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About 10,000 papers for “fault classification”

Fault classification

Mohamed Nounou, Hazem Nounou, Nour Basha et al. · Elsevier eBooks · 2025

Cited by 0

Fault Classification: Discussion

M. J. Rickard · Geological Society of America Bulletin · 1972

Research Article| August 01, 1972 Fault Classification: Discussion M. J RICKARD M. J RICKARD Geology Department, Australian National University, Canberra, Australia Search for other works by this author on: GSW Google Scholar GSA Bulletin (1972) 83 (8): 2545–2546. https://doi.org/10.1130/0016-7606(1972)83[2545:FCD]2.0.CO;2 Article history received: 17 Jan 1972 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation M. J RICKARD; Fault Classification: Discussion. GSA Bulletin 1972;; 83 (8): 2545–2546. doi: https://doi.org/10.1130/0016-7606(1972)83[2545:FCD]2.0.CO;2 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGSA Bulletin Search Advanced Search Abstract No abstract available This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Cited by 36

Fault classification proportion.

Yi-Qiang Xia (20161326), Yang Yang (45629) · Figshare · 2024

<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. MWOA tackles common optimization problems like local optima and premature convergence using two key methods: shrinking encircling and spiral position updates. In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. After that, comparisons were conducted between MWOA and seven recently proposed metaheuristics, utilizing the CEC2005 benchmark functions to assess MWOA’s optimization performance. Moreover, the Wilcoxon rank sum test is used to verify the effectiveness of the proposed algorithm. Eventually, MWOA was juxtaposed with the BiLSTM classifier and six other meta-heuristics combined with the BiLSTM classifier. The aim was to affirm that MWOA-BiLSTM outperforms its counterparts, showcasing superior performance across crucial metrics such as accuracy, precision, recall, and F1-Score. The study results unequivocally demonstrate that MWOA showcases exceptional optimization capabilities, adeptly striking a harmonious balance between exploration and exploitation.</p></div>

Cited by 0Open access

Fault classification confusion matrix.

Hao Chen (5190), Chenlei Han (17537615), Yucheng Zhang (136327) et al. · Figshare · 2023

<div><p>Mechanical faults are the main causes of abnormal opening, refusal operation, or malfunction of high-voltage circuit breakers. Accurately assessing the operational condition of high-voltage circuit breakers and delivering fault evaluations is essential for the power grid’s safety and reliability. This article develops a circuit breaker fault monitoring device, which diagnoses the mechanical faults of the circuit breaker by monitoring the vibration information data. At the same time, the article adopts an improved deep learning method to train vibration information of high-voltage circuit breakers, and based on this, a systematic research method is employed to identify circuit breaker faults. Firstly, vibration information data of high-voltage circuit breakers is obtained through monitoring devices, this vibration data is then trained using deep learning methods to extract features corresponding to various fault types. Secondly, using the extracted features, circuit breaker faults are classified and recognized with a systematic analysis of the progression traits across various fault categories. Finally, the circuit breaker’s fault type is ascertained by comparing the test set’s characteristics with those of the training set, using the vibration data. The experimental results show that for the same type of circuit breaker, the accuracy of this method is over 95%, providing a more efficient, intuitive, and practical method for online diagnosis and fault warning of high-voltage circuit breakers.</p></div>

Cited by 0Open access

Fault classification setup [15].

Zawar Ahmed Khan (22574556), Muhammad Amir Raza (22391989), Muhammad I. Masud (22574559) et al. · Figshare · 2025

<div> This study explores the design of an effective fault classification algorithm for 3 phase induction motor, an integral unit in many industrial systems. It is found that traditional fault detection methods and deep learning approaches are both effective; however, current techniques can either be computationally exhaustive, or suffer from low accuracy, thus making them inapplicable in many real-world settings. To address these limitations, this study evaluates different machine learning algorithms for accurate and efficient fault detection using a dataset of triaxial vibrational data converted into current variables. A dataset of triaxial vibrational current data at 0.7 mm bearing and rotor faults at various loads (100W, 200W, and 300W) were considered. For the data preprocessing, we handled with the missing values by interpolation and handle data imbalance fault types with Synthetic Minority Over-sampling Technique (SMOTE). Through Fast Fourier Transform (FFT) techniques, the frequency domain information were extracted, which is key for current signals, adding to the feature set. In addition, dimensionality reduction with Principal Component Analysis (PCA) and feature selection was done with SelectKBest. Then, the different machine learning models such as Random Forest (RF), Decision Tree (DT), k-nearest neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) was trained to optimize the hyperparameters and make them perform to its best possible. The results shows the accuracy and performance of all models, DT and RF show good performance, with 99.95% accuracy, while KNN performs well, but at a higher computational cost in testing. Generally known for its capability to handle all the complex dataset, XGBoost wasn’t able to perform in this scenario as it got an accuracy of 87.13%, indicating potentially more optimization is required for the model. This work serves as the groundwork for future work with a multiplicity of fault types, motor specifications, and the incorporation of additional feature-engineering techniques to develop a more robust and intelligent framework for fault detection. </div>

Cited by 0Open access

Classification of fault breccias and related fault rocks

Nigel Woodcock, Kate Mort · Geological Magazine · 2008

Abstract Despite extensive research on fault rocks, and on their commercial importance, there is no non-genetic classification of fault breccias that can easily be applied in the field. The present criterion for recognizing fault breccia as having no ‘primary cohesion’ is often difficult to assess. Instead we propose that fault breccia should be defined, as with sedimentary breccia, primarily by grain size: with at least 30% of its volume comprising clasts at least 2 mm in diameter. To subdivide fault breccias, we advocate the use of textural terms borrowed from the cave-collapse literature – crackle, mosaic and chaotic breccia – with bounds at 75% and 60% clast content. A secondary breccia discriminant, more difficult to apply in the field, is the ratio of cement to matrix between the clasts. Clast-size issues concerning fault gouge, cataclasite and mylonite are also discussed.

