Search

In this paper, a hybrid system is proposed to predict and classifies the power system transmission line faults. The proposed technique is the consolidation of both the truncated singular value decomposition (TSVD) and Human urbanization algorithm (HUA) based Recurrent Perceptron Neural Network (RPNN), and hence it is named as TSVD-HUARPNN technique. TSVD is matrix decomposition, this technique qualify the outcome it as fast or not. In the proposed work, the qualification of the results from the TSVD is improved by a lemma theorem; it is a proven proposition which is used to obtain a larger and optimal result. For that reason, it is also known as a "helping theorem" or an "auxiliary theorem". Here, it has two modules for power system fault analysis: (i) fault detection, (ii) fault classification. The first process of the proposed system is the generation of the dataset of normal and abnormal conditions of transmission line parameters of power system using TSVD. The extracted dataset is assessed by HUA-based RPNN system to classify the fault analysis that occurs in transmission system. The TSVD-HUARPNN system is used to predict and classify the fault present in the transmission line. The proposed TSVD-HUARPNN system ensures the system with less complexity for the detection and classification of the fault, therefore the accuracy of the system is increased. By then, the proposed model is activated in MATLAB/Simulink, its performance is evaluated with the existing models. The performance with noise at 20 dB of the proposed technique is 99.77%. • Hybrid technique is proposed for predicts, classifies the power system transmission line faults. • The proposed technique is the consolidation of both TSVD and HUA based RPNN. • TSVD prepare dataset based on transmission line parameters normal and abnormal condition. • The extracted dataset is assessed by the HUA based RPNN technique. • The proposed technique guarantees the system with lessens complexity. [ABSTRACT FROM AUTHOR]

This study aims to display fuzzy logic (FL) technique for diagnosis of fault induction machine. This allows monitoring of fuzzy information from different signals to give more accurate judgment on the health of the engine, through using a multi-winding model of induction machine for the simulation of broken bars. This model allows study the influence of defects and appear the behavior of the machine in the different modes of running conditions (healthy and fault). In this work, we focus the application of a fuzzy logic technique based on the fast Fourier transformation (FFT) by analyzing the stator current for fault detection. The results of the simulation obtained allowed us to show the importance of the fuzzy logic approach based on classification of signals for detecting the faulty. [ABSTRACT FROM AUTHOR]

The polyphase machine provides increased reliability by enabling operation with one or more phases in fault, and the most common faults are stator fault, it's for that a new analysis model of stator open phase faults in a five-phase induction motor has been presented in this paper. A new modeling of five phase induction machine in natural frame is elaborated, and the performance of five phase induction machine was analyzed under stator open phase using the principal component analysis (PCA) method. The PCA is a tool to reduce multidimensional data to lower dimensions while retaining most of the information, where the effect of the fault conditions caused by open phase on electromagnetic torque, mechanical speed, stator current and efficiency of this motor has been investigated. The findings of this research may be useful and help researchers who are interested on the fault tolerant control to minimize or eliminate this effect. [ABSTRACT FROM AUTHOR]

This study offers the opportunity to extend the functioning of the most advanced protection systems. The faults which can arise on the power transmission lines are numerous and varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the conventional sensor as the Mho distance relay also known as the admittance relay is generally used. This relay will be discussed later in this study. By taking into account the preventive risks of the Mho relay and discover the new techniques of artificial intelligence, namely the neural network which can contribute to the precise and rapid detection of all types of short-circuit faults. The results of the simulation tests demonstrate the effectiveness of the methods proposed for the automatic diagnosis of faults. [ABSTRACT FROM AUTHOR]

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy. [ABSTRACT FROM AUTHOR]

Condition and health monitoring of electrical machines during dynamic loading is a common, yet challenging problem in main battle tanks. Existing methods address this issue by extracting various features which are subsequently used in a classifier to isolate faults. However, this approach relies on the feature set being extracted and therefore most of the time does not provide expected accuracy in identification of faults. In this work, we have used convolution neural network that utilises the original time domain measurements for fault detection and isolation (FDI). Results from experimental studies indicate that the proposed approach can perform FDI with more than 95\% accuracy using commonly available current measurements. [ABSTRACT FROM AUTHOR]

