Effective fault detection technology is of great significance to the safety and economy of nuclear power plants (NPPs). To accurately identify early faults in NPPs, this study proposes a novel fault detection method b...
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Effective fault detection technology is of great significance to the safety and economy of nuclear power plants (NPPs). To accurately identify early faults in NPPs, this study proposes a novel fault detection method based on sparse denoising autoencoder (SDAE) and kernel principal component analysis (KPCA). First, the operating data of NPPs is collected by numerous sensors, and the operating parameters are grouped according to physical properties. Then, the corresponding fault detection model is established according to each parameter group, and each detection model consists of the SDAE and KPCA. The case study evaluated four accident scenarios (LOCA, SLBIC, FHAIC, FHAIAB) across two development degrees (0-1 % and 0-0.1 %). The proposed method achieved fault detection rates of 99.07 %, 95.20 %, 99.73 %, and 99.60 % for the 0-1 % degree with zero false alarms. Even for the subtler 0-0.1 % degree, it maintained a 94.84 % average detection rate and no false alarms. Compared to traditional methods, its average fault detection rate was higher than that of PCA and KPCA by 62.9 % and 32.4 % (0-1 % degree), and by 89.5 % and 88 % (0-0.1% degree), demonstrating its potential application value in NPPs.
As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnos...
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ISBN:
(纸本)9798350366907;9789887581581
As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnostic effect of using input signals in complex environments is not satisfactory. Therefore, this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder, which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features, reducing the interference caused by noise. Experiments have shown that this method can effectively identify the fault status of aircraft engines, and compared to existing methods, this method can provide more accurate diagnostic results.
As the heart of an aircraft,it is crucial to accurately grasp the operational status of ***,it is difficult to fully reflect the accurate fault status through a single sensor signal,and the diagnostic effect of using ...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
As the heart of an aircraft,it is crucial to accurately grasp the operational status of ***,it is difficult to fully reflect the accurate fault status through a single sensor signal,and the diagnostic effect of using input signals in complex environments is not ***,this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder,which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features,reducing the interference caused by *** have shown that this method can effectively identify the fault status of aircraft engines,and compared to existing methods,this method can provide more accurate diagnostic results.
Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. Howe...
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Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on sparse denoising autoencoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.
Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparsedenoising autoencod...
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Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of -10 dB and label mismatch error of 50%.
The swift development of the Internet of Things (IoT) devices has created a pressing need for effective cybersecurity measures. They are vulnerable to different cyber threats that can compromise the functionality and ...
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The swift development of the Internet of Things (IoT) devices has created a pressing need for effective cybersecurity measures. They are vulnerable to different cyber threats that can compromise the functionality and security of urban systems. Distributed Denial of Service (DDoS) attacks are among IoT networks' most challenging and destructive cyber threats. With the rapid growth in IoT devices and users, the vulnerability of IoT devices to such attacks has enhanced significantly, making DDoS attacks a predominant threat. This work introduces several approaches for effectively detecting IoT-based DDoS threats. Classical machine learning (ML) techniques mostly face difficulty in managing real-world traffic characteristics effectually, making them less appropriate for detecting DDoS attacks. In contrast, Artificial Intelligence (AI)-based methods have proven more effective in detecting cyber-attacks than conventional approaches. This manuscript proposes an effective Feature Pruning with Optimal Deep Learning-based DDoS Attack Detection (FPODL-DDoSAD) technique in the IoT framework. The FPODL-DDoSAD technique initially uses a min-max scalar for the data scaling into the standard layout. Besides, the feature pruning process is performed using an improved pelican optimization algorithm (IPOA), which enables the choice of an optimal subset of features. Meanwhile, DDoS attacks are recognized using a sparse denoising autoencoder (SDAE) model. Furthermore, the parameter tuning of the SDAE classifier is accomplished by utilizing the Fish Migration Optimizer (FMO) technique. The experimental values of the FPODL-DDoSAD approach are assessed on the benchmark BoT-IoT dataset. The comparison study of the FPODL-DDoSAD method demonstrates a superior accuracy value of 99.80% over existing techniques.
The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault dia...
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The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.
Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution's negative impact on the environment and people's health. The data-driven forecasting model can help a...
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Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution's negative impact on the environment and people's health. The data-driven forecasting model can help a better understanding of environmental air quality. The existing data-driven forecasting models usually ignore missing values, the correlations between the pollutant and meteorological factors and fail to perform temporal modeling effectively, affecting prediction accuracy. In response to these issues, we present a deep learning based Convolutional LSTM-SDAE (CLS) model to forecast the particulate matter level, revealing the correlation between particulate matter and meteorological factors. In the proposed architecture, the k nearest neighbor (KNN) imputation technique is employed to recover the air quality dataset's missing values. The Convolutional Long Short Term Memory (CNN-LSTM) unit identifies the vast dataset's hidden features and performs pollutants' temporal modeling. In addition, Bidirectional Gatted Recurrent Unit (BIGRU) is implemented as both encoder and decoder in sparse denoising autoencoder, which reconstructs the CNN-LSTM model's output in the dynamic fine-tuning layer to get robust prediction results. The experimental results in Talcher, India, and Beijing, China indicate that the model can improve forecasting accuracy and outperforms the other state of art and baseline models.
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