PD Detection Research Articles

Excitation energy transfer based naphthalimide-boron-dipyrromethene dyads as two-photon fluorescent probes for palladium(0) detection and live cell imaging

Increasing industrial demand has accelerated the consumption and contamination of palladium (Pd) worldwide. The implementation of fluorescent probes, especially with two-photon excitation, holds great promise for Pd detection in environmental and biological analysis. In this work, two naphthalimide (NPI)-boron-dipyrromethene (BDP) dyads (named NBH and NBN) were developed as two-photon fluorescent probes for Pd(0) detection based on an excitation energy transfer (EET) strategy, employing NPI as the energy donor and BDP as the acceptor. Spectroscopic studies revealed that both probes exhibited a fast response to Pd(0) in a pronounced fluorescence "ON-OFF" manner. The Pd-catalyzed Tsuji-Trost reaction activated an intramolecular photoinduced electron transfer (PET) process between the aniline and BDP moieties, resulting in the fluorescence quenching of the BDP fluorophore. The reaction-based probes exhibited excellent sensitivity and selectivity for Pd(0) species with a detection limit as low as 2.4 nM. These probes were successfully applied to the determination of Pd residues in drug, soil, and water samples. A simple smartphone-based platform was also constructed to determine Pd(0) via an RGB reader. With negligible cytotoxicity, confocal imaging study validated their feasibility of monitoring Pd(0) in living cells through multiple excitation channels.

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

AbstractPartial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

ABSTRACTParkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep learning‐based model for the diagnosis of PD using a resting state electroencephalogram (EEG) signal. The objective of the study is to develop an automated model that can extract complex hidden nonlinear features from EEG and demonstrate its generalizability on unseen data. The model is designed using a hybrid model, consisting of a convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), and attention mechanism. The proposed method is evaluated on three public datasets (UC San Diego, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Dynamic channelwise functional-connectivity states extracted from resting-state EEG signals of patients with Parkinson’s disease

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease that usually happens to elderly people, with a wide range of motor and dementia symptoms. An objective and convenient biomarker for PD detection is extremely valuable, especially one that could be acquired non-invasively and low-costly. To this end, this study used resting-state scalp electroencephalography (EEG) signals to explore dynamic functional-connectivity (dFC) states between each pair of EEG recording channels, without source localization.MethodsdFC refers to synchronization patterns over time between each pair of EEG channels. First, five frequency bands were extracted from EEG signals with fourth-order Butterworth bandpass filter, including delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (8–30 Hz) and gamma (30–50 Hz). Then, after non-random joint fluctuation was measured with weighted symbolic mutual information (wSMI) algorithm, whole-brain dynamic channelwise dFC states were estimated, and classified with k-means clustering. At last, FC state occurrences were calculated, and ANOVA analyses were performed for each state. Two open-source resting-state EEG data sets (https://doi.org/10.18112/openneuro.ds002778.v1.0.4: 32 channels, 16 health controls and 15 PD subjects. https://doi.org/10.18112/openneuro.ds003490.v1.1.0: 64 channels, 25 health controls and 25 PD subjects) were used to test our methods.ResultsSignificant changes in proportions of various dFC states within beta frequency-band were consistently observed in these both data sets (p value < 0.05).ConclusionsOur findings suggest that channelwise dFC states within beta frequency-band directly extracted from resting-state scalp–EEG recordings could potentially serve as a biomarker of PD.

Parkinson’s Detection Using Voice Features and Spiral Drawings

Parkinson's is a dynamic neurodegenerative disease that presents multiple symptoms that advance over time. Our project proposes an innovative Parkinson's discovery machine learning model that combines both voice examination and spiral drawings assessments to capture numerous angles of the disease's symptomatology. Our approach looks for developing a comprehensive Parkinson’s detection model over different stages and symptoms of the disease. By integrating voice analysis techniques to discern subtle changes in speech patterns and spiral drawing assessments to evaluate motor function, our method aims to provide a more holistic assessment of PD symptoms. By leveraging the complementary strengths of voice analysis and spiral drawing assessments, our proposed PD detection project aims to overcome the limitations of existing approaches and provide clinicians with a more comprehensive model for early detection, diagnosis and monitoring of Parkinson's Disease. Ultimately, this initiative strives to enhance patient outcomes, improve treatment efficacy, and advance our understanding of PD progression.

