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Journal articles on the topic 'Fault detection and prediction'

Author: Grafiati

Published: 26 June 2021

Last updated: 29 July 2025

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1

Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.

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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal

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Rizvi, Mohammed. "Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review." Advances in Research 24, no. 5 (2023): 80–88. http://dx.doi.org/10.9734/air/2023/v24i5961.

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Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep le

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S, Swetha, and Dr S. Venkatesh kumar. "Fault Detection and Prediction in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 878–80. http://dx.doi.org/10.31142/ijtsrd18647.

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Wasi, Ullah. "Multiple Fault Detection and Isolation in Target Tracking Using Liner Prediction Techniques." International Journal of Engineering Works (ISSN:2409-2770) 3, no. 11 (2017): 83–86. https://doi.org/10.5281/zenodo.247110.

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This paper proposes schemes for fault detection and isolation in a multi-fault setting. Now-a-days, sensor fault and failure are important issue in various wireless sensor networks. This works suggests a few algorithms based on simple phenomenon of data fusion. Initially, a mutual consensus has been developed among followers (e.g. UAVs in this case) who are tracking the same target. Having known the followers relative positions w.r.t. target, a median is computed by each follower. This median is then shared with neighbours to compare with their estimated values about the target position. If es

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Yang, Hyunsik, and Younghan Kim. "Design and Implementation of Machine Learning-Based Fault Prediction System in Cloud Infrastructure." Electronics 11, no. 22 (2022): 3765. http://dx.doi.org/10.3390/electronics11223765.

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The method for ensuring availability in an existing cloud environment is primarily a metric-based fault detection method. However, the existing fault detection method makes it difficult to configure the environment as the cloud size increases and becomes more complex, and it is necessary to accurately understand the metric in order to use the metric accurately. Furthermore, additional changes are required whenever the monitoring environment changes. In order to solve these problems, various fault detection and prediction methods based on machine learning have recently been proposed. The machin

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Zeng, Aiping, Lei Yan, Yaping Huang, Enming Ren, Tao Liu, and Hui Zhang. "Intelligent Detection of Small Faults Using a Support Vector Machine." Energies 14, no. 19 (2021): 6242. http://dx.doi.org/10.3390/en14196242.

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The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD ca

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Wang, Shizhuang, Xingqun Zhan, Yawei Zhai, and Baoyu Liu. "Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction." Sensors 20, no. 3 (2020): 590. http://dx.doi.org/10.3390/s20030590.

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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and fil

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Kabir Chakraborty, Sanchari De, Tamanna Saha, and Purnima Nama. "Fault location prediction under line-to-ground fault in transmission line using artificial neural network." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 857–66. https://doi.org/10.30574/wjaets.2025.15.2.0552.

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The electrical power system occasionally suffers from failures, often caused by the faults occurring within the system. Accurate fault location prediction is important to ensure the reliable operation of the power system and to minimize the downtime during the occurrence of fault conditions. While traditional methods of fault location detection remain effective for specific scenarios, Artificial Neural Network (ANN) provide a more versatile, efficient, and cost-effective approach to fault location detection. This study focuses on predicting fault positions under line-to-ground (L-G) fault usin

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Laxmi, Dewangan*1 &. Prof. Anish Lazrus2. "A REVIEW ON SOFTWARE PRONE DETECTION AND ITS PREVENTION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 598–603. https://doi.org/10.5281/zenodo.1161695.

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The need of distributed and complex business applications in big business requests error free and quality application frameworks. This makes it critical in programming improvement to create quality and fault free programming. It is likewise critical to outline dependable and simple to keep up as it includes a great deal of human endeavors, cost and time amid programming life cycle. A software advancement process performs different exercises to limit the faults, for example, fault prediction, defect localization, prevention and amendment. This paper shows a study on current practices for progra

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Biddle, Liam, and Saber Fallah. "A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM." Automotive Innovation 4, no. 3 (2021): 301–14. http://dx.doi.org/10.1007/s42154-021-00138-0.

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AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient comp

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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.

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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

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Trivedi, Mihir, Riya Kakkar, Rajesh Gupta, et al. "Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles." Mathematics 10, no. 19 (2022): 3626. http://dx.doi.org/10.3390/math10193626.

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The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of

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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.

