Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

Wang, Yuehai; Yan, Yongzheng; Wang, Qinyong

doi:10.1155/2016/5682847

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…The ratio of energy in A4 frequency band to that in the whole frequency band is less than 1% by energy proportion calculation. If deeper decomposition (such as 5‐level decomposition) is carried out, the A4 band with less energy and information will be further filtered, and the computational burden will also be increased 50 . Hence, the 4‐level decomposition is adequate.…”

Section: Methodsmentioning

confidence: 99%

“…If deeper decomposition (such as 5-level decomposition) is carried out, the A4 band with less energy and information will be further filtered, and the computational burden will also be increased. 50 Hence, the 4-level decomposition is adequate. The microwave signals are decomposed into one final approximation A4 and details D1 ∼ D4.…”

Section: Signal Analysis Using Discrete Wavelet Transformmentioning

confidence: 99%

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Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

Xiao

Liu

et al. 2021

Medical Physics

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Purpose: Ultra-Wide Band (UWB) microwave breast cancer detection is a promising new technology for routine physical examination and home monitoring. The existing microwave imaging algorithms for breast tumor detection are complex and the effect is still not ideal, due to the heterogeneity of breast tissue, skin reflection, and fibroglandular tissue reflection in backscatter signals. This study aims to develop a machine learning method to accurately locate breast tumor. Methods: A microwave-based breast tumor localization method is proposed by time-frequency feature extraction and neural network technology. First, the received microwave array signals are converted into representative and compact features by 4-level Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). Then, the Genetic Algorithm-Neural Network (GA-NN) is developed to tune hyper-parameters of the neural network adaptively. The neural network embedded in the GA-NN algorithm is a four-layer architecture and 10-fold cross-validation is performed. Through the trained neural network, the tumor localization performance is evaluated on four datasets that are created by FDTD simulation method from 2-D MRI-derived breast models with varying tissue density, shape, and size. Each dataset consists of 1000 backscatter signals with different tumor positions, in which the ratio of training set to test set is 9:1. In order to verify the generalizability and scalability of the proposed method, the tumor localization performance is also tested on a 3-D breast model. Results: For these 2-D breast models with unknown tumor locations, the evaluation results show that the proposed method has small location errors, which are 0.6076 mm, 3.0813 mm, 2.0798 mm, and 3.2988 mm, respectively, and high accuracy, which is 99%, 80%, 94%, and 85%, respectively. Furthermore, the location error and the prediction accuracy of the 3-D breast model are 3.3896 mm and 81%. Conclusions: These evaluation results demonstrate that the proposed machine learning method is effective and accurate for microwave breast tumor localization. The traditional microwave-based breast cancer detection method is to reconstruct the entire breast image to highlight the tumor. Compared with the traditional method, our proposed method can directly get the breast tumor location by applying neural network to the received microwave array signals, and circumvent any complicated image reconstruction processing.

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Section: Methodsmentioning

confidence: 99%

Section: Signal Analysis Using Discrete Wavelet Transformmentioning

confidence: 99%

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

Xiao

Liu

et al. 2021

Medical Physics

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“…DWT also reduces the noise from the input data along with decreasing the input data size [133]. DWT has also been widely used for the dimensionality reduction of time-series data [134], [135], [136].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

A Survey on Dimensionality Reduction Techniques for Time-Series Data

et al. 2023

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Data analysis in modern times involves working with large volumes of data, including timeseries data. This type of data is characterized by its high dimensionality, enormous volume, and the presence of both noise and redundant features. However, the "curse of dimensionality" often causes issues for learning approaches, which can fail to capture the temporal dependencies present in time-series data. To address this problem, it is essential to reduce dimensionality while preserving the intrinsic properties of temporal dependencies. This will help to avoid lower learning and predictive performances. This study presents twelve different dimensionality reduction algorithms that are specifically suited for working with timeseries data and fall into different categories, such as supervision, linearity, time and memory complexity, hyper-parameters, and drawbacks. INDEX TERMS Time-series data, Dimensionality reduction, High-dimensional dataThis article has been accepted for publication in IEEE Access.

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“…Li et al [10] applied a method based on the LMD and the SVM algorithm. Wang et al [11] performed a wavelet analysis-based method. Zhang et al [12] introduced a fault diagnosis approach using generalised multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimisation (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

An incipient fault diagnosis method based on Att-GCN for analogue circuits

Yang¹,

Li²,

Gao³

2023

Meas. Sci. Technol.

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Incipient faults for analogue circuits in modern electronic systems are difficult to diagnose due to poor fault features. To address this issue, a method based on the attention weighted graph convolution network (Att-GCN) is proposed in this paper. The structural and data features of samples are jointly extracted to mine the effective characteristics from incipient faults. First, a wavelet packet energy transform and a probabilistic principal component analysis (ProbPCA) are employed to enhance the sample fault information. Then, the distance clustering method is deployed to construct the sample set into a non-European structure sample graph, where the structural features of fault samples are preserved. Second, an Att GCN, which combines the spatial-domain graph convolution network and improved self-attention mechanism, is constructed to extract the structural features and data features to obtain more effective fault information. Additionally, the multisample dropout method is introduced to reduce network overfitting in the training process. To assess the method's actual performance for fault diagnosis, experiments are carried out in the Sallen-Key bandpass filter circuit, the four-op-amp biquadratic filter circuit and the amplifier board circuit. The outcomes indicate that this method improves the incipient fault diagnosis accuracy for analogue circuits.

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Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

Cited by 7 publications

References 30 publications

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

A Survey on Dimensionality Reduction Techniques for Time-Series Data

An incipient fault diagnosis method based on Att-GCN for analogue circuits

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