During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G-P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers. [ABSTRACT FROM AUTHOR]

Qualitative and/or semi-quantitative risks analysis measures are not able to model and assess the effects of two or more simultaneous faults or the benefits which redundancy may bring to the safety system. One of their shortcomings is the incapacity of being used efficiently for low frequency modelling and predication of events with severe consequences. By approaching the scientific study upon human reliability along with technical and performance data are ensured the premises for developing an innovative tool for dynamic modelling the safety of a complex socio-technical system. [ABSTRACT FROM AUTHOR]

Abstract This research studies the problem of fault detection observer design for two-dimensional (2-D) continuous-time nonlinear systems in Takagi–Sugeno (T–S) form. Finite frequency (FF) specifications are used to design the observers, which makes observer designing different from previously proposed 2-D detection observers. Faults and disturbances are considered to be dominated in two different FF domain intervals. Fault sensitivity and disturbance robustness are measured by two FF performance indices, respectively. The aim of this paper is to design fault detection observers such that the residual error system has the sensitivity to faults and the desired robustness to disturbances. Sufficient conditions for the existence of a desired fault detection fuzzy observer are established in terms of linear matrix inequalities (LMIs). Simulation results indicate that faults can be detected effectively using the proposed method. Highlights • H − and H ∞ , finite frequency (FF) performance indices are firstly defined to measure the sensitivity against fault and the disturbance robustness, which contain the frequency information of fault and disturbance inputs and cover the FF H − and H ∞ indices defined in Wang et al. (2015) for linear 2-D continuous systems as special cases. • Based on the defined FF H − and H ∞ performance indices, sufficient conditions for the existence of fault detection observers are developed to ensure that the residual error system is stable and has desired FF H − and H ∞ performance levels. • A systematic fault detection scheme is proposed by converting the corresponding fault detection observer design into a convex optimization problem via introducing some auxiliary fixed matrix variables. [ABSTRACT FROM AUTHOR]

Despite their reliability, induction motors tend to fail. Around 41% of faults in motors are bearing related and that is the most common fault in motor field. Due to the lack of research on generalized roughness bearing fault diagnostics by use of a stator current spectrum, the presented study analyses both single-point and generalized roughness bearing faults and their classification possibilities. In this paper, a new method for generalized roughness ball bearing fault identification by use of a stator current signal analysis is presented. The algorithm relies on DiscreteWavelet Transform and Welch's spectral density analysis. The composition of both methods is used for building a feature vector for the classifier. In order to achieve classification, support vector machine classifier with linear kernel function has been applied. The validation experiment and results are presented. [ABSTRACT FROM AUTHOR]

Typically, software development processes are time consuming, expensive, and rigorous, particularly for safety-critical applications. Even if guidelines and recommendations are defined by sector-specific functional safety standards, development process may not be completed because of excessive costs or insufficient planning. The V-model is one of the most well-known software development lifecycle model. In this study, the V-model lifecycle is modified by adding an intermediate step. The proposed modification is realized by checking the fault diagnosability of each module. The proposed modification provides three advantages: (1) it checks whether the constructed model covers all software requirements related with faults; (2) it decreases costs by early detection of modeling deficiencies before the coding and testing phases; and (3) it enables code simplicity in decision of fault occurrence. [ABSTRACT FROM AUTHOR]

This paper presents modeling and study of 12-pulse HVDC (High Voltage Direct Current) based on real time simulation where the HVDC inverter is connected to a weak AC system. In goal to study the dynamic performance of the HVDC link, two serious kind of disturbance are applied at HVDC converters where the first one is the single phase to ground AC fault and the second one is the DC link to ground fault. The study is based on two different mode of analysis, which the first is to test the performance of the DC control and the second is focalized to study the effect of the protection function on the system behavior. This real time simulation considers the strength of the AC system to witch is connected and his relativity with the capacity of the DC link. The results obtained are validated by means of RT-lab platform using digital Real time simulator Hypersim (OP-5600), the results carried out show the effect of the DC control and the influence of the protection function to reduce the probability of commutation failures and also for helping inverter to take out from commutation failure even while the DC control fails to eliminate them. [ABSTRACT FROM AUTHOR]

