Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

Wotzka, Daria; Sikorski, Wojciech; Szymczak, Cyprian

doi:10.3390/en15093167

Cited by 8 publications

(3 citation statements)

References 76 publications

(80 reference statements)

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“…A similar work was reported in [38]. Besides, other feature extraction methods based on time-frequency transformation can also be found in [39], [40], [41], [42], and [43], interested readers can refer to the original articles for more details.…”

Section: ) Transform Domain-based Featuresmentioning

confidence: 76%

“…In [55], a sequential feature selection algorithm namely MRMR was used to select a 18-D feature subset from the original 84-D feature vector for subsequent PD classification step. Similar feature selection scheme was also adopted in [43], and the authors demonstrated that using only the first feature indicated by the MRMR algorithm was sufficient for accurate recognition of UHF PD signals.…”

Section: ) Feature Selection Algorithmsmentioning

confidence: 99%

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A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.

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Section: ) Transform Domain-based Featuresmentioning

confidence: 76%

Section: ) Feature Selection Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A negative relationship occurs when the value of the feature increases, and then it decreases the other or vice versa. Zero correlation is observed when one feature does not affect the other [51]. The analysis of the correlation between all the features by tuning the Pearson's coefficient is in Equation ( 2), which describes the coefficient for tuning all the features.…”

Section: Correlation Featuresmentioning

confidence: 99%

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

et al. 2023

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Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

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Partial Discharge Detection Method for Gas Insulated Switchgear Based on Acoustic Array

Liang,

Yu,

Jiapaer

et al. 2024

Lecture Notes in Electrical Engineering

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Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

Cited by 8 publications

References 76 publications

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Partial Discharge Detection Method for Gas Insulated Switchgear Based on Acoustic Array

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