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<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

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Zhang, Weirui, Zeru Sun, Dongxu Lv, Yanfei Zuo, Haihui Wang, and Rui Zhang. "A Time Series Prediction-Based Method for Rotating Machinery Detection and Severity Assessment." Aerospace 11, no. 7 (2024): 537. http://dx.doi.org/10.3390/aerospace11070537.

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Monitoring the condition of rotating machinery is critical in aerospace applications like aircraft engines and helicopter rotors. Faults in these components can lead to catastrophic outcomes, making early detection essential. This paper proposes a novel approach using vibration signals and time series prediction methods for fault detection in rotating aerospace machinery. By extracting relevant features from vibration signals and using prediction models, fault severity can be effectively quantified. Our experimental results show that the proposed method has potential in early fault detection a

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Xu, Kaijin, and Xiangjin Song. "A Current Noise Cancellation Method Based on Fractional Linear Prediction for Bearing Fault Detection." Sensors 24, no. 1 (2023): 52. http://dx.doi.org/10.3390/s24010052.

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The stator current in an induction motor contains a large amount of information, which is unrelated to bearing faults. This information is considered as the noise component for the detection of bearing faults. When there is noise information in the current signal, it can affect the detection of motor bearing faults and lead to the possibility of false alarms. Therefore, to accomplish an effective bearing fault detection, all or some of these noise components must be properly eliminated. This paper proposes the use of fractional linear prediction (FLP) as a noise elimination method in bearing f

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Yang, Tengyue, Haiying Wang, and Guorong Ma. "ARMA time series prediction model for fault detection of launch vehicle." Journal of Physics: Conference Series 2764, no. 1 (2024): 012089. http://dx.doi.org/10.1088/1742-6596/2764/1/012089.

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Abstract Spaceflight is a high-risk activity, especially for launch vehicles, and the losses caused by launch failures are considerable. Real-time fault detection is a prerequisite to ensure that future launch vehicles can detect and isolate faults in a timely manner, and ultimately reduce the hazard of fault. In this article, a time series prediction model of axial overload and three-axis angular rate is established using ARMA (Auto Regressive Moving Average) time series prediction model, and the residuals of the prediction model are analyzed and combined with the fault determination decision

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Osborne, Michael, Roman Garnett, Kevin Swersky, and Nando De Freitas. "Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 349–55. http://dx.doi.org/10.1609/aaai.v26i1.8173.

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Many signals of interest are corrupted by faults of anunknown type. We propose an approach that uses Gaus-sian processes and a general “fault bucket” to capturea priori uncharacterised faults, along with an approxi-mate method for marginalising the potential faultinessof all observations. This gives rise to an efficient, flexible algorithm for the detection and automatic correction of faults. Our method is deployed in the domain of water monitoring and management, where it is able to solve several fault detection, correction, and prediction problems. The method works well despite the fact that

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Yang, Huibao, Bangshuai Li, Xiujing Gao, Bo Xiao, and Hongwu Huang. "Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods." Journal of Marine Science and Engineering 13, no. 7 (2025): 1198. https://doi.org/10.3390/jmse13071198.

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In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults,

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Al Qasem, Osama, and Mohammed Akour. "Software Fault Prediction Using Deep Learning Algorithms." International Journal of Open Source Software and Processes 10, no. 4 (2019): 1–19. http://dx.doi.org/10.4018/ijossp.2019100101.

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Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (

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Sivavelu, Sureka, and Venkatesh Palanisamy. "Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1534. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1534-1547.

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Software fault prediction is the significant process of identifying the errors or defects or faults in a software product. But, accurate and timely detection is the major challenging issue in different existing approaches to predicting software defects. A novel Gaussian linear feature embedding-based statistical test piecewise multilayer perceptive deep learning classifier (GLFE-STPMPDLC) is introduced to improve software fault prediction accuracy and minimize time consumption. First, the input data are collected from the dataset. Next, the software metrics selection is carried out to select t

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Sureka, Sivavelu, and Palanisamy Venkatesh. "Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction." Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction 30, no. 3 (2023): 1534–47. https://doi.org/10.11591/ijeecs.v30.i3.pp1534-1547.