The North-Western RIF of Morocco is considered as one of the most mountainous zone in the Middle East and North Africa. This area is more serious in the corridor faults region, where the recent reactivation of those tectonic layering may greatly contribute to the triggering of landslides. The consequences of this phenomenon can be enormous property damage and human casualties. Furthermore, this disaster can disrupt progress and destroy developmental efforts of government, and often pushing nations back by many years. In our previous works of Tetouan-Ras-Mazari region, we identified the areas that are prone to landslides by different methods like Weights of Evidence (WofE) and Logistic Regression (LR). In fact, these zones are built and susceptible. Undoubtedly, the challenge to save human lives is vital. For this reason, we develop a robust monitoring model as part of an alert system to evacuate populations in case of imminent danger risks. This model is ground-based remote monitoring system consist of more than just field sensors; they employ data acquisition units to record sensor measurements, automated data processing, and display of current conditions usually via the Internet of Things (IoT). To sum up, this paper outlines a new approach of monitoring to detect when hillslopes are primed for sliding and can provide early indications of rapid and catastrophic movement. It reports also continuous information from up-to-the-minute or real-time monitoring, provides prompt notification of landslide activities, advances our understanding of landslide behaviors, and enables more effective engineering and planning efforts. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection method based on Dynamic Independent Component Analysis (DICA) with new statistics. These new statistics are statistical moments and first characteristic function that surrogate the norm operator to calculate the fault detection statistics to determine the control limit of the independent components (ICs). The estimation of first characteristic function by its series is modified such that the effect of series remainder on estimation is reduced. The advantage of using first characteristic function and moments, over second characteristic function and cumulants, as fault detection statistics is also presented. It is shown that the proposed method can detect a class of faults that the former methods cannot; in particular faults with small amplitude ICs that have either different probability density function or identical probability density function of the ICs, but different low order moments of the ICs compared with the normal performance. Simulation result are used to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

HVAC system is one of the biggest contributors to building energy use, and meanwhile a considerable part of energy is wasted due to the faulty operation of it. Hence, detecting and diagnosing HVAC system faults and improving energy efficiency are of large economic and environmental impact worldwide. However, the current study or application is highly limited by the scarcity of energy usage data, while in recent years electricity submeter was widely implemented and massive amounts of submetering data were accumulated in China. The aim of this paper is therefore to develop a performance assessment framework which is specifically designed for HVAC systems in such commercial buildings with electricity submetering data. This framework consists of three parts. 1) A HVAC terminal energy usage disaggregation algorithm and 2) a cooling load estimation algorithm are firstly performed to make up data deficiency. And then, 3) a diagnosis algorithm is proposed, which uses EPI generated from a Chinese standard (GB/T 17981-2007) as the benchmarks. Finally, a real case study in Shanghai is presented to illustrate the proposed framework and shows that the building operators can easily identify the poor performance area. [ABSTRACT FROM AUTHOR]

The DVtest method (Dynamic Resistance Measurement - DRM) is an off-line, non-destructive testing method based on a dc current being injected through a winding and a tap changer as it moves through all of its positions. The test current is recorded with high resolution. Also, an on-load tap changer (OLTC) motor current can be recorded simultaneously with the test current. A high sampling rate is very important due to fast transition processes (transition time of a resistor OLTC type is approximately 50 ms). The DRM method has been proven as a very effective method in the early detection of possible faults on on-load tap-changers (OLTCs). This test may be used to detect problems such as slow transition time, open circuits, problems with contacts, transition resistors, mechanism, motor control, and much more. This paper provides the description of the DVtest method for OLTC analysis. In addition, a few interesting cases about detected tap changer defects and diagnostic analysis are presented, as well. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor networks (MWSNs) are wireless networks of small sensors moving around a certain coverage area relaying information among themselves and conveying their readings and data to base stations. Imposters are malicious nodes actively engaging other legitimate nodes in the network to read or inject wrong data. MWSN are susceptible to imposter attack and therefore, the protection of MWSN from imposter nodes placed by an adversary to contaminate the sensed data is essential for the reliability of its operation. Imposters detection algorithms need to be distributed in nature and therefore they are susceptible to variety of faults that can perturb the variables for algorithm and cause a major malfunction in the operation of the algorithm and subsequently the entire network if proper recovery mechanisms are not employed. The distributed nature of imposter detection schemes for WSN and the physical environment where the sensors are deployed require some approaches such as stabilization to deal with faults. A stabilizing distributed algorithm can withstand transient faults and start in an arbitrary initial configuration by eventually entering a legitimate system configuration regardless of the current system configuration. We view a fault as a transient fault if it affects the states of the system processes but not their program. In this paper, we propose an imposter detection scheme that can effectively deal with transient faults and arbitrary initialization. In addition, the proposed algorithm effectively adapts to the introduction and the removal of sensor nodes to/from the WSN which makes the proposed algorithm appropriate for practical sensor network applications. Other faults that can occur in the network and in the nodes are beyond the scope of this work. [ABSTRACT FROM AUTHOR]

