A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models

Bhavsar, Keval; Vakharia, Vinay; Chaudhari, Rakesh; Vora, Jay; Pimenov, Danil Yurievich; Giasin, Khaled

doi:10.3390/machines10030176

Cited by 33 publications

(9 citation statements)

References 35 publications

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“…The delta, theta, alpha, beta, and gamma bands were segmented using wavelet decomposition with Daubechies-2 (DB2) as the basis or mother wavelet. The Daubechies family was chosen for its good performance, as reported in [ 24 , 25 ]. In more detail, DB2 in EEG cases has been commonly used and shows good performance, as reported in [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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Section: Methodsmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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“…When constructing a prediction model for yarn quality fluctuation, the first work is to analyze the influence of multi-related parameters on yarn quality fluctuation. 33 Since there are many parameters affecting yarn quality and they are related to each other, 34 the dynamic fluctuation of yarn quality with the change of the parameters is caused by the dynamic fluctuation of yarn quality. A typical situation is that even if each quality parameter fluctuates within the normal range, the actual yarn quality may also fluctuate abnormally.…”

Section: Multi-relation Parameters Feature Subspace Modeling In Spinn...mentioning

confidence: 99%

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Hu,

Zhang,

Zhao

et al. 2023

Journal of Engineered Fibers and Fabrics

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Yarn quality is a key indicator in spinning production process, which directly determines the processing quality of the finished fabric. Considering the problems that the yarn quality is difficult to predict due to many influencing factors and widely monitoring dimensions existed. This paper proposes a multi-correlation parameters-based feature subspace model for yarn quality fluctuation prediction method. Firstly, Kernel Principal Component Analysis (KPCA) algorithm is used to analyze the relationship between the relevant parameters affecting yarn quality, and the characteristic subspace of yarn quality fluctuation is reconstructed. Then, a feature subspace-oriented deep learning prediction structure based on Deep Belief Network (DBN) is constructed, and the intelligent prediction model of yarn quality is established by using Particle Swarm Optimization (PSO) algorithm to optimize the modeling parameters. Finally, a case for spinning spun yarn process data set is used to validate the effectiveness of the proposed method, result shows that the proposed method can effectively analyze the multi-correlation parameters fluctuation law, which shows good effect for predicting yarn quality accurately.

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“…Four mother wavelets are used for WPT; they are Daubechies, Symplet, Biorthogonal, and Coiflet [35,36]. Other types of mother wavelets are not compatible for WPT, where they can be used for continuous wavelet transform analysis only.…”

Section: Selection Of Mother Waveletmentioning

confidence: 99%

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Pang

Zhang

Wang

et al. 2022

IET Generation Trans & Dist

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Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time-frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves.

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A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models

Cited by 33 publications

References 35 publications

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

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