PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals

Deep learning techniques for transmission line fault classification – A comparative study

Automated accurate fire detection system using ensemble pretrained residual network

The importance of Power Transmission System PTS fault detection and classification is increasing day by day as because consumption of electricity is increasing. Short circuit fault in Power Transmission Line Network PTLN can cause severe damage to the power transmission system as well as economic loss. Power Transmission System requires new methods to detect and classify fault behaviour to prevent it from heavy damage. Machine Learning ML algorithms can be very effective to classify and detect various types of faults within the PTLN. There are variety of ML algorithms to recognise and classify the faults but as complexity of PTS is increasing day by day, reliability of these algorithms is decreasing. This study uses various types of ML algorithms to generate predictive models to evaluate what kind of algorithm is more appropriate to recognise and classify faults within the PTLN. Faults investigated in this research work include (L-L) double line fault, (L-L-L) three phase fault, (L-G) line to ground fault, (L-L-G) double line to ground fault, and (L-L-L-G) three phase fault with the involvement of the ground. The data was evaluated using six (06) ML algorithms that are Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (Knn), Random Forest, XGBoost (XGB) and Naive Bayes (NB) for recognise of fault and classification within the PTLN. The performance of ML algorithms obtained by comparing the results and determine which algorithm is fast and more accurate. These results can be used to create more effective ML algorithms for PTS. The results indicate that the application of ML algorithms for PTS task could improve the PTLN yield and save time for technical teams.

We propose a new batch bioprocess monitoring approach which combines the Gaussian mixture model (GMM) with the multiway analysis in localized discriminant embedding subspace for detecting and classifying different types of faults. Due to the inherent process multimodality within abnormal points, the capability of traditional multimay locality preserving projection (MLPP) is degraded. For the routine bioprocess operation with abnormal events, GMM is used to partition the training set with various types of faults into different clusters. Three localized distance matrices reflecting the relationships of nearby points are then computed. The extracted leading discriminant embedding directions can not only separate the normal and faulty data by maximizing the distance among nearby data points from different clusters, but also preserve the intrinsic geometrical structure within the multiple faulty clusters by minimizing the small distance and penalizing the large distance among neighboring data from the same cluster. The newly developed multiway localized discriminant embedding (MLDE) approach is applied to two test scenarios in the fed-batch penicillin fermentation process and compared with the conventional MLPP method. The results demonstrate that the MLDE approach is superior to the MFDA method in detecting abnormal events and classifying different types of faults in fed-batch process with higher accuracy and sensitivity.

This paper describes the experiment of analyzing MRI images of the brain of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The aim of this research is to use new methods of extracting features from MRI images and selecting the most relevant features. The values of cortical thickness in regions of interest are used as a features for MRI images. The Support Vector Machine — Recursive Feature Elimination, Infinite Feature Selection, Relief-F, Feature Selection Via Concave Minimization and Support Vector Machine, Minimum Redundancy Maximum Relevance, Fisher, Unsupervised Discriminative Feature Selection, L0-norm Based Feature Selection, and Feature Selection via Eigenvector Centrality techniques for feature selection and ranking were used. In the experiment, the MRI images from ADNI database was used, which contains data from 539 subjects. In the result, the following maximum values of area under ROC curves for three datasets are obtained: NC-AD — 0.90, NC-MCI — 0.71, MCI-AD — 0.69. Classification accuracy with linear Support Vector Machine for the corresponding datasets: NC-AD — 80%, NC-MCI — 64%, MCI-AD — 65%. The results of this paper can be used for building a classifier in space of selected features.

