Detection Of Such Faults Research Articles

A Simple Magnetic-Flux-Based Method for Localizing High-Resistance Stator Ground Faults in Synchronous Machines

This paper presents a new offline method based on the analysis of the magnetic flux to determine the location of stator ground faults in synchronous machines. First, the defective phase is identified via the insulation resistance (Megger) test, and then the longitudinal axis of the rotor is aligned with the faulty phase winding. During two measuring modes, a voltage is applied separately to each end of the faulty phase while the other end is floating and the core is grounded. The application of the voltage between each terminal of the faulty phase and ground will lead to the circulation of a current through the stator winding to the fault point, being its path closed by the grounded core. A magnetic flux will then be created, which can be sensed indirectly by measuring the induced voltage in the rotor field winding. The fault point can be identified by measuring the applied current or voltage and the magnetic flux created in both measuring modes. Since stator ground faults usually occur via high resistances, the method is extended for the detection of such faults by processing the data using the stationary wavelet transform. The experimental results show that the proposed method is very sensitive and is able to identify the ground fault location for fault resistances up to a few hundred of k-Ohms.

ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection

Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models.

Formal description model and conditions for detecting linked coupling faults of the memory devices

Objectives. The aim of the work is to develop and analyze a formal model for describing complex linked coupling faults of memory devices and to formulate the necessary and sufficient conditions for their detection. The relevance of these studies lies in the fact that modern memory devices, characterized by a large amount of stored data and manufactured according to the latest technological standards, are distinguished by the manifestation of complex types of faults in them.Methods. The presented results are based on the classical theory and practice of march tests (March tests) of memory devices. In particular, the paper uses formal mathematical models for describing memory faults and shows their limitations for representing complex linked coupling faults. The main idea of the approach proposed by the authors is based on the use of a new formal description of such faults, the key element of which is the introduction of roles performed by the cells involved in the fault.Results. Three main roles are defined that cells of the complex linked coupling faults perform, namely the role of the aggressor (A), the role of the victim (V), as well as the role of both the victim and the aggressor (B), performed by two cells simultaneously in relation to each other. It is shown that the scenario for the implementation of the roles of memory failure cells is determined by the marching test used, and, first of all, by the address sequence used to access the cells. The procedure for setting a formal model of a linked fault is given, the basis of which is the roles performed by the cells included in the fault and the scenario specified by the test. A statement is given that determines, on the basis of a new formal description of linked coupling faults, the necessary and sufficient conditions for the detection of such faults. The presence of undetectable linked coupling faults is shown, and the conditions for their detection are formulated using multiple March tests. The conducted experimental studies have confirmed the validity of the formulated provisions of the article. On the basis of the classical example of a linked coupling fault, the fulfillment of necessary and sufficient conditions for its detection by a single march test is shown.Conclusion. The results of the research confirm that the proposed formal mathematical model for describing linked coupling faults makes it possible to determine their detection by marching tests. Within the framework of the proposed model, the necessary and sufficient conditions for detecting linked coupling faults by marching tests that detect single coupled faults are determined.

Fault Detection in Solar Energy Systems: A Deep Learning Approach

While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults. This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from infrared solar modules, was utilized in this study, consisting of 12 classes: cell, cell-multi, cracking, diode, diode-multi, hot spot, hot spot-multi, no-anomaly, offline-module, shadowing, soiling, and vegetation. The methodology employed the exemplar Efficientb0 model. From the exemplar model, 17,000 features were selected using the NCA feature selector. Subsequently, classification was performed using an SVM classifier. The proposed method applied to a dataset consisting of 12 classes has yielded successful results in terms of accuracy, F1-score, precision, and sensitivity metrics. These results indicate average values of 93.93% accuracy, 89.82% F1-score, 91.50% precision, and 88.28% sensitivity, respectively. The proposed method in this study accurately classifies photovoltaic panel defects based on images of infrared solar modules.

Eccentricity Fault Diagnosis in BLDC motor using Finite Element and Frequency Research

The present article examines how rotor, stator and eccentricity faults can be identified in a Brushless DC (BLDC) motor. For a defective BLDCM, a novel finite-element analysis (FEA) index is implemented for non-invasive detection of such faults. The selected index was the elements for amplitude/frequency modulation units with a specific pattern of frequency obtained from the stator current spectrum. The application of this index enables the incidence and type and the proportion of eccentricity to be accurately determined. A finite-element analysis (FEA) method that takes into consideration all the structural and physical features of the motor elements, nonuniform permanent-magnet (PM) features as well as nonuniform air gap permeance, is used to model the BLDC eccentricities. In an attempt to be processed accurately, this model employs tools such as FFT, STFT, Wigner-Ville, Choi Williams analysis and Gabor Transforms to the expected indicators. The suggested index is validated by comparing the simulation and experimentation outcomes of both healthy and faulty machines.

