Log in

Relevant bibliographies by topics / Fault identification and classification / Journal articles

To see the other types of publications on this topic, follow the link: Fault identification and classification.

Journal articles on the topic 'Fault identification and classification'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fault identification and classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Mahaweerawat, Atchara, Peraphon Sophatsathit, Chidchanok Lursinsap, and Petr Musilek. "MASP – An Enhanced Model of Fault Type Identification in Object-Oriented Software Engineering." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 312–22. http://dx.doi.org/10.20965/jaciii.2006.p0312.

Full text

Abstract:

To remain competitive in the dynamic world of software development, organizations must optimize the use of their limited resources to deliver quality products on time and within budget. This requires prevention of fault introduction and quick discovery and repair of residual faults. In this paper, a new model for predicting and identifying of faults in object-oriented software systems is introduced. In particular, faults due to the use of inheritance and polymorphism are considered as they account for significant portion of faults in object-oriented systems. The proposed MASP model acts as a f

APA, Harvard, Vancouver, ISO, and other styles

2

Liu, Chunyang, Weiwei Zou, Zhilei Hu, et al. "Bearing Health State Detection Based on Informer and CNN + Swin Transformer." Machines 12, no. 7 (2024): 456. http://dx.doi.org/10.3390/machines12070456.

Full text

Abstract:

In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different f

APA, Harvard, Vancouver, ISO, and other styles

3

Pérez-Ruiz, Juan Luis, Igor Loboda, Iván González-Castillo, Víctor Manuel Pineda-Molina, Karen Anaid Rendón-Cortés, and Luis Angel Miró-Zárate. "A comparative study of data-driven and physics-based gas turbine fault recognition approaches." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 4 (2021): 591–609. http://dx.doi.org/10.1177/1748006x21989648.

Full text

Abstract:

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of esti

APA, Harvard, Vancouver, ISO, and other styles

4

Cheng, Guanyuan, and Shaojian Song. "Fault Detection and Identification in MMCs Based on DSCNNs." Energies 16, no. 8 (2023): 3427. http://dx.doi.org/10.3390/en16083427.

Full text

Abstract:

Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-

APA, Harvard, Vancouver, ISO, and other styles

5

Mashayekhi, V., S. Hasani Borzadaran, and M. Hoseintabar Marzebali. "Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks." International Transactions on Electrical Energy Systems 2022 (February 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/4224356.

Full text

Abstract:

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the lon

APA, Harvard, Vancouver, ISO, and other styles

6

Tong, Shuiguang, Yidong Zhang, Jian Xu, and Feiyun Cong. "Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (2017): 2280–96. http://dx.doi.org/10.1177/0954406217715483.

Full text

Abstract:

In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fau

APA, Harvard, Vancouver, ISO, and other styles

7

Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu, and Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors." Journal of Physics: Conference Series 2095, no. 1 (2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.

Full text

Abstract:

Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support v

APA, Harvard, Vancouver, ISO, and other styles

8

Lodhi, Raja, and Rajkumar Sharma. "A Practical Approach of Software Fault Prediction Using Error Probabilities and Machine Learning Approaches." International Journal of Research 11, no. 5 (2024): 124–37. https://doi.org/10.5281/zenodo.11195244.

Full text

Abstract:

<em>identification of software faults associated with software. The identification of faults is usually carried out using the task of classification. The task of classification utilises the code attributes and other features to predict the fault instances. The detection of software faults is prominently affected by a poor classification decision and hence an improved decision-making model is required to predict the patterns using the attributes collected out from the datasets. In the first part of the research, the study proposes a Bayes Decision classifier associated with the finding of error

APA, Harvard, Vancouver, ISO, and other styles

9

Bagbaba, Ahmet Cagri, Felipe Augusto da Silva, Matteo Sonza Reorda, Said Hamdioui, Maksim Jenihhin, and Christian Sauer. "Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips." Electronics 11, no. 3 (2022): 319. http://dx.doi.org/10.3390/electronics11030319.