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System.

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

An innovative approach to enhance image alignment through affine transformation, allowing images to be rotated from 0 to 135 degrees. This transformation is a crucial step in improving the diagnostic process, as image misalignment can lead to inaccurate results. The accurate alignment sets the stage for a robust U-Net model, which excels in image segmentation. Precise segmentation is vital for isolating affected brain regions, aiding in the identification of PD-related anomalies. Finally, we introduce the DenseNet architecture model for disease classification, distinguishing between PD and non-PD cases. The combination of these DL models outperforms existing diagnostic approaches in terms of acceptance precision (99.45%), accuracy (99.95%), sensitivity (99.67%), and F1-score (99.84%). In addition, we have developed user-friendly graphical interface software that enables efficient and reasonably accurate class detection via Magnetic Resonance Imaging (MRI). This software exhibits superior efficiency contrasted to current cutting-edges technique, presenting an encouraging opportunity for early disease detection. In summary, our research tackles the problem of low accuracy in existing PD diagnostic models and addresses the critical need for more precise and timely PD diagnoses. By enhancing image alignment and employing advanced DL models, we have achieved substantial improvements in diagnostic accuracy and provided a valuable tool for early PD detection.

Advanced Preliminary Screening for PD Detection for Overhead Distribution Lines with Covered Conductors

This study proposes a novel approach to optimize partial discharge detection in overhead lines with covered conductors. The approach uses 2D Convolutional Neural Networks and HW neural network accelerator Edge TPU. The critical advancement of this research lies in exploring two distinct 2D classification methods - 2D histograms and spectrogram - and adjusting neural network thresholds to selectively identify potential positive PD samples with enhanced accuracy. The proposed approach addresses the prevalent challenge of costly and limited remote data transmission in PD detection. It significantly reduces the need for extensive data transfer by focusing on potentially positive samples. This evaluation of two approaches for detection of partial discharges, coupled with the strategic threshold adjustment, presents a novel solution in the realm of PD detection, offering increased efficiency and cost-effectiveness. By evaluating and comparing these strategies of 2D classification and threshold optimization, this research contributes to the field of partial discharges detection. It proposes a method that not only minimizes operational costs but also aligns with environmental sustainability goals, paving the way for more advanced maintenance practices in power transmission systems.

Research on a New PD Detection Sensor in Transformer Oil Tank Seam

Research on a New PD Detection Sensor in Transformer Oil Tank Seam

Colorimetric Paper-Based Analytical Devices (PADs) Backed by Chemometrics for Pd(II) Detection.

This paper presents the development of cheap and selective Paper-based Analytical Devices (PADs) for selective Pd(II) determination from very acidic aqueous solutions. The PADs were obtained by impregnating two cm-side squares of filter paper with an azoic ligand, (2-(tetrazolylazo)-1,8 dihydroxy naphthalene-3,6,-disulphonic acid), termed TazoC. The so-obtained orange TazoC-PADs interact quickly with Pd(II) in aqueous solutions by forming a complex purple-blue-colored already at pH lower than 2. The dye complexes no other metal ions at such an acidic media, making TazoC-PADs highly selective to Pd(II) detection. Besides, at higher pH values, other cations, for example, Cu(II) and Ni(II), can interact with TazoC through the formation of stable and pink-magenta-colored complexes; however, it is possible to quantify Pd(II) in the presence of other cations using a multivariate approach. To this end, UV-vis spectra of the TazoC-PADs after equilibration with the metal ions solutions were registered in the 300-800 nm wavelength range. By applying Partial Least Square regression (PLS), the whole UV-vis spectra of the TazoC-PADs were related to the Pd(II) concentrations both when present alone in solution and also in the presence of Cu(II) and Ni(II). Tailored PLS models obtained with matrix-matched standard solutions correctly predicted Pd(II) concentrations in unknown samples and tap water spiked with the metal cation, making the method promising for quick and economical sensing of Pd(II).