In recent days, rail transit accidents happen from time to time, but the causes are difficult to be found. According to the stochastic and real-time characteristics of equipment faults, three layer models based on stochastic hybrid automata (SHA) are proposed for interlocking systems. The three layer models consist of a system model, a monitoring model and a fault prediction model. The accidents caused by the equipment faults are predicted by simulating these models together on UPPAAL-SMC platform. The main contributions of this paper include: (1) extracting model patterns for interlocking systems (2) presenting a pattern-based system model generation process and an automatic generation method of monitoring model based on time constraints and (3) defining the accidents prediction model of collision accidents to predict the accidents and monitoring accident causes through model simulation. [ABSTRACT FROM AUTHOR]

In this paper, classification of ball bearing faults using vibration signals is presented. A review of condition monitoring using vibration signals with various intelligent systems is first presented. A hybrid intelligent model, FMM-RF, consisting of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model, is proposed. A benchmark problem is tested to evaluate the practicality of the FMM-RF model. The proposed model is then applied to a real-world dataset. In both cases, power spectrum and sample entropy features are used for classification. Results from both experiments show good accuracy achieved by the proposed FMM-RF model. In addition, a set of explanatory rules in the form of a decision tree is extracted to justify the predictions. The outcomes indicate the usefulness of FMM-RF in performing classification of ball bearing faults. [ABSTRACT FROM AUTHOR]

The electronic equipment used in DC microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. An effective method to detect, isolate, and protect the DC microgrid system against the effects of short circuit faults is extremely important. This research proposes a highly effective new method to protect the DC microgrid system using an Artificial Neural Network (ANN) that will detect and isolate the fault before it affects other parts of the system. It would, therefore, be more dependable for DC microgrid protection. This protection network is distributed all along the DC microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. [ABSTRACT FROM AUTHOR]

One of the most critical categories of machine failure is rolling element bearing failure. Most bearing failures start from raceway fatigue spalls. From the initial formation of spalls, a bearing may still have 10-20% useful life remaining. This makes rolling element bearing an ideal candidate component for fault prognosis. The size estimation for bearing raceway spalls can provide crucial information for bearing fault prognosis. Vibration analysis has been used for bearing fault detection and diagnosis for many years. However, the estimation of bearing fault size using vibration analysis has been only found in dealing with simulated or notched ideal bearing faults. It is a significant challenge to estimate the size of naturally occurring bearing faults using vibration analysis. The objective of this research is to define some feasible vibration signal processing methodologies in dealing with size estimation for naturally generated and propagated faults in high-speed bearings. In this paper, we propose a scheme of estimating bearing spall size based on synchronous signal averaging (SSA) with respect to the bearing fault characteristic frequency, combined with envelope and wavelet analyses of the averaged signals. The averaged signal presents the vibration characteristics within one period of impacts produced by the bearing faults. When the fault size is smaller than the pitch spacing of the balls, the features associated with the balls entry into and exit from the spalled zone can be extracted by envelope and wavelet analyses, and then, the fault size can be estimated. The main novelty in this paper is the use of the tacho-less SSA method in size estimation of naturally occurring large spalls in high-speed rolling element bearings. The technique is validated using the vibration data from naturally spalled bearings in a high-speed bearing test rig. The results show that the technique is effective in revealing the entry and exit features needed for the size estimation of naturally occurring bearing faults. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Parallel transmission lines are difficult to protect due to mutual coupling between circuits. This paper proposes a Naïve Bayes classifier (NBC) based fault detection and classification technique for protection of parallel transmission line involving inter-circuit faults. NBC is a good classification tool for larger data sets as the training process takes less time with greater accuracy. Input given to the fault detection module is the fundamental components of three phase current signals of both circuits. Input given to the fault phase identification and fault classification module is the fundamental component of three phase current signals and zero sequence currents of both the circuits. Seven separate classifiers are designed for fault phase identification for A1, B1, C1, A2, B2, C2 and G. From fault phase identification module faults are classified. Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases. Response time of the proposed method is within 10 ms for all the fault cases studied. [ABSTRACT FROM AUTHOR]

