

Section: Pre-trained and Fine-tuned Modelmentioning

confidence: 99%

Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method

Niu,

Pan,

Qin

et al. 2024

Applied Sciences

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In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the k value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD.

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“…Transfer learning (TL) technology has been introduced to facilitate fault diagnosis and expand the application scope of contemporary intelligent fault diagnosis methods based on deep learning technology [16,17]. Transfer learning models enable the transmission of knowledge acquired from one domain to another, such as domain adversarial neural networks (DANN) [18], domain separation networks [19], maximum mean discrepancy (MMD) [20], and D-coral [21], thereby facilitating classification, detection, and other tasks in the target domain.…”

Section: Introductionmentioning

confidence: 99%

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

Yu,

Liu,

Wang

et al. 2024

Meas. Sci. Technol.

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High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model's capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

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Fault diagnosis in rotating machines based on transfer learning: Literature review

Cited by 14 publications

References 186 publications

Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network

Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

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