Different Types Of Faults Research Articles

Overview of Fault Zones Based on Remote Sensing Data as Contribution to the Safety of Infrastructure and Land Use in Southern Egypt

Part of a requisite for natural hazard awareness and damage prevention in Egypt is the detailed monitoring of surface fault zones. The inventory of active faults and their risk assessment is an essential contribution to the safety of settlements, land use and infrastructure (railroads, highways, pipelines) and to damage prevention. Thus, this study aims to contribute to the detection, inventory, and documentation of fault zones. Surface faulting hazard assessment considers any surface consequences caused by surface near faults such as abrupt horizontal and vertical displacements and rotations, or mass movements after stronger earthquakes. Long-term, aseismic, slow-creeping movements along fault zones have to be considered as well. Southern Egypt offers unique and optimal conditions for the research of fault zones and their different structural expressions and conditions because of the dry desert climate conditions and relatively low human influence on the landscape in extended areas, especially for the investigation of different types of faults, their interactions with each other and the outcropping rocks, and fault-related deformation structures. Volcanic activity has been influenced in the geologic past often by larger fault zones as dike intrusions, volcanic cones and plugs or craters occur concentrated along these zones of weakness facilitating the uprise of magma. Larger fault zones are crossing reservoir areas. Surface water intrusions into deep-seated faults have played a role in triggering earthquake swarms in the reservoir areas during the last decades. Mapping of active faults is not only an important but also a cost-intense task when carried out in the field or when using geodetic and geophysical data. In the scope of this study, fault detection was carried out based on different open-source satellite data (Landsat 8 and 9, Sentinel 2 optical data, Sentinel 1 and ALOSPASAR radar data, and Google Earth and Bing Map high-resolution satellite images) from the southern part of Egypt. Faults were digitized using ArcGIS and QGIS software. An inventory of fault-related structural features (depressions, ridges, rotation structures, etc.) and rift zones was elaborated based on remote sensing data. An overview of different types of faults and their related structures as well as their interactions with their host rock conditions could be achieved. By merging the inventory results with infrastructural and land use data, critical areas with potential damage risk were pointed out.

Development of an Operational Digital Twin of a Freight Car Braking System for Fault Diagnosis

AbstractIn the evolving landscape of digital technology, the concept of digital twinning has emerged as one of the most important innovations. Digital twinning, characterized by a one‐to‐one correlation with physical structures in a digital environment, has become an important tool in transferring physical dynamics into digital replicas. This platform, integrating advanced sensor technologies and analytical tools, enables a comprehensive monitoring, diagnosis, and optimization concept. The present research focuses on the application of digital twinning in addressing the complex challenge of managing fault types in freight car brakes. A Digital Twin (DT) model of a freight car braking system is developed that forms the basis for an innovative approach to fault classification. This approach leverages the ability of the DT to emulate and analyze the behavior of the braking system under different conditions. The effectiveness of the proposed method is evaluated through extensive testing and analysis with an experimental data set. The results show that the method is able to accurately diagnose different types of faults. This represents a significant advance in the field of freight car brake system diagnosis.

Gearbox faults severity classification using Poincaré plots of acoustic emission signals

Classification of fault severity in gearboxes using Acoustic Emission (AE) signals is challenging because such signals represent a highly non-linear and possibly chaotic system. Due to the common assumption of linearity, the statistical features extracted from these systems are suboptimal for the classification of fault severity. Hence, this paper uses the Poincaré plot (PP) of Acoustic Emission (AE) signals to extract useful features to classify fault type and severity in gearboxes. For this development, four fault types were applied over different gears and then tested on an experimental condition monitoring bench: broken tooth, pitting, scuffing, and cracks, each with nine severity levels. Then, the feature set was extracted from the conventional 2-D PP, composed of shape-related features and a set of features known as complex correlation measurements (CCM). The fault type and severity classification was performed using four frequency bands. Low and band-pass filtered signals obtained the highest classification accuracy with Random Forest (RF): the fault type was classified with an accuracy of 99.69%, the severity was classified with an accuracy depending on the fault type corresponding to pitting 98.76%, cracks 98.71%, broken tooth 98.96%, and scuffing 98.51%. The PP features set has a low computational cost even for large datasets representing AE signals, which can benefit the practical possibility of the implementation with high classification accuracy for different types of fault and severity levels in a gearbox.