With the evolution of new technologies, the growing reliance on networks has increased immensely. Such an advancement induces malicious attacks and software errors to occur frequently. Building robust network services that can tolerate a wide range of failure types is a fundamental problem in distributed systems. The most fundamental approach, called Byzantine fault tolerance, helps mask arbitrary failures exhibited by failing nodes. Here, the problem of reliably broadcasting messages in a mutli hop network is dealt, where some nodes are likely to fail. Most of the existing solutions require high network connectivity and these requirements become difficult to satisfy when the network grows large. In this paper, a study on various Byzantine fault tolerant methods has been carried out which has been developed and implemented by research experts in this field. These solutions are particularly customized to adapt to sparsely connected networks. [ABSTRACT FROM AUTHOR]

Early and accurate detection and isolation of industrial process faults are crucial to avoiding abnormal situations that cause productivity losses. Principal component analysis and reconstruction-based contribution (PCA-RBC) is a popular method used for such tasks. Unfortunately, this method does not guarantee correct fault isolation in cases where the faulty variables contribute little or do not contribute at all to the main principal components of the PCA model. This is the case, for example, of some pollutant emission levels that do not affect the global performance of a biomass boiler, but that should be maintained below certain thresholds. This paper proposes to adapt the PCA-RBC method to cope with such limitations. The idea is first to classify the data onto normal and abnormal conditions according to a selected parameter threshold, and then to build a PCA model using the normal dataset. The RBC approach is applied on the abnormal dataset to identify the variables that mostly contribute to the faulty situations. The proposed method is successfully demonstrated using real data from an industrial case. It is noted that an attempt to develop an accurate predictive model of the selected parameter using projection to latent structures (PLS) was unsuccessful. [ABSTRACT FROM AUTHOR]

The paper introduces a generalized method for analysis of multiple, simultaneous short circuit, open circuit, and open circuit falling conductor faults in mixed three-phase and six-phase power systems with untransposed lines. The method is systematic and suitable for all types of faults, any number of simultaneous faults, and any number of phases. Calculation of all network unbalanced voltages and currents during faults is done in one straightforward step. Coupling among sequence networks in untransposed transmission lines is accounted for. Coupling between the three-sequence networks of the three-phase part and the six-sequence networks of six-phase part is also derived. The method is applied also for transient stability study of mixed three-phase and six-phase power systems during any type of faults. Detailed derivation of the governing equations in each part is presented. Simulation results on the IEEE 300-bus system and the IEEE 30-bus system are given to validate the proposed method. [ABSTRACT FROM AUTHOR]

Misfire in an Internal Combustion engine is a serious problem that needs to be addressed to prevent engine power loss, fuel wastage and emissions. The vibration signal contains the vibration signature due to misfire and a combination of all vibration emissions of various engine components. The vibration signals acquired from the engine block are used here. Descriptive statistical features are used to represent the useful information stored in vibration signals. Out of all the statistical features, useful features were identified using the J48 decision tree algorithm and then the selected features were classified using logistic and simple logistic functions. In this paper, performance analysis of logistic and simple logistic function has presented for detecting misfire in Spark Ignition (SI) Engine. [ABSTRACT FROM AUTHOR]