Full text

Abstract:

ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuse

APA, Harvard, Vancouver, ISO, and other styles

10

Dhaked, Dheeraj Kumar, Lokesh Kumar Raman, Dinesh Birla, and Maheep Dwivedi. "Identification and classification of faults using fuzzy logic controller in transmission line." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 417–30. http://dx.doi.org/10.47974/jim-1672.

Full text

Abstract:

This manuscript discusses the identification and categorization of faults in transmission network system with fuzzy logic controller (FLC) with high-speed digital protective relay, which can be used for real-time data analysis. The FLC uses signals for the extraction of original signals for multi-resolution analysis. The proposed FLC technique detects the fault occurrence based on fault index. The stoutness of technique is demonstrated on transmission line models under different fault situations on MATLAB Simulink platform for the post-fault current of all three phases the end of line. In the

APA, Harvard, Vancouver, ISO, and other styles

11

Adly, Ahmed R., Sehiemy Ragab A. El, Mahmoud A. Elsadd, and Almoataz Y. Abdelaziz. "A novel wavelet packet transform based fault identification procedures in HV transmission line based on current signals." International Journal of Applied Power Engineering 8, no. 1 (2019): 11–21. https://doi.org/10.11591/ijape.v8.i1.pp11-21.

Full text

Abstract:

This paper presents an adaptive fault identification algorithm bases on wavelet packet transform (WPT) for two-terminal power transmission lines. The proposed scheme performs four functions which are the fault detection, fault classification, distinguishing among the temporary and the permanent faults, and detection of the arc extinguish instant. The presented algorithm only uses the measured current at one terminal reducing the required cost. Also, it can mitigate the error resulting from the load variations via updating the presetting value. Consequently, it does not need retesting under cha

APA, Harvard, Vancouver, ISO, and other styles

12

Ahmed, R. Adly, A. El Sehiemy Ragab, A. Elsadd Mahmoud, and Y. Abdelaziz Almoataz. "A novel wavelet packet transform based fault identification procedures in HV transmission line based on current signals." International Journal of Applied Power Engineering 8, no. 1 (2020): 11~21. https://doi.org/10.5281/zenodo.7353514.

Full text

Abstract:

This paper presents an adaptive fault identification algorithm bases on wavelet packet transform (WPT) for two-terminal power transmission lines. The proposed scheme performs four functions which are the fault detection, fault classification, distinguishing among the temporary and the permanent faults, and detection of the arc extinguish instant. The presented algorithm only uses the measured current at one terminal reducing the required cost. Also, it can mitigate the error resulting from the load variations via updating the presetting value. Consequently, it does not need retesting under cha

APA, Harvard, Vancouver, ISO, and other styles

13

Zhao, Yuanyuan, Huijuan Hao, Yu Chen, and Yu Zhang. "Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network." Electronics 12, no. 13 (2023): 2826. http://dx.doi.org/10.3390/electronics12132826.

Full text

Abstract:

In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to

APA, Harvard, Vancouver, ISO, and other styles

14

Arfeen, Zeeshan, Ehtisham Arshad, Raja Massod Lark, et al. "Advanced Fault Detection, Classification, and Analysis Framework for HV Transmission Lines using RT Synchronized Monitoring and Control Systems." UCP Journal of Engineering & Information Technology 2, no. 2 (2025): 41–51. https://doi.org/10.24312/ucp-jeit.02.02.436.

Full text

Abstract:

The emergence of new technologies such as IoT, along with the merger of renewable energies, AI, smart grids, and non linear loads is enhancing the complexity of modern power systems and detecting fault as well as its correction much harder. Traditional methods suffer from inadequate speed, accuracy, less coverage, and latency that renders them highly ineffective in varying conditions. Reliable power transmission is vital for modern infrastructure, as faults on transmission lines can disrupt supply, damage equipment, and create safety risks. This paper presents a Fault Detection and Analysis Sy

APA, Harvard, Vancouver, ISO, and other styles

15

Niu, Wei, Guo Qing Wang, Zheng Jun Zhai, and Juan Cheng. "Fault Classification Model of Rotor Based on Support Vector Machine." Applied Mechanics and Materials 66-68 (July 2011): 1982–87. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1982.

