Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network

Zhuo, Pengcheng; Zhu, Ying; Wu, Wenxuan; Shu, Junqing; Xia, Tangbin

doi:10.1007/s12204-018-2028-4

Cited by 12 publications

(3 citation statements)

References 17 publications

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“…There has been several booming results in the eld of advanced deep learning and multitask learning for predicting diabetes. In the recent years, machine learning traditional models are very much popular to solve several problems like classifying images [1], processing text [2], diagnosing fault in real world dataset [3] and in applications including healthcare [4][5]. It is widespread to use both traditional and advanced ML algorithms to address the progress of disease and for disease prediction [6-8].…”

Section: Related Workmentioning

confidence: 99%

An automatic detection and classification of diabetes mellitus using CNN

Aswiga

Karpagam

et al. 2023

Soft Comput

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Machine learning algorithms have recently attracted more attention in the medical eld and have been successfully used in a variety of medical applications. Machine learning is used to assist medical practitioners to solve complicated problems. Thus to function autonomously without seeking any assistance or help from a person, a well-known branch of machine learning called deep learning completes several related tasks parallely. It is successfully used in a variety of applications, including disease prediction and disease progression. Since tasks are assumed to be related to one another, existing learning methods adapts the Convolution neural network (CNN) to classify the diabetes mellitus disease effectively. To detect the abnormality, the proposed work uses a deep learning network that includes a novel CNN model that splits the data into separate training and testing sets before doing classi cation. Using CNN, the proposed work achieves better accuracy and outperforms well to the best of our knowledge.

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Section: Related Workmentioning

confidence: 99%

An automatic detection and classification of diabetes mellitus using CNN

Aswiga

Karpagam

et al. 2023

Soft Comput

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“…In the resent past, machine learning models are very popular to solve various problems like image classification [11], text processing [12], real-time fault diagnosis [13] and healthcare [14,15]. It is very common to use ML algorithms to address disease prediction [16,17] [18].…”

Section: Related Workmentioning

confidence: 99%

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

et al. 2020

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Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep features.

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“…Lv et al [31,32] studied the special role of delay in nonlinear systems. Zhuo et al [33,34] studied the application of memory and feedback models in fault diagnosis. In summary, adding historical information to the system's feedback process in a certain form will greatly improve the output signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

Tang

Shi

2020

Shock and Vibration

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To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

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Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network

Cited by 12 publications

References 17 publications

An automatic detection and classification of diabetes mellitus using CNN

An automatic detection and classification of diabetes mellitus using CNN

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

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