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Software fault prediction is the significant process of identifying the errors or defects or faults in a software product. But, accurate and timely detection is the major challenging issue in different existing approaches to predicting software defects. A novel Gaussian linear feature embedding-based statistical test piecewise multilayer perceptive deep learning classifier (GLFESTPMPDLC) is introduced to improve software fault prediction accuracy and minimize time consumption. First, the input data are collected from the dataset. Next, the software metrics selection is carried out to select th

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Shin, Donghoon, Kang-moon Park, and Manbok Park. "Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles." Electronics 9, no. 11 (2020): 1774. http://dx.doi.org/10.3390/electronics9111774.

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This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding veh

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Ma, Jie, and Jianan Xu. "Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/348729.

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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To

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Gaaloul, Yasmine, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, et al. "Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models." Energies 18, no. 10 (2025): 2482. https://doi.org/10.3390/en18102482.

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Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The proposed methodology establishes a predictive baseline model of the system’s healthy behavior under normal operating conditions, enabling real-time detection of deviations between expected and actual performance. Faults such as string disconnect

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Israa, Hussain Musaddak Maher Abdul Zahra Refed Adnan Jaleel. "Improved Image Processing Technique Based Internet of Things and Convolutional Neural Network for Fault Classification of Solar Cells." LC International Journal of STEM (ISSN: 2708-7123) 3, no. 1 (2021): 23–38. https://doi.org/10.5281/zenodo.6547218.

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In the clean, renewable electricity generation, the solar photovoltaic (PV) classification structure became the most appealing. Furthermore, due to varied characteristics and ambient temperature, performance varies. To analyze its performance, a real-time and remote monitoring system is required. The use of the Internet of Things (IoT) in the solar cells classification and in the solar PV systems monitoring is dependent on image processing, and its effectiveness has been investigated. Data gathering, data gateway, and a Constitutional Neural (CNN) model for fault classification prediction in s

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Apeh, Oliver O., and Nnamdi I. Nwulu. "Machine learning predictions for fault detections in solar photovoltaic system: A bibliographic outlook." Journal of Infrastructure, Policy and Development 9, no. 2 (2025): 9940. https://doi.org/10.24294/jipd9940.

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Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic d

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Encalada-Dávila, Á., C. Tutivén, B. Puruncajas, and Y. Vidal. "Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder." Renewable Energy and Power Quality Journal 19 (September 2021): 487–92. http://dx.doi.org/10.24084/repqj19.325.

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Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain

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Yan, Zhaopeng. "Review of Methods for Prediction and Identification of Small Faults." International Journal of Natural Resources and Environmental Studies 2, no. 3 (2024): 53–58. http://dx.doi.org/10.62051/ijnres.v2n3.08.

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Fault system is the oil and gas reservoir is one of the main control factors of characteristic changes especially, for dense reservoirs, so it is very important for quality reservoir prediction. However, due to the complex relationship and contradiction between the genesis and scale problems of small faults and the characteristics and resolution of seismic response, the seismic detection technology of small fault development zones is one of the current research hotspots. Seismic attribute analysis is one of the effective means for small fault identification and prediction. Various types of sei

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Yang, Jun Gang, Jie Zhang, Jian Xiong Yang, and Ying Huang. "A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing." Key Engineering Materials 522 (August 2012): 793–98. http://dx.doi.org/10.4028/www.scientific.net/kem.522.793.

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A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used t

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Lodhi, Ehtisham, Fei-Yue Wang, Gang Xiong, et al. "A Novel Deep Stack-Based Ensemble Learning Approach for Fault Detection and Classification in Photovoltaic Arrays." Remote Sensing 15, no. 5 (2023): 1277. http://dx.doi.org/10.3390/rs15051277.

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The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long s

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Betti, Alessandro, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, and Dimitri Thomopulos. "Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps." Sensors 21, no. 5 (2021): 1687. http://dx.doi.org/10.3390/s21051687.

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In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in pred

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Liu, Jingjing, Chuanyang Liu, Yiquan Wu, Huajie Xu, and Zuo Sun. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images." Energies 14, no. 14 (2021): 4365. http://dx.doi.org/10.3390/en14144365.

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Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “I

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Treetrong, Juggrapong. "Fault Prediction of Induction Motor Based on Time-Frequency Analysis." Applied Mechanics and Materials 52-54 (March 2011): 115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.115.

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Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault sev

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Zhang, Yu, Runcai Huang, and Zhiwei Li. "Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling." Applied Sciences 13, no. 16 (2023): 9276. http://dx.doi.org/10.3390/app13169276.