Full text

Abstract:

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can cor

APA, Harvard, Vancouver, ISO, and other styles

16

Bhattacharya, Debshree, and Manoj Kumar Nigam. "A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 261–69. http://dx.doi.org/10.17762/ijritcc.v11i7.7934.

Full text

Abstract:

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for co

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Jiajun, Chenjing Li, Yue Liu, Ji Sun, and Haokun Lin. "Single Line-to-Ground Fault Type Multilevel Classification in Distribution Network Using Realistic Recorded Waveform." Sensors 23, no. 21 (2023): 8948. http://dx.doi.org/10.3390/s23218948.

Full text

Abstract:

The further identification of fault types for single line-to-ground faults (SLGFs) in distribution networks is conducive to determining the cause of grounding faults and formulating targeted measures for hidden danger treatment and fault prevention. For the six types of SLGFs generated in the actual power grid, this paper deeply studies their fault characteristics. Firstly, the classification criterion of fault transition resistance is derived by the generation mechanism of fault zero sequence voltage (ZSV). At the same time, by comparing and analyzing the same and different characteristics be

APA, Harvard, Vancouver, ISO, and other styles

18

Ong, Wei Chuan, Fadilah Ab Aziz Nur, Mat Yasin Zuhaila, Ashida Salim Nur, and A. Wahab Norfishah. "Fault classification in smart distribution network using support vector machine." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1148–55. https://doi.org/10.11591/ijeecs.v18.i3.pp1148-1155.

Full text

Abstract:

Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven di

APA, Harvard, Vancouver, ISO, and other styles

19

Adly, Ahmed R., Ragab El Sehiemy, Mahmoud A. Elsadd, and Almoataz Y. Abdelaziz. "A novel wavelet packet transform based fault identification procedures in HV transmission line based on current signals." International Journal of Applied Power Engineering (IJAPE) 8, no. 1 (2019): 11. http://dx.doi.org/10.11591/ijape.v8.i1.pp11-21.

Full text

Abstract:

<p>This paper presents an adaptive fault identification algorithm bases on wavelet packet transform (WPT) for two-terminal power transmission lines. The proposed scheme performs four functions which are the fault detection, fault classification, distinguishing among the temporary and the permanent faults, and detection of the arc extinguish instant. The presented algorithm only uses the measured current at one terminal reducing the required cost. Also, it can mitigate the error resulting from the load variations via updating the presetting value. Consequently, it does not need retesting

APA, Harvard, Vancouver, ISO, and other styles

20

Medattil Ibrahim, Abdul Haleem, Madhu Sharma та Vetrivel Subramaniam Rajkumar. "Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs". Energies 16, № 11 (2023): 4262. http://dx.doi.org/10.3390/en16114262.

Full text

Abstract:

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mai

APA, Harvard, Vancouver, ISO, and other styles

21

Casoli, Paolo, Mirko Pastori, Fabio Scolari, and Massimo Rundo. "A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps." Energies 12, no. 5 (2019): 953. http://dx.doi.org/10.3390/en12050953.

Full text

Abstract:

In recent years, the interest of industry towards condition-based maintenance, substituting traditional time-based maintenance, is growing. Indeed, condition-based maintenance can increase the system uptime with a consequent economic advantage. In this paper, a solution to detect the health state of a variable displacement axial-piston pump based on vibration signals is proposed. The pump was tested on the test bench in different operating points, both in healthy and faulty conditions, the latter obtained by assembling damaged components in the pump. The vibration signals were acquired and exp

APA, Harvard, Vancouver, ISO, and other styles

22

Bisht, Kamlesh S., Nafees Ahamad, and Saurabh Awasthi. "Efficient Fault Classification in Distributed Generation Systems using M-KNN and Grid Search Techniques." International Energy Journal 25, no. 1A (2025): 195. https://doi.org/10.64289/iej.25.01a05.9796852.