A pragmatic approach of Parkinson disease detection using hybrid case-based reasoning neuro-fuzzy classification system over Mobile edge computing

Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier.

A high‐efficiency portable system for insulation condition assessment of wind farm inter‐array cables with double‐sided partial discharge detection and localisation

AbstractPartial discharge (PD) diagnosis is a crucial tool to assess the insulation condition of wind farm cables. Among PD diagnosis techniques, PD localisation is promising as it can provide target maintenance indicators on the insulation weak points of the cables. Accordingly, this paper developed a portable PD detection and location system for wind farm inter‐array cables. The system consists of two non‐invasive and lightweight testing units, which can be conveniently deployed on an energised cable, enabling highly efficient online PD diagnosis of the widely distributed inter‐array cables. The system achieves accurate PD localisation of the energised cable via an improved double‐sided travelling wave method. The method exhibits two superior features: the double‐sided testing units are accurately synchronised via the joint application of Global Position Systems and a pulse‐based interaction process, and a windowed phase difference method is proposed and integrated into the system to robustly estimate the time‐of‐arrival difference in low signal‐to‐noise ratio environment. Validation experiments were conducted on both a 10‐kV cable in the laboratory and a real 35‐kV cable in an on‐shore wind farm.

Low-Cost Online Partial Discharge Monitoring System for Power Transformers

The article presents in detail the construction of a low-cost, portable online PD monitoring system based on the acoustic emission (AE) technique. A highly sensitive piezoelectric transducer was used as the PD detector, whose frequency response characteristics were optimized to the frequency of AE waves generated by discharges in oil-paper insulation. The popular and inexpensive Teensy 3.2 development board featuring a 32-bit MK20DX256 microcontroller with the ARM Cortex-M4 core was used to count the AE pulses. The advantage of the system is its small dimensions and weight, easy and quick installation on the transformer tank, storage of measurement data on a memory card, battery power supply, and immediate readiness for operation without the need to configure. This system may contribute to promoting the idea of short-term (several days or weeks) PD monitoring, especially in developing countries where, with the dynamically growing demand for electricity, the need for inexpensive transformer diagnostics systems is also increasing. Another area of application is medium-power transformers (up to 100 MVA), where temporary PD monitoring using complex measurement systems requiring additional infrastructure (e.g., control cabinet, cable ducts for power supply, and data transmission) and qualified staff is economically unjustified.

On-site Insulation Tests for MV cable using Very Low Frequency and Damped AC Techniques

MV cables are vital components in urban power networks. Several insulation test techniques are proposed for guaranteeing the operation of MV cables. In this paper, two kinds of off-line testing techniques are introduced, namely Tanδ measurement using VLF and PD detection using DAC. Firstly, the operation principle of each technique is introduced. Then, a simulation model was built in Simulink platform to demonstrate the operation processes of detecting the defects in the cable. At last, an on-site case using both VLF and DAC techniques is introduced for presenting the functions of different techniques.

Deep convolutional neural network for partial discharge monitoring system

In the insulating material, the event of PDs takes place due to the air pockets, voids, or imperfections in such fields. In HV components like cables, the PD detection aids in ascertaining the insulating material condition. Consequently, the territory of PD estimation and conclusion is acknowledged as one of the most significant non-destructive methods for surveying the quality and specialized respectability of HV power mechanical assembly and links. In this work, a new data-driven model to recognize the condition of PD pulses of power cables is proposed with the aid of optimized Convolutional Neural Networks (CNNs). The two main stages of the developed framework are feature extraction and recognition. The data downloaded by VSB from the power cable is subjected to dimensionality reduction via Principal Component Analysis (PCA). Then, from these signals, the features are extracted with the aid of technical indicators like Rate of change (ROC), Relative Strength Index (RSI), Adaptive Moving Average (AMA), and Standard Deviation. Additionally, the features of the original power line data are also extracted. These features are put into an enhanced CNN as input. A new hybrid version known as the Combined Sealion-Swarm Optimization Algorithm (CS-SOA) optimizes the weight and activating function of CNN to increase the accuracy of the PD condition observation. From the result, it can be noticed that the accuracy of the proposed model, is 77.7%, 5.55%, and 3.335% higher than the existing models like SVM, CNN, and LSTM at TP= 80.