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Aiming at the problem that existing wind turbine gearbox fault prediction models often find it difficult to distinguish the importance of different data frames and are easily interfered with by non-important and irrelevant signals, thus causing a reduction in fault diagnosis accuracy, a wind turbine gearbox fault prediction model based on the attention-weighted long short-term memory network (AW-LSTM) is proposed. Specifically, the gearbox vibration signal is decomposed by empirical modal decomposition (EMD), to contain seven different frequency components and one residual component. The decom

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Baral, Aditi, Neha Verma, Image Adhikari, Sailesh Chitrakar, and Ole Gunnar Dahlhaug. "Fault Detection in Turbines Using Machine Learning: A study of the capabilities of Various Classification Algorithms." IOP Conference Series: Materials Science and Engineering 1314, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1757-899x/1314/1/012004.

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Abstract This paper presents a comprehensive study of application of different machine learning techniques for the prediction of turbine faults in an early stage. As the need to ensure an optimal turbine operation through early stage fault detection grows, precise predictive models using machine learning techniques are also increasing rapidly. This study primarily focuses on development and evaluation of the predictive models capable of anticipating fault phases using different range of classification algorithms. It assesses the effectiveness of different classification methods including Suppo

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Saied, Majd, Abbas Mishi, Clovis Francis, and Ziad Noun. "A Deep Learning Approach for Fault-Tolerant Data Fusion Applied to UAV Position and Orientation Estimation." Electronics 13, no. 16 (2024): 3342. http://dx.doi.org/10.3390/electronics13163342.

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This work introduces a novel fault-tolerance technique for data fusion in Unmanned Aerial Vehicles (UAVs), designed to address sensor faults through a deep learning-based framework. Unlike traditional methods that rely on hardware redundancy, our approach leverages Long Short-Term Memory (LSTM) networks for state estimation and a moving average (MA) algorithm for fault detection. The novelty of our technique lies in its dual strategy: utilizing LSTMs to analyze residuals and detect errors, while the MA algorithm identifies faulty sensors by monitoring variations in sensor data. This method all

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Karthik Chinnapolamada. "Deep Learning Model for Prediction of Air Mass Deviation Faults." ARAI Journal of Mobility Technology 2, no. 2 (2022): 192–97. http://dx.doi.org/10.37285/ajmt.1.2.4.

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Major Systems of an internal combustion Engine are Air System, Fuel system, and Exhaust system. Any malfunction in these systems increases emissions. OBD legislation mandates to monitor these systems for any faults and appropriate action should be taken in case of the any faults which increase vehicle emissions. The idea of the paper is to find the Air mass flow deviation faults using datamining and machine learning based approach. Detection of fault is classifying whether system is faulty or not. Objective is to create a deep learning model using the available vehicle data to classify the sys

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Xiao, Sa, Jiajie Yao, Yanhu Chen, Dejun Li, Feng Zhang, and Yong Wu. "Fault Detection and Isolation Methods in Subsea Observation Networks." Sensors 20, no. 18 (2020): 5273. http://dx.doi.org/10.3390/s20185273.

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Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and t

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Smith, Stewart, Olesya Zimina, Surender Manral, and Michael Nickel. "Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands." Interpretation 10, no. 2 (2022): SC17—SC30. http://dx.doi.org/10.1190/int-2021-0137.1.

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Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subs

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Prejbeanu, Răzvan Gabriel. "A Sensor-Based System for Fault Detection and Prediction for EV Multi-Level Converters." Sensors 23, no. 9 (2023): 4205. http://dx.doi.org/10.3390/s23094205.

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Power electronic converters and alternating current motors are the actual driving solution applied to electric vehicles (EVs). Multilevel inverters with high performance are modern and the basis for powering and driving EVs. Fault component detection in multilevel power converters requires the use of a smart sensor-based strategy and an optimal fault analysis and prediction method. An innovative method for the detection and prediction of defects in multilevel inverters for EVs is proposed in this article. This method is based on an algorithm able to determine in a fast and efficient way the fa

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Kini, K. Ramakrishna, Fouzi Harrou, Muddu Madakyaru, and Ying Sun. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis." Energies 16, no. 15 (2023): 5793. http://dx.doi.org/10.3390/en16155793.

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Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Speci

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Durga and Dr. Anupa Sinha. "Enhancing Software Reliability through Intelligent Fault Prediction Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 945–56. https://doi.org/10.32628/cseit25113376.