Full text

Abstract:

To meet the growing demand for electricity and ensure a sustainable future, there is a significant shift towards distributed generation (DG), including non-renewable energy sources. The stability and reliable operation of DGs, which involve non-uniform power generation, present challenging problems. Proper fault diagnosis and mitigation are crucial in these systems. Consequently, reliable fault identification and mitigation are essential to ensure the trustworthiness and functionality of DGs. Established mathematical models for fault identification, location, and system isolation can be time-c

APA, Harvard, Vancouver, ISO, and other styles

23

Bharath, K. V. S., Frede Blaabjerg, Ahteshamul Haque, and Mohammed Ali Khan. "Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell." Energies 13, no. 12 (2020): 3144. http://dx.doi.org/10.3390/en13123144.

Full text

Abstract:

This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fault identification methods and data limitations in existing data driven approaches. This is achieved by developing a 3D model of fuel cells (FC) based on semi empirical model, analytical representation of electrochemical model, thermal model, and impedance model. The developed model is simulated for membrane drying and flooding faults in PEMFC and t

APA, Harvard, Vancouver, ISO, and other styles

24

Zhang, Yi, Yong Lv, and Mao Ge. "A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy." Entropy 23, no. 2 (2021): 191. http://dx.doi.org/10.3390/e23020191.

Full text

Abstract:

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is

APA, Harvard, Vancouver, ISO, and other styles

25

Nath, Umesh, Kumari Ashwini, and Pramod Sharma. "A REVIEW – FAULT DETECTION AND CLASSIFICATION IN TRANSMISSION AND DISTRIBUTION LINES." Journal of Dynamics and Control 9, no. 5 (2025): 138–46. https://doi.org/10.71058/jodac.v9i5013.

Full text

Abstract:

Fault identification is a critical process in ensuring the reliability and safety of power systems, including electrical grids, mechanical structures, and industrial processes. In this review paper researchers use a various parameter wich is hepful to identify the fault with a minimum duration of time. Those parameters are Hilbert transform (HT), Stockwell transform (ST), Allination Coefficient, and some useful techniques like GSM and IOT based but the most likely topic that’s chosen by researcher is single processing technique. This technique effectively captures and analyzes the signal chara

APA, Harvard, Vancouver, ISO, and other styles

26

Kouachi, Sabah, Nacerdine Bourouba, Kamel Mebarkia, and Imad Laidani. "Analog Circuits Fault Diagnosis Using ISM Technique and a GA-SVM Classifier Approach." Electronics ETF 28, no. 2 (2024): 54–67. https://doi.org/10.53314/els2428054k.

Full text

Abstract:

This present work aims to contribute to the solution of the problems encountered in electronic circuits fault diagnosis. One of these troubleshoots faced is the lack of effective features that help to optimize fault classifier and hence improve circuit fault detection and identification. Thus, our feature extraction approach is based on the CUT’s transfer function. This is deduced from the Matlab identification system IS model (ISM), namely the OE model belonging to the ARMA model’s family. These features are the transfer function polynomial coefficients playing a crucial role in the fault fre

APA, Harvard, Vancouver, ISO, and other styles

27

Nicchiotti, Gianluca, Idris Cherif, and Sebastien Kuenlin. "Unsupervised Learning for Bearing Fault Identification with Vibration Data." PHM Society European Conference 8, no. 1 (2024): 9. http://dx.doi.org/10.36001/phme.2024.v8i1.4047.

Full text

Abstract:

Machine learning methods are increasingly used for rotating machinery monitoring. Usually at system set up, only data of the machinery in healthy conditions, the so-called nominal data, are available for the machine learning phase. This type of training data enables fault detection capabilities and several methods such as Gaussian Mixture Model, One Class Support Vector Machines and Auto Associative Neural Networks (Autoencoders) have been already proved successful for this task. However, in some predictive maintenance applications, information on the type of defect may represent a key element

APA, Harvard, Vancouver, ISO, and other styles

28

Hojabri, Mojgan, Severin Nowak, and Antonios Papaemmanouil. "ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network." Energies 16, no. 16 (2023): 6023. http://dx.doi.org/10.3390/en16166023.

Full text

Abstract:

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance f

APA, Harvard, Vancouver, ISO, and other styles

29

Bon, Nguyen Nhan, and Le Van Dai. "Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods." International Journal of Engineering and Technology Innovation 12, no. 2 (2022): 91–109. http://dx.doi.org/10.46604/ijeti.2022.7571.