The Design of Calibration System Based on Cable Accessories

In today’s society, people can’t live without electricity. During the operation of power equipment, there may be various potential safety risks. In particular, partial discharge is easy to occur at the terminal. If people can not deal with the problem of partial discharge in time, equipment continue to live.Setting can cause security, so the electric power industry staff attaches great importance to the local discharge monitoring problem.Ultrasonic technology is better so it is gradually into the application of partial discharge detection.But the ultrasonic detection technology is to detect the intensity of ultrasound, and our traditional method is to measure the discharge of partial discharge method, Therefore, in order to obtain the relationship between ultrasonic intensity and partial discharge, the following experiments are made. After we pass the power frequency voltage withstand device, the transformer is continuously pressurized.Gain DB and discharge PC were obtained by digital partial discharge instrument and ultrasonic partial discharge instrument. After fitting, the linear relation is obtained and the quality inspection of PD detector is completed.It can make the detection effect of partial discharge detector more complete, and provide the basis for the quality inspection of the detector, This system also provides a strong support for the wide application of ultrasonic partial discharge detector.

PD Signal Detection Model Based on Multiscale Simulation of EM Generation and Transmission

Nowadays, the digital twin method has been gradually applied to power equipment condition monitoring. Since a main fundamental part of the digital twin is a simulation process with better fitting for the real one, this paper is devoted to building up a PD detection model to simulate the generation and transmission of electromagnetic (EM) wave signals caused by PDs, especially those inside the void defects. This model combines the micro discharge process with the transmission process inside certain geometry structures. Specifically, two commonly used methods are applied to simulate the processes correspondingly. The dielectric barrier discharge (DBD) model is used for the PD simulation inside void defects, while the finite-difference time-domain (FDTD) method is for the EM signal transmission process. The pulsed discharge current can be obtained during the simulation of DBD, which can function as the excitation source of the transmission process solved by FDTD. According to the PD detection model, the influences on the simulation results are discussed, including the impact on the discharge current caused by coefficient variation and the impact on the transmission process caused by the power equipment geometry structure and materials. As mentioned above, this paper is aimed at building a direct relationship between the observed EM signal and the micro discharge process, which reveals the physical process of PD signal measurement in virtual space that can provide more features for PD pattern recognition or noise elimination, and further the mechanism and theoretical analysis support for digital twin application.

Detection of Parkinson’s Disease Using the Spiral Diagram and Convolutional Neural Network

This study intends to propose a PD detection using spiral sketching and CNN. The fundamental idea is to analyze a person's spiral drawings and classify them as healthy or having Parkinson's disease. Spiral sketches drawn by healthy people look almost like standard spiral shapes. However, the spirals drawn by people with Parkinson's disease look distorted because they deviate significantly from their perfect spiral shape due to slow movement, and poor hand-brain coordination. In this paper Convolution, Neural Network is used to detect Parkinson’s, and 83.6% classification accuracy is obtained.

New nano materials-based optical sensor for application in rapid detection of Fe(II) and Pd(II) ions

For the analytical determination of Fe(II) and Pd(II) concentrations, a novel optical sensor based on spectrophotometric technique was used. The optical sensor was prepared by direct immobilization of a novel synthesized chromophore, 5-amino-phenanthrolin-3 formyl salicylic acid, onto nanocellulose. Human vision can identify the color associated with Fe II ions, and spectrophotometric methods can measure it with detection and quantification limits of 0.239 and 0.796 ppb, respectively. Pd(II) detection and quantification limits were 0.318 ppb and 1.06 ppb, respectively. The effects of various parameters on the detection of Fe(II) or Pd(II) ion content were investigated and optimized. The optical phenanthroline-nanocellulose (5-Phen) sensor could be reproduced multiple times and used with a higher capacity each time. The results demonstrated that the 5-Phen sensor could measure Fe(II) in human blood serum accurately and successfully even without any pre-concentration.