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Software fault prediction plays a crucial role in enhancing software quality and reliability by enabling early detection of defect-prone modules. This study proposes a machine learning-based predictive framework to identify software faults using historical software metrics and defect data. The objective is to evaluate and compare the performance of various machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), and Neural Networks—for their effectiveness in fault detection. Public benchmark datasets comprising object-oriented metrics such as CK, Halstead, and McCabe w

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Tang, Zhanxin, Bangyu Wu, Weihua Wu, and Debo Ma. "Fault Detection via 2.5D Transformer U-Net with Seismic Data Pre-Processing." Remote Sensing 15, no. 4 (2023): 1039. http://dx.doi.org/10.3390/rs15041039.

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Seismic fault structures are important for the detection and exploitation of hydrocarbon resources. Due to their development and popularity in the geophysical community, deep-learning-based fault detection methods have been proposed and achieved SOTA results. Due to the efficiency and benefits of full spatial information extraction, 3D convolutional neural networks (CNNs) are used widely to directly detect faults on seismic data volumes. However, using 3D data for training requires expensive computational resources and can be limited by hardware facilities. Although 2D CNN methods are less com

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Mizuno, Osamu, and Michi Nakai. "Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?" Advances in Software Engineering 2012 (May 10, 2012): 1–8. http://dx.doi.org/10.1155/2012/924923.

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We have proposed a detection method of fault-prone modules based on the spam filtering technique, “Fault-prone filtering.” Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a g

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Ozcan, Mehmet, and Cahit Perkgoz. "Deep learning-based proactive fault detection method for enhanced quadrotor safety." Aviation 28, no. 3 (2024): 175–87. http://dx.doi.org/10.3846/aviation.2024.22173.

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The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencod

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Zhang, Zhiteng, Xiaofang Zhang, Tianhong Yan, Shuang Gao, and Ze Yu. "Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach." Machines 11, no. 5 (2023): 551. http://dx.doi.org/10.3390/machines11050551.

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Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provi

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Wang, Tianhao, Hongying Meng, Rui Qin, Fan Zhang, and Asoke Kumar Nandi. "Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi." Applied Sciences 14, no. 7 (2024): 3129. http://dx.doi.org/10.3390/app14073129.

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Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex

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Liu, Hailang, and Xuanyu Liu. "Electrical fault detection and classification based on multiple machine learning algorithms." Applied and Computational Engineering 74, no. 1 (2024): 245–50. http://dx.doi.org/10.54254/2755-2721/74/20240484.

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Electrical faults in the power system refer to a variety of abnormalities in the power equipment, and these faults may lead to equipment damage or even cause dangerous events such as fires and explosions, which pose a serious threat to people's lives and property. Therefore, it is very important to find and deal with these faults in time. In this paper, based on the electrical fault dataset of power system, SVM, decision tree, KNN and random forest model are used to detect electrical faults. After the confusion matrix results are analyzed, most of the predictions of the four models are correct

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Faizan Ahmad. "Evaluating Fault Tolerance in Distributed Systems using Predictive Analytics with Gated Recurrent Unit and Long Short-Term Memory Models." Journal of Information Systems Engineering and Management 10, no. 27s (2025): 378–99. https://doi.org/10.52783/jisem.v10i27s.4421.

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Fault tolerance is crucial for ensuring reliability in distributed systems, where minor disruptions can cascade into significant failures, causing downtimes, productivity loss, and financial damage. The complexity and interdependencies of distributed systems make them particularly prone to faults. Designing robust fault-tolerant mechanisms is therefore essential to cater the reliability demands of modern systems. Predictive analytics has become a game-changing approach, transitioning from managing faults reactively to detecting and preventing them proactively. This study examines the integrati

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Manu K P. "Embedded system design for fault detection in power distribution networks." World Journal of Advanced Research and Reviews 13, no. 2 (2022): 625–32. https://doi.org/10.30574/wjarr.2022.13.2.0069.

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Power distribution networks are critical for ensuring a stable and uninterrupted supply of electricity. However, faults in these networks can lead to severe disruptions, increased maintenance costs, and potential safety hazards. Rapid and accurate fault detection is essential to minimize downtime, enhance grid reliability, and prevent large-scale power failures. This research paper presents the design and implementation of an embedded system for real-time fault detection in power distribution networks. The proposed system integrates advanced sensing technologies, microcontrollers, and communic

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