Full text

Abstract:

This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed metho

APA, Harvard, Vancouver, ISO, and other styles

30

Qian, Xiaoyi, Yuxian Zhang, and Mohammed Gendeel. "State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines." Energies 12, no. 11 (2019): 2046. http://dx.doi.org/10.3390/en12112046.

Full text

Abstract:

Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining

APA, Harvard, Vancouver, ISO, and other styles

31

Sahoo, Anjan Kumar, and Sudhansu Kumar Samal. "Deep learning based detection, classification, and location of power system faults." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4248–59. http://dx.doi.org/10.11591/eei.v13i6.7239.

Full text

Abstract:

The identification, categorization, and localization of faults play a crucial role in maintaining the smooth operation of power systems. Distance relays possess a significant capability to withstand power fluctuations, thereby minimizing inadvertent disruptions in transmission lines. Addressing these challenges involves the adoption of advanced fault analysis techniques to enhance the accuracy and speed of relay operations. While modern machine learning (ML) approaches are still nascent in fault analysis, the authors propose a novel deep learning (DL) based long short term memory (LSTM) method

APA, Harvard, Vancouver, ISO, and other styles

32

Gajjela, Rakesh, and Suresh Babu P. "Dynamic Behaviour Based Wide-Area Back-Up Protection of Transmission Lines." Journal of Emerging Trends in Electrical Engineering 2, no. 2 (2020): 1–17. https://doi.org/10.5281/zenodo.3991324.

Full text

Abstract:

<em>This paper proposes a decentralized approach for identification and classification of faults in transmission lines which uses the dynamic behavior of the generators in the power system. After classification of fault, the location of the fault in the transmission line is calculated. In the proposed method, power system is divided into different protection zones (PZ), each protection zone has a generator whose gain in momentum (GIM) is monitored rigorously. With the help of GIM, Sequence Voltage Magnitudes at buses and reactive power flow through the transmission lines the faulty transmissio

APA, Harvard, Vancouver, ISO, and other styles

33

.., Silvester Bennys, M. Mythily, D. .., and D. Manamalli. "Data Driven Machine Learning For Fault Detection And Classification In Binary Distillation Column." Journal of Cybersecurity and Information Management 11, no. 1 (2023): 47–57. http://dx.doi.org/10.54216/jcim.110105.

Full text

Abstract:

Mathematical programming can express competency concepts in a well-defined mathematical model for a particular Any system that runs is always be expected to experience faults in different ways. Any change in the physical state of numerous components, control machinery, as well as environmental factors, might result in these problems. In process industries, where prompt detection is crucial in maintaining high product quality, dependability, and safety under various operating situations, finding these flaws is one of the most difficult tasks. The goal of this project is to implement several mac

APA, Harvard, Vancouver, ISO, and other styles

34

Makhtar, Siti Noormiza, Umi Syahirah Mohd Sabudin, Muhammad Harith Zaini, Baizura Bohari, Khairol Amali Ahmad, and Kurnianingsih Kurnianingsih. "Propeller Fault Classification for Quadrotor Aerial Vehicle using Spatial Displacement Statistical Features." Journal of Advanced Research in Applied Sciences and Engineering Technology 65, no. 2 (2025): 210–21. https://doi.org/10.37934/araset.65.2.210221.

Full text

Abstract:

Unmanned Aerial Vehicles (UAVs) have become increasingly vital in various applications, from surveillance to logistics. However, ensuring operational reliability is crucial, mainly in detecting potential faults that could compromise flight safety. This study explores spatial displacement data captured through optical sensors as valuable information to translate a quadrotor UAV’s flight behaviour. Incorporating spatial displacement statistical features into a fault classification model can enhance the detection of spatial variations, leading to the identification of abnormalities and potential

APA, Harvard, Vancouver, ISO, and other styles

35

Babayomi, Oluleke O., and Peter O. Oluseyi. "Intelligent Fault Diagnosis in a Power Distribution Network." Advances in Electrical Engineering 2016 (October 19, 2016): 1–10. http://dx.doi.org/10.1155/2016/8651630.