Cited by 292

Overall accuracy of fault classification.

Hao Chen (5190), Chenlei Han (17537615), Yucheng Zhang (136327) et al. · Figshare · 2023

&lt;div&gt;&lt;p&gt;Mechanical faults are the main causes of abnormal opening, refusal operation, or malfunction of high-voltage circuit breakers. Accurately assessing the operational condition of high-voltage circuit breakers and delivering fault evaluations is essential for the power grid’s safety and reliability. This article develops a circuit breaker fault monitoring device, which diagnoses the mechanical faults of the circuit breaker by monitoring the vibration information data. At the same time, the article adopts an improved deep learning method to train vibration information of high-voltage circuit breakers, and based on this, a systematic research method is employed to identify circuit breaker faults. Firstly, vibration information data of high-voltage circuit breakers is obtained through monitoring devices, this vibration data is then trained using deep learning methods to extract features corresponding to various fault types. Secondly, using the extracted features, circuit breaker faults are classified and recognized with a systematic analysis of the progression traits across various fault categories. Finally, the circuit breaker’s fault type is ascertained by comparing the test set’s characteristics with those of the training set, using the vibration data. The experimental results show that for the same type of circuit breaker, the accuracy of this method is over 95%, providing a more efficient, intuitive, and practical method for online diagnosis and fault warning of high-voltage circuit breakers.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Fault classification for distance protection

Magnus Akke · IEEE PES Transmission and Distribution Conference and Exhibition · 2003

This paper presents an overview of power system fault classification methods and challenges. It also contains some ideas about structured testing.

Cited by 7

Fault Type Enumeration and Classification

M.M. Hugue · 1991

Abstract : The author defines a 'fault' as the identified or hypothesized cause of an error. An error is the manifestation of a fault, an undesired state either at the boundary or at an internal point in the system. A failure is the inability of the system or component to provide the specified service caused by an error. This report surveys of fault taxonomies used in classifying faults, and enumerates many commonly used fault types. Faults can be classified according to many attributes including cause, origin, persistence, duration, nature, extent, value, activity, symmetry and malice. After defining these and other fault attributes, we classify some fault types, and discuss the difficulties encountered in classifying many faults according to these taxonomies without sufficient knowledge of the characteristics of the environments, systems, and applications in which the faults arise. We enumerate and define faults in three classes: physical, environmental and operational faults. Finally, we present a total fault classification to be used within systems to define the classes of faults of interest and to specify the measures taken to tolerate those faults.

Cited by 2

A dynamic fault classification scheme

Bernhard Fechner · 2008

From [7] further properties of intermittent faults can be derived which help to classify these faults: a repeated manifestation at the same location and that they occur in bursts. Naturally, intermittent and permanent faults and can be recovered by replacing the faulty component.

Cited by 1

Smart fault location and fault classification in transmission line

A.S. Neethu, T. S. Shiny Angel · 2017

Transmission lines are the essential link between power stations and consumers, which carries bulk amount of power to the required premises. So protection of transmission lines become more predominant and need exact fault location on transmission line. This paper presents a better way for finding fault location using discrete wavelet transform (DWT) and artificial neural network (ANN) and one algorithm is developed for finding the type of fault using wavelet transform. The simulation is carried out using MATLAB software.

Cited by 16

Phasor measurement unit application-based fault allocation and fault classification

Sonu Kumar Bairwa, Satyendra Pratap Singh · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2023

This paper makes a contribution to the field of fault location finding in a new way that helps in the improvement of grid reliability. This paper proposes a study-based approach for fault allocation and fault type classification that uses the study of voltage and current frequency during the abnormal condition. Although, ideally frequency of voltage and current are the same in the abnormal condition they may differ from each other. This difference in frequency is separately measured by the phasor measurement unit (PMU) block at MATLAB/Simulink platform. The PMU (PLL-based, positive-sequence) block is inspired by the IEEE Std C37.118.1-2011. In this approach, we measure the line voltage and current frequency variation with the help of installed PMU after this we present this measurement in characteristics form with the help of the scoping tool in MATLAB/Simulink and study them one by one, and proposed a conclusion for fault location identification and fault type classification. The proposed approach is able to identify the source side and load side fault location and also able to classify faults into two categories namely symmetrical and asymmetrical. The proposed approach is tested on two MATLAB/Simulink models and observed satisfactory.

Cited by 0Open access

Fault Classification in Distribution Systems Bsaed on Fault Current Angles

Angelo R. Oliveira, Paulo Augusto Nepomuceno Garcia, Leonardo Willer de Oliveira et al. · IEEE Latin America Transactions · 2016

This paper presents an intelligent fault classification scheme to distribution systems by using artificial neural networks associated with fault current angles information. The objective is to classify series, shunt and simultaneous faults with cable either on the source or on the load side. Results show that the scheme is efficient to detect cable disruption, improving distribution system security. The IEEE distribution test feeders are used to show the effectiveness of the proposed approach.

Cited by 10