Development of a Fault Detection and Localization Model for a Water Distribution Network

Water distribution networks are complex systems that aid in the delivery of water to residential and non-residential areas. However, the networks can be affected by different types of faults, which could lead to the wastage of treated water. As such, there is a need to develop a reliable leakage detection and localization system that can detect leak occurrences in the network. This study, using a simulated dataset from EPANET, presents the application of supervised machine learning classifiers for leak detection and localization in the water distribution network of the University of Port Harcourt Choba campus. The study compared three machine learning classification tools that are used in pattern recognition analysis: the support vector machine, k-nearest neighbor, and artificial neural network. The robustness and effectiveness of the proposed approach are compared with those of the performance of the classifiers for leakage detection in the network of the case study. The results show that the support vector machine performs the best, with 79% accuracy, while the respective accuracies for the remaining classifiers are 70% for the k-nearest neighbor and 61% for the artificial neural networks. The high accuracy demonstrated by the models shows that they are able to detect and address issues relating to fault detection in a water distribution network. This model could provide a leakage detection system to be applied to buildings for the efficient management of water in their networks.

A Secure Dual-Layer Fault Protection Strategy for Distribution Network with DERs: Enhancing Security in the Face of Communication Challenges.

Earlier protection methods mainly focused on using communication channels to transmit trip signals between the protective devices (PDs), with no solutions provided in the case of communication failure. Therefore, this paper introduces a dual-layer protection system to ensure secure protection against fault events in the Distribution Systems (DSs), particularly in light of communication failures. The initial layer uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence grid voltage components, functioning as a fault sensor, to formulate an adaptive algorithm based on a Finite State Machine (FSM) for the detection and isolation of faults within the grid. This layer primarily relies on communication protocols for effective coordination. A Second-Order Generalized Integrator (SOGI) expedites the derivation of the estimated variables, ensuring fast detection with minimal computational overhead. The second layer uses the behavior of the positive- and negative-sequence components of the grid voltages during fault events to locate and isolate these faults. In the event that the first layer exposes a communication failure, the second layer will automatically be activated to ensure secure protection as it operates, using the local information of the Protective devices (PDs), without the need for communication channels to transmit trip signals between the PDs. The proposed protection system has been assessed using simulations with MATLAB/Simulink and providing experimental results considering an IEEE 9-bus standard radial system. The obtained results confirm the capability of the system for identifying and isolating different types of faults, varying conditions, and modifications to the grid configuration. The results show good behavior of the initial THD-based layer, with fast time responses ranging from 6 to 8.5 ms in all the examined scenarios. In contrast, the sequence-based layer exhibits a protection time response of approximately 150 ms, making it a viable backup option in the event of a communication failure.

A Protection Principle of LCC–VSC Three-Terminal HVdc System Based on Instantaneous Boundary Impedance

In the field of hybrid multi-terminal HVdc (high voltage direct current) transmission, an inevitable problem is the short availability of fault information. Namely, only 1-ms complete fault information can be used to identify faults. To overcome this limitation, this study introduces the concept of instantaneous boundary impedance and proposes a dc line protection method. First, a dc transmission line is divided into three types of zones, namely internal, forward-external, and backward-external zones. Then, the instantaneous boundary impedance is analyzed and compared under normal operation and different types of faults. The results indicate that under normal operation, the instantaneous boundary impedance is zero; under a dc line fault, the instantaneous boundary impedance is less than zero; finally, under an external fault, the instantaneous boundary impedance is greater than zero. The proposed principle is validated using Wudongde LCC (line-commuted converter)-VSC (voltage source converter) three-terminal hybrid HVdc RTDS (real-time digital simulation) test system. The experimental results demonstrate that the proposed protection method is effective under different types, locations, and impedances of faults and signal-to-noise ratios within a period of 1 ms. Compared to the existing methods, the proposed protection method can achieve better performance in withstanding fault impedance (600 Ω) and has better robustness against noise interference (10 dB).

Distributed Generation Stability During Fault Conditions

The difficulties facing electric utility companies to satisfy on-site customer power demand aroused an increasing interest in small distributed generators (DG). Also the environmental concerns, the technology evolution and the need to integrate renewable energy resources into distribution system gave way to the construction of (DG) using alternative energy sources. The presence of such new generators creates new operating conditions for the interconnected system. The aim of the paper is to study the impacts of faults on stability in systems with (DG) and their satiability during faults. In this paper, the impact of different types of faults at various locations on a distribution system with and without the presence of (DG) is studied. The IEEE 13 node distribution test feeder is used as a model for the study together with synchronous generator (DG). The simulation of the distribution system and the (DG) is performed using PSCAD software. A comparison between fault currents with and without the insertion of the DG is carried. The effect of such faults on the stability of the used DG is considered taking into account the control actions expected for the system to remain within the stability limit.