In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

Background and PurposeArrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals.MethodWe have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion.ResultsOur proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation.ConclusionOur results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

This paper proposes a new scheme for locating the faults of three‐terminal double‐circuit transmission line without transposer in the phase domain that is not affected by type of fault. Synchronous voltage and current were measured by phasor measurement units (PMUs) installed at three busbars. Considering the coupling effect of parallel lines, the distribution line model was used to obtain more accurate results and third‐order Taylor series expansion to decouple distributed line model. The phase and branch of all faults on the transmission lines were effectively identified, and various types of faults were accurately located including normal shunt fault, evolving fault and cross‐country fault. The proposed algorithm was tested under different fault locations, resistances, initial angles and types (including cross‐country faults and evolving faults). In addition, the effects of different sampling rates, measurement errors, earth resistivities and transmission line parameter errors were also considered. The simulation calculation was performed in MATLAB software and three‐terminal transmission lines were simulated in PSCAD/EMTP software. The results indicate that maximum estimated error was less than 1.8631%.

This paper describes tests made to determine the characteristics of the clearance of faults in several types of a-c. low-voltage network copper-conductor cables installed in accordance with present standards of underground construction, and includes a discussion of test results together with other relative data studied. The investigation included the burning clear of various types of faults in buried non-magnetic sheath cables, and non-metallic sheath and lead-covered cables for duct installation. A comparison is made of the burn-off characteristics of the several cable constructions and their installations. The amount of short-circuit current and length of time required to clear the different types of faults in No. 4/0, 350,000-and 500,000-cir. mil cables have been determined by test and by calculations in some instances. Conclusive data were not obtained to clear up all present conflicting opinions related to the burn-off characteristics of a-c. low-voltage network cables. However, a study of the subject matter would seem to establish at least a partial basis in regard to burn-off characteristics for determining the conductor size of low-voltage network mains and the spacing of network transformers. Desirable developments for a-c. network cables indicated by the results of this investigation are emphasized.

In an electric power system, a power transformer is one of the most critical equipment and cannot be separated from possible abnormal conditions due to fault. Dissolved Gas Analysis (DGA) is a reliable technique for detecting the presence of a fault condition that just occurred in oil immersed transformer. Basically DGA is a process to calculate the levels or values of hydrocarbon gases that are formed due to abnormalities. The gas inside the transformer can function as a marker for various types of faults. In this paper, for DGA testing and evaluation of the type of fault in the power transformer using the interpretation of IEEE std C57.104 and IEC 60599. The method used for DGA testing according to the IEEE interpretation is total dissolved combustible gas (TDCG), key gas, doernenburg ratio, and roger ratio. While the method used for DGA testing at IEC 60599 is the duval triangle, the basic gas ratio and CO2/CO ratio. From the results of DGA tests that have been carried out, all of these methods will be used to ascertain the type of fault that occurs in the power transformer.

Electric power sector is broadly categorised into four sections as electric power generation, transmission, distribution and utilisation. Generated power has to be transmitted from generating station to load centre through transmission system with higher efficiency. It is necessary to know various types of faults and their impact on power system reliability. Power system faults are broadly classified into open circuit and short circuit faults. In this paper, effect of short circuit faults on a three phase transmission system has been analysed. Entire work has been done using MATLAB simulink environment. The complete modelling and proposed simulation work have been done, and the analysis of transient faults was carried out with the MATLAB simulink software. In this work, MATLAB software is used for creating various types of faults for shorter duration on transmission lines such as L-G fault, LL-G fault, LL fault and three phase short circuit and it also analysed the effect of faults on voltage and current through different phases.

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems.

Recognition of lumps in the breast region is well exploited through mammography. For radiologist, the identification of cancerous tissues is tedious and time-consuming, and many automated computer-aided techniques have been proposed to enhance the clinical diagnosis. This specific research work suggests the application of neighborhood component analysis (NCA) as a feature selection technique for breast mammograms classification. Tamura features (coarseness, contrast and directionality) which provide the characteristics of image surface and objects appearance in images and statistical features (mean, variance, skewness, kurtosis, energy and entropy) were extracted from the breast mammograms, and NCA was applied to identify the best features. Finally, support vector machine classification was performed to classify abnormal condition from normal. Simulation study using the local hospital datasets revealed an overall classification accuracy of 93% by making use of quadratic kernel with SVM classifier.

A hybrid ensemble-filter wrapper feature selection approach for medical data classification