Shipboard Fault Detection Through Nonintrusive Load Monitoring: A Case Study

As crew sizes aboard maritime vessels shrink in efforts to reduce operational costs, ship operators increasingly rely on advanced monitoring systems to ensure proper operation of shipboard equipment. The nonintrusive load monitor (NILM) is an inexpensive, robust, and easy to install system useful for this task. NILMs measure power data at centralized locations in ship electric grids and disaggregate power draws of individual electric loads. This data contains information related to the health of shipboard equipment. We present a NILM-based framework for performing fault detection and isolation, with a particular emphasis on systems employing closed-loop hysteresis control. Such controllers can mask component faults, eventually leading to damaging system failure. The NILM system uses a neural network for load disaggregation and calculates operational metrics related to machinery health. We demonstrate the framework’s effectiveness using data collected from two NILMs installed aboard a U.S. Coast Guard cutter. The NILMs accurately disaggregate loads, and the diagnostic metrics provide easy distinction of several faults in the gray water disposal system. Early detection of such faults prevents costly wear and avoids catastrophic failures.

Sliding Mode Observers-based Fault Detection and Isolation for Wind Turbine-driven Induction Generator

Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.

Optimal Sensor Placement for Fault Diagnosis Using Magnitude Ratio

Major performance losses occur in process industries due to failures that are not identified at the incipient stage. Early detection of such faults is also critical for the safety of the equipment, operating personnel, and other resources. When a fault occurs in a system, it can propagate and affect several process variables. Variables that need to be measured in order to detect and diagnose the faults have to be identified and chosen economically. An algorithmic approach for identifying the optimal number, type, and location of the sensors for fault detection and diagnosis is useful, particularly for large-scale, chemical process plants. In this work, previous algorithms for sensor placement that use signed directed graph (SDG) models for the process are enhanced to include magnitude ratio (MR) information to identify more promising sensor locations. Further, we also study the combination of fault evolution sequences (FES) already introduced in the literature and the MR information for effective fault diagnosis. This is achieved by including the idea of artificial sensors that represent pairwise sensors from the original list of possible sensors. Based on the MR and FES, the artificial sensors can assume discrete values, much like the SDG approach. A symmetric difference operator is used on both the original sensors (whose behaviors are modeled as before using SDG) and the artificial sensors to identify sensor placements. This approach elegantly incorporates the new MR and FES information in the original well-accepted SDG based sensor placement algorithms. Several case studies are presented to demonstrate the usefulness of the proposed approach.

On-line process monitoring using local measures of association. Part II: Design issues and fault diagnosis

Control charts based on partial correlations have proved to be an effective approach to detect fine deviations on the underlying structure of process data. The prompt detection of such faults is dependent on the proper selection of the control chart design parameters, namely their control limits, subgroup size (off-line case) and forgetting factor (on-line case). In this article, specific guidelines are provided to attain the desired detection power while maintaining the intended false alarm rate. A formal relationship that relates the on-line monitoring approach with the simpler off-line implementation is also derived. This relationship can then be used to design on-line control charts based on insights and results obtained with the more interpretable off-line version. A new fault diagnosis procedure is also introduced in order to take advantage of the partial correlations ability to remove the effects of faulty variables in the data, and thus obtain higher identification accuracy and decrease the total time invested in diagnosis activities and troubleshooting.

A parallel algorithm for robust fault detection in semiconductor manufacturing processes

The semiconductor manufacturing consists of a number of processes, and even a small fault occurring at any point can damage the product quality. The fast and accurate detection of such faults is essential to maintain high manufacturing yields. In this paper, we propose a parallel algorithm for fault detection in semiconductor manufacturing processes. The algorithm is a modification of the discord detection algorithm called HOT SAX, which adopted the SAX representation of time-series for efficient storage and computation. We first propose a sequential algorithm and then extend it to a parallel version. We evaluate our algorithm through experiments using the data obtained from a real-world semiconductor plasma etching process. As a result, our fault detection algorithm achieved 100 % accuracy without any false positive or false negative.