Influence of fault impedance on voltage quality in radial distribution systems with distributed synchronous generator: a sensitivity analysis

The power quality delivered to final consumers has deserved special attention on the part of the scientific community due to the increasing deterioration of voltage and current waveforms as a result of use of non-linear loads. Deployment of Distributed Generation Power is expected to have a range of effects (positive or negative) on the distribution grid. This paper presents a sensitivity analysis of voltage quality of a radial distribution system located in Guarulhos, Brazil, in a presence of a distributed synchronous generator in one of its bars. The sensitive analysis is done by varying the resistive fault impedance by considering the occurrence of different types of faults due to atmospheric discharging. The system is modelled and simulated in time domain through ATP software. It was verified that the impacts caused by the symmetrical and asymmetric faults on voltage levels are attenuated as the resistive fault impedance increases.

Design Requirements for a Dynamic Series Compensator for Voltage Sags Mitigation in Low Voltage Distribution System

:Power quality issues have become an increasing concern due to an increase of sensitive loads in distribution system. A dynamic series compensator or Dynamic Voltage Restorer (DVR) is a custom device that can be used to protect sensitive loads from various power quality problems from disturbed incoming supply. The dynamic voltage restorer (DVR) has become popular as a cost effective solution for the protection of sensitive loads from voltage sags. This paper highlights a series of discussion, analysis and studies performed on the active power injection requirements for a DVR under various system and load conditions, as well as for different types of fault in both single phase and three-phase fault. The analysis and experimental results validate the effectiveness of the DVR for mitigation voltage sag in low voltage distribution system.

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder

In practical engineering, data often lack labels, resulting in difficulty in fault diagnosis. Because stack-denoising autoencoders possess robust feature extraction capabilities and resistance to interference, an automatic and unsupervised bearing fault diagnosis method based on the stack-denoising autoencoder without an output layer was proposed in this study. As the stacked denoising autoencoder is an unsupervised algorithm, this approach can reduce reliance on manually labeled data labels. Therefore, this study proposed a new method for automatic fault diagnosis. First, the bearing fault features of the rolling bearing were extracted using the stack denoising autoencoder without an output layer. Meanwhile, the dimensions of the features were directly reduced to two or three dimensions by several hidden layers, thereby reducing manual experience. Second, the labels extracted from the clustering model were selected as inputs for different classifier models to automatically identify different types of faults. Two open-source rolling bearing datasets under various conditions were used to validate the classification performance of the proposed method. Finally, its effectiveness was verified using the experimental results. Various indicators were used to evaluate the performance of the proposed method, and the results showed an automatic bearing fault diagnosis accuracy of up to 90% when using different models and working conditions. Among the two datasets, the classification model achieved the highest accuracies of 0.99667 and 0.97143 and the lowest accuracies of 0.98000 and 0.90476, respectively.

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

Nowadays, wind turbine fault detection strategies aresettled as a meaningful pipeline to achieve required levels of effi-ciency, availability, and reliability, considering there is an increasinginstallation of this kind of machinery, both in onshore and offshoreconfiguration. In this work, it has been applied a strategy that makesuse of SCADA data with an increased sampling rate. The employedwind 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 bycertain installed sensors have been studied. The proposed strategy isbased on a normality model by means of an autoencoder. As of this,faulty data are used for testing from which prediction errors werecomputed to detect if those raise a fault alert according to a definedmetric which establishes a threshold on which a wind turbine workssecurely. The obtained results determine that the proposed strategyis successful since the model detects the considered three types offaults. Finally, even when prediction errors are small, the model isable to detect the faults without problems.