Full text

Abstract:

This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, class

APA, Harvard, Vancouver, ISO, and other styles

36

Zhou, Huawei, Tonghao Mi, Chunju Zhao, et al. "Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis." Applied Sciences 14, no. 11 (2024): 4745. http://dx.doi.org/10.3390/app14114745.

Full text

Abstract:

A concrete production system (CPS) fault in dam engineering is one of the important factors influencing dam construction quality, which may directly affect the concrete-pouring construction progress and construction efficiency of the dam, and can even cause construction quality defects in the dam body. Reasonable classification and identification are of great significance to ensure the construction progress and quality of concrete dams. In this study, based on the concrete production logs of multiple concrete dams and literature reviews, a fault classification system for a CPS is proposed by c

APA, Harvard, Vancouver, ISO, and other styles

37

Tunde, Adejumo Wahid, Emmanuel M. Eronu, and Babawale B. Folajinmi. "Optimized ANN-Based Methodology for Fault Detection and Localization in Power Transmission Networks." Journal of Engineering Research and Reports 27, no. 1 (2025): 140–54. https://doi.org/10.9734/jerr/2025/v27i11374.

Full text

Abstract:

Transmission lines are integral to transporting electrical power from generation sites to consumers. Transmission lines are subject to various faults that disrupt service and threaten system integrity. Fault analysis (identification, classification, and localization) is essential to minimize downtime and operational costs. Improved fault control raises grid dependability, decreases outages, and optimizes operations, promoting renewable integration and cost savings. It enhances safety, power quality, and resilience while facilitating innovative grid modernization and scalability for future dema

APA, Harvard, Vancouver, ISO, and other styles

38

Özdemir, Ömer, Raşit Köker, and Nihat Pamuk. "Fault Classification and Precise Fault Location Detection in 400 kV High-Voltage Power Transmission Lines Using Machine Learning Algorithms." Processes 13, no. 2 (2025): 527. https://doi.org/10.3390/pr13020527.

Full text

Abstract:

Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. The characteristic parameters of mixed-conductor power transmission lines connected to the grid were calculated using the relevant line data. Based on these parameters, a dataset was created with computer-derived values. This dataset included variations in arc resistance an

APA, Harvard, Vancouver, ISO, and other styles

39

Pundir, Meena, Jasminder Kaur Sandhu, Rajwinder Kaur, Gurjinder Singh, and Aina Mehta. "A Systematic Review of Fault Management Framework in Wireless Sensor Networks." ECS Transactions 107, no. 1 (2022): 6473–83. http://dx.doi.org/10.1149/10701.6473ecst.

Full text

Abstract:

Wireless Sensor Networks (WSNs) have progressively appeared as a buzzword for the prevalent research areas of networking in the twenty-first century. The main application areas include threat detection, industrial monitoring, environment monitoring, military surveillance, weather forecasting. Since the sensor nodes are deployed in a hostile environment, they can develop a fault due to this environmental impact. The occurrence of these faults must not hamper the functioning of the entire network. Hence, Fault Management plays a vital role in improving fault tolerance. To improve the Quality of

APA, Harvard, Vancouver, ISO, and other styles

40

Zhou, Huan, Jianyun Chen, Manyuan Ye, Qincui Fu, and Song Li. "Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm." Energies 16, no. 3 (2023): 1163. http://dx.doi.org/10.3390/en16031163.

Full text

Abstract:

This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert–Huang transform and deep learning is propos

APA, Harvard, Vancouver, ISO, and other styles

41

Li, Zhenhua, Junjie Cheng, and A. Abu-Siada. "Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine." (Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, no. 8 (2021): 837–45. http://dx.doi.org/10.2174/2352096514666211026142216.

Full text

Abstract:

Background: Winding deformation is one of the most common faults an operating power transformer experiences over its operational life. Thus, it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA

APA, Harvard, Vancouver, ISO, and other styles

42

Sonu, Kumar Bairwa, and Pratap Singh Satyendra. "Phasor measurement unit application-based fault allocation and fault classification." International Journal of Advances in Applied Sciences (IJAAS) 12, no. 1 (2023): 15–26. https://doi.org/10.11591/ijaas.v12.i1.pp15-26.