Analysis of Field Data on Web Security Vulnerabilities

Most web applications have critical bugs (faults) affecting their security, which makes them vulnerable to attacks by hackers and organized crime. To prevent these security problems from occurring it is of utmost importance to understand the typical software faults. This paper contributes to this body of knowledge by presenting a field study on two of the most widely spread and critical web application vulnerabilities: SQL Injection and XSS. It analyzes the source code of security patches of widely used web applications written in weak and strong typed languages. Results show that only a small subset of software fault types, affecting a restricted collection of statements, is related to security. To understand how these vulnerabilities are really exploited by hackers, this paper also presents an analysis of the source code of the scripts used to attack them. The outcomes of this study can be used to train software developers and code inspectors in the detection of such faults and are also the foundation for the research of realistic vulnerability and attack injectors that can be used to assess security mechanisms, such as intrusion detection systems, vulnerability scanners, and static code analyzers.

Detection of interturn faults in PMSMs with different winding configurations

Interturn faults in permanent magnet synchronous motors (PMSMs) may develop fast into more severe faults such as coil-to-coil, phase-to-phase and phase-to-ground short circuits. These faults are very destructive and may irreversibly damage the PMSM. Therefore, it is highly desirable to develop suitable methods for the early detection of such faults. The effects of interturn faults are visible in both the stator currents and the zero-sequence voltage component (ZSVC) spectra. By designing appropriate fault diagnosis schemes based on the analysis of the harmonic content of such electric variables it is possible to detect short circuit faults in its early stage. However, the stator winding configuration of the PMSM deeply impacts the harmonic content of both spectra. This paper studies the effects of different stator winding configurations in both the stator currents and the ZSVC spectra of healthy and faulty machines. Results presented may help to develop fault diagnosis schemes based on the acquisition and further analysis of the stator currents and/or the ZSVC harmonic components.

Diagnosis of Interturn Faults in PMSMs Operating Under Nonstationary Conditions by Applying Order Tracking Filtering

Interturn faults in permanent magnet synchronous machines may have very harmful effects if not early identified. This study deals with the detection of such faults when the machine operates under varying speed conditions. The performance of two methods is analyzed and compared, i.e., the analysis of the third harmonic of the stator currents and the first one of the zero-sequence voltage components. The Vold-Kalman filtering order tracking algorithm is introduced and applied to track the harmonics of interest when the machine operates under a wide speed range and different load levels. This study also presents two reliable fault indicators especially focused to detect stator winding interturn faults under nonstationary speed conditions. Experimental results endorse the methodology proposed, showing its potential to carry out a reliable fault diagnosis scheme.

A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram

The narrowband amplitude demodulation of a vibration signal enables the extraction of components carrying information about rotating machine faults. However, the quality of the demodulated signal depends on the frequency band selected for the demodulation. The spectral kurtosis (SK) was proved to be a very efficient method for detection of such faults, including defective rolling element bearings and gears [1]. Although there are conditions, under which SK yields valid results, there are also cases, when it fails, e.g. in the presence of a relatively strong, non-Gaussian noise containing high peaks or for a relatively high repetition rate of fault impulses. In this paper, a novel method for selection of the optimal frequency band, which attempts to overcome the aforementioned drawbacks, is presented. Subsequently, a new tool for presentation of results of the method, called the Protrugram, is proposed. The method is based on the kurtosis of the envelope spectrum amplitudes of the demodulated signal, rather than on the kurtosis of the filtered time signal. The advantage of the method is the ability to detect transients with smaller signal-to-noise ratio comparing to the SK-based Fast Kurtogram. The application of the proposed method is validated on simulated and real data, including a test rig, a simulated signal, and a jet engine vibration signal.

Motor shaft misalignment detection using multiscale entropy with wavelet denoising

Misalignment of motor shaft (also manifesting as static eccentricity) is a common motor fault resulting from improper installation or damage of the machine components and their support structure. Spectrum analysis is generally used for online detection of such faults. This study presents a novel approach to discover features that distinguish the vibration signals of a normal motor from those of a misaligned one. These features are obtained from the difference of multiscale entropy of a signal, before and after the signal is denoised using wavelet transform. Experimental results show that classifiers based on these features obtain better and more stable accuracy rates than those based on frequency-related features.