Fault diagnosis technology of CNC electromechanical system in mechanical engineering equipment manufacturing under structural coupling

Abstract This study addresses the fault diagnosis technology of CNC electromechanical systems in mechanical engineering equipment manufacturing, and explores the fault detection methods under the influence of structural coupling to improve the accuracy and efficiency of fault diagnosis. The study first analyzes the time-domain and frequency-domain features for fault diagnosis, including quantitative and dimensionless features used to identify different types of faults. Subsequently, the study explores feature dimensionality reduction methods, including algorithms such as PCA, LLE and t-SNE, and compares the effectiveness of their application in fault diagnosis. The research focuses on proposing a lightweight deep learning fault diagnosis framework called LTCN-BLS, which combines 2-DLTCN and 1-DLTCN branches, and an ILAEN-based BLS classifier to effectively extract and fuse time-domain and time-frequency-domain features of the data. The experimental results show that the LTCN-BLS framework has high accuracy and low network complexity in fault diagnosis, and has obvious advantages in early fault monitoring, degradation assessment, and robustness compared with traditional methods.

Research on Integrated Layer Determination and Fault Crossing Technology of Multi-branch Horizontal Wells for Surface Area Management

Based on the application of multi-branch horizontal well technology in surface area management, combined with the hydrogeological structure of the coal bed floor, the method of determining the layer by azimuthal gamma, rock chip logging and drilling parameters is proposed. Combined with the change of azimuthal gamma, determine whether drilling encounters faults or not, put forward the technology of crossing faults when drilling encounters different types of faults, improve the rate of drilling smooth layer, reduce the leakage of drilling fluids, and form a set of key technologies of comprehensive stratigraphy judgment and crossing faults applicable to the grouting of multi-branch horizontal wells at the surface, based on the existing engineering examples, analyze the stratigraphy, tectonic conditions, and the application of comprehensive stratigraphy judgment and crossing faults technology in the case of directional drilling. The average smooth layer rate of Z1 and Z2 hole group is 96.96% and 99.2% respectively, which effectively ensures the quality of grouting in the fault structure area and is of great significance to the prevention and control of water damage in the mine.

Fault Detection and Isolation in Smart-Grid Networks of Intelligent Power Routers Modeled as Probabilistic Boolean Networks

A self-organizing complex-network modeling method, probabilistic Boolean networks, is presented as a model-based diagnostic system for detecting and isolating different types of faults, failures, and modes of operation in which a network of intelligent power routers is deployed over a standard power test case: the Western System Coordinating Council 9 Bus System. Such a system allows designers and engineering professionals to make educated decisions pertaining to the design of smart-grid systems endowed with intelligent power routers. There is a recurrent necessity to design reliable and fault-tolerant smart power systems, maintaining adequate operation and adherence to performance specifications, while keeping costs at the minimum. This diagnostics system will help achieve such goals: better design through thorough analysis of the conditions that lead to a fault on a smart grid, proper detection of these faults, and isolation of the respective assets.

Model analysis and control of biped dynamic walker with fault steps in a gait cycle

The biped robot has two identical legs that undergo alternating stance and swing phases during walking. The robot’s motion is facilitated by actuators positioned at the ankle and hip joints. The paper investigates various fault scenarios in dynamic walker models, specifically situations where one or both actuators experience failure while the robot is in motion. The first model assumes faults in both the hip and ankle joints, while the second model assumes a fault only in the ankle joint. The dynamic walker typically undergoes two steps in each cycle. This study specifically examines the different types of faults that may occur during the second step. The goal is to identify the potential walking gaits that can result from these faults. It is observed that walking with faults at both the hip and ankle joints exhibited greater stability and a wider range of stable states compared to walking with a fault only at the ankle joint. This suggests that incorporating hip joint faults or underactuation into robotic or prosthetic gait patterns could improve their stability and overall performance. The local and global stability of the dynamic walker were analyzed using Poincare’s method and the basin of attraction plot, respectively. Additionally, a control algorithm based on kinetic energy shaping is proposed for the second model, which resulted in an increased basin of attraction. Notably, the second model exhibited the ability to walk with variable step length and speed, akin to human locomotion.

Research on the fault diagnosis method of AC power supply in substations based on a synchronous digital sampling technology

In order to improve the accuracy and efficiency of AC power supply fault diagnosis in substations, a fault diagnosis method of AC power supply in substations based on synchronous digital sampling technology is proposed and designed in this study. Based on the IEC 61850-9-2 communication protocol, the same sampling of the status data of the substation AC power supply is realized first. Then, in order to improve the fault diagnosis effect, the data is pre-processed such as de-noising and de-redundancy. Finally, the substation AC power supply fault diagnosis is carried out based on Dempster/Shafer (D-S). The experimental results show that for different types of faults, the diagnosis accuracy of the proposed method is higher than 95.5%, and the diagnosis time is less than 1.31 ms.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to broken bar fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to stator winding fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.