Full text

Abstract:

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, positivesequence) bl

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Xiong, Jialu Li, Wenbo Wu, Fan Dong, and Shuting Wan. "Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network." Entropy 25, no. 5 (2023): 737. http://dx.doi.org/10.3390/e25050737.

Full text

Abstract:

At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed. The convolution neural network adopts a simple structure of three-layer convolution. The average pooling layer is used to replace the common max

APA, Harvard, Vancouver, ISO, and other styles

44

A., Naresh kumar, Suresh Kumar M., Ramesha M., Gururaj Bharathi, and Srikanth A. "Support vector machine based fault section identification and fault classification scheme in six phase transmission line." International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 1019–24. https://doi.org/10.11591/ijai.v10.i4.pp1019-1024.

Full text

Abstract:

The higher complexity of a six phase transmission system (SPTS) construction and the large number of possible faults makes the protection task challenging. Moreover, the reverse & forward path faults in SPTS cannot be detected by traditional relay as it becomes under-reach. In this paper, a support vector machine (SVM) method including Haar wavelets for SPTS fault section identification and fault classification is focused. The positive-sequence component phase angle and currents at middle two buses are used to formulate a suggested method. Feasibility of suggested SVM is tested with a 138

APA, Harvard, Vancouver, ISO, and other styles

45

Chen, ZhiQiang, Chuan Li, and René-Vinicio Sanchez. "Gearbox Fault Identification and Classification with Convolutional Neural Networks." Shock and Vibration 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/390134.

Full text

Abstract:

Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time do

APA, Harvard, Vancouver, ISO, and other styles

46

Lv, Shuang Bing. "The Application of Identification of Fault by Well-Seismic Comprehensive Method - Taking Xingbei Oilfield as Example." Advanced Materials Research 962-965 (June 2014): 494–99. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.494.

Full text

Abstract:

For small faults have great influence on remaining oil distribution and injection-production relation in the latter period of high water-cut oilfield, an accurate distribution of these minor faults is one of the main control factors of injection-production adjustment. So, the little fault location predication accurately is necessary. But, research that only rely on logging data description of fault and reservoir characteristics have certain limitation, especially in the fault-points, crosswell faults combination and extension length, plane position and continuity between wells sand body, etc.

APA, Harvard, Vancouver, ISO, and other styles

47

Chen, Yang, Qifu Chen, and Rui Wang. "Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics." Applied Sciences 15, no. 4 (2025): 2240. https://doi.org/10.3390/app15042240.

Full text

Abstract:

Deep learning methods based on neural network models have been widely applied to bearing fault classification. Although they can achieve high accuracy, they also come with significant complexity. Bearing faults often generate impact vibrations, which produce regular fault characteristic peaks on the envelope spectrum. This paper utilizes the differences in frequency and intensity of the envelope spectrum characteristic peaks under different bearing fault conditions as fault features. By combining these features with the simple and efficient Naive Bayes classifier for fault diagnosis, the algor

APA, Harvard, Vancouver, ISO, and other styles

48

Hashmi, Muhammad Baqir, Mohammad Mansouri, Amare Desalegn Fentaye, Shazaib Ahsan, and Konstantinos Kyprianidis. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines." Energies 17, no. 3 (2024): 719. http://dx.doi.org/10.3390/en17030719.

Full text

Abstract:

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors’ knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study,

APA, Harvard, Vancouver, ISO, and other styles

49

Li, Yijin, Jianhua Lin, Geng Niu, Ming Wu, and Xuteng Wei. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids." Energies 14, no. 16 (2021): 5040. http://dx.doi.org/10.3390/en14165040.

Full text

Abstract:

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold

APA, Harvard, Vancouver, ISO, and other styles

50

Hu, Pan, Cunsheng Zhao, Jicheng Huang, and Tingxin Song. "Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN." Processes 11, no. 10 (2023): 2969. http://dx.doi.org/10.3390/pr11102969.

Full text

Abstract:

Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image throu

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!