A representation for coordination fault detection in large-scale multi-agent systems

Teamwork requires that team members coordinate their actions. The representation of the coordination is a key requirement since it influences the complexity and flexibility of reasoning team-members. One aspect of this requirement is detecting coordination faults as a result of intermittent failures of sensors, communication failures, etc. Detection of such faults, based on observations of the behavior of agents, is of prime importance. Though different solutions have been presented thus far, none has presented a comprehensive and efficient resolution for large-scale teams. This paper presents a formal approach to representing multi-agent coordination, and multi-agent observations, using matrix structures. This representation facilitates easy representation of coordination requirements, modularity, flexibility and reuse of existing systems. Based on this representation, we present a novel solution for fault-detection that is both generic and efficient for large-scale teams. We demonstrate the modularity of the representation by presenting a reuse of existing systems and by importing other models (e.g. hierarchical systems) into the new representation. Finally, we extend the representation to support dynamical aspects of complex systems.

Quality evaluation of pseudorandom patterns of a logic BIST

AbstractFaults based on complex logic phenomena such as bridges, opens, and delays are increasing as microfabrication progresses and chips speeds increase. However, the capacity for detecting such faults with merely a test set, which has the goal of reducing the number of test patterns based on conventional single stuck‐at fault models, is not sufficient. This paper offers a multifaceted evaluation of the fault detection capacity of a logic BIST using pseudorandom patterns, based on the idea that the detection of such faults will be possible through the generation of combinations of diverse logic values with many test patterns. Through fault simulation for several fault models and the evaluation of a logic BIST using real devices, it is shown that it is possible to detect faults which could not be detected with a conventional scan test set. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 89(12): 63–70, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20323

KALMAN FILTERING AND TORQUE SPECTRAL ANALYSIS FOR BROKEN BAR DETECTION IN INDUCTION MOTORS

Rotor asymmetries in induction machines perturb many components such as, flux patterns and electromagnetic torque. The supervision of these signals enables early detection of such faults and help to machine diagnostic. This paper studies the detection of rotor imperfection by spectral analysis of the electromagnetic torque computed by two rotor flux estimators. In the first approach, the Kalman filter is used assuming to be known the mechanical velocity. The second approach uses Extended Kalman Filter (EKF) for speed estimation. Experimental results show the great capability of these methods to detect this type of faults.

Concurrent checking for VLSI

This paper presents various concurrent checking techniques for VLSI, including self-checking circuits and concurent checking techniques for temporary faults based on time redundancy. It cautions that emerging technological constraints and application requirements will expend dramatically the use of these techniques. In particular, various industrial sectors (e.g. railway control, satellites, avionics, telecommunications, control of critical automotive functions, medical electronics, industrial control, etc), have increasing needs for concurrent checking features. Some of these applications concern mass production and should support the standardization of such techniques and the development of commercial CAD tools supporting them. Furthermore, drastic device shrinking and increasing operating speeds that accompany the technological evolution to deeper submicron, reduces significantly the noise margins and increases dramatically the impact of transient faults. In addition various spurious faults are becoming predominant and require very complex test conditions. Under such conditions, test pattern generation complexity and test length make impossible the detection of such faults. As a consequence, technological progress will be blocked quickly if no particular actions are undertaken to cope with increasingly high soft-error rates and undetected spurious faults resulting on timing errors. The paper will discuss these emerging requirements and problems and describe how concurrent checking can be used to cope with.

Production evaluation of real-time statistical process control on a sub-0.5 μm inductively coupled metal etch process

The semiconductor manufacturing industry is demanding improved algorithms for the detection and isolation of equipment faults. The real-time statistical process control (RTSPC) algorithm analyzes real-time sensor and actuator data for the detection of such faults. The software was evaluated using SECS-II (Serial Equipment Communication Standard) data from several commercial plasma etchers. These data were used to build statistical models which were then applied to data recorded during subsequent wafer processing. RTSPC was able to detect several faults related to failure of rf matching networks. For one of the rf match failures, the RTSPC alarm preceded the component failure by several weeks. This alarm was correlated to a shift in the critical dimension of wafers processed in this etcher. The statistical data filters included within RTSPC are discussed, as well as the characterization of the real-time data. Emphasis is placed on whether uni-variate, multivariate, or time-varying statistics are required to detect the faults seen on the etchers, and additional algorithms which would enhance the fault detection capability.