In this work, an automated statistical analysis of vibration characteristics for the condition monitoring of hydraulic systems is proposed. Features from FFT vibration data of a micromechanical accelerometer measured at the gear pump are extracted, selected by their correlation coefficient to the target fault value, and subsequently reduced to discriminant functions which allow the classification of component conditions using multivariate statistics. The approach was successfully evaluated with an experimental hydraulic test bench simulating typical fault scenarios like valve switching degradation or gas leakage of the accumulator. It was shown that the studied fault scenarios can be selectively detected (internal pump leakage) and even quantified concerning the grade of severity in case of valve switching degradation, accumulator gas leakage, and oil aeration. Furthermore, the long-term stability of the statistical model was evaluated over several weeks. [ABSTRACT FROM AUTHOR]

Fault detection in electrical machines is a topic widely explored by researchers, especially bearing faults that represent about half of the total three-phase induction motor failure occurrences. This kind of fault is detectable by specific frequencies of the stator current and is a wide source of investigation. Thus, this work presents a predictability analysis method that provides patterns based on measures of relative entropy, Bhattacharyya distance, and Lempel-Ziv complexity estimated over reconstructed signals obtained from wavelet packet decomposition components. The signals under study were collected from motors with faults in the inner or outer races, which were artificially created in laboratory. These patterns were applied to three neural network topologies, which were used to classify the signals into two groups: normal or faulty. [ABSTRACT FROM AUTHOR]

In this paper a new software system for finding of incipient faults is presented. An experiment is made with real measurement of partial discharge(PD) that appeared in power transformer. The software system uses acquisition data to define the real state of this transformer. One of the most important criteria for the power transformer's state is the presence of partial discharges. The wave propagation caused by partial discharge depends on scheme of the winding and construction of the power equipment. In all cases, the PD source had a specific position so the wave measured from the PD -coupling device had a specific waveform. The waveform is different when PD-coupling device is put on a specific place. The waveform and the time of propagation are criteria for the localization of the source of incipient faults in the volume of power transformer. [ABSTRACT FROM AUTHOR]

Software reliability growth models (SRGMs) have evolved from describing fault detection process (FDP) into incorporating fault correction process (FCP) as well. Restricted by mathematical tractability, analytical models are facing difficulties for more accurate description the real world situations, e.g. debuggers being different in terms of debugging capabilities and experiences. In this paper, a simulation approach is proposed to model FDP and FCP together considering debuggers with different contributions to the fault detection rate and different fault correction time. [ABSTRACT FROM AUTHOR]

To obtain high dynamic performance on induction motor drives (IMD), variable voltage and variable frequency operation has to be performed by measuring speed of rotation and stator currents through sensors and fed back them to the controllers. When the sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed. This paper studies the negative effects on a 12.5 hp induction motor drives when the field oriented control system is subjected to sensor faults. To illustrate the importance of this study mine hoist load diagram is considered as shaft load of the tested machine. The methods to recover the system from sensor faults are discussed. In addition, the various speed sensorless schemes are reviewed comprehensively. [ABSTRACT FROM AUTHOR]

Wide area adaptive backup protection is one of the research hotspots in complicated electric power system field. The researches in this paper are mainly serving wide area protection system. We will study a novel fault factor analysis in complicated electric power system, and this scheme can reduce the influence of relay protection's wrong action device on fault detection. It has been proved by a large number of simulation experiments, even if there is the influence of random disturbance, the system failure in electric power system can be rapidly and effectively analysed by utilizing a fault factor analysis scheme. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are typically resource-constrained and battery driven. They are usually deployed in hostile and inaccessible environments to perform monitoring and tracking. Therefore, due to their physical deployment location sensor nodes are very prone to faults and failures. In recent years, much work has been done on the various aspects of the WSNs, especially fault management. Fault management is concerned with detecting, diagnosing, isolating and resolving faults and failures. Thereby, a network's management system with an efficient fault management platform makes the network fault tolerant in the events of faults and failures. In this context, many solutions have been proposed for fault management in WSNs. However, hierarchical architecture based schemes have proven to be more efficient as compare to centralized and distributed schemes. This paper aims to overview hierarchical architecture based schemes for fault management in WSNs. We critically analyze their effectiveness and short comings for large-scale WSNs. We believe through such an exercise provides a great background to establish new and effective fault management solutions for WSNs. [ABSTRACT FROM AUTHOR]

The finite element analysis and experimental measurement are used to study the vibration characteristics of gearbox impact faults. Parametric models of gearbox gear train are built through three-dimensional modeling software, and the finite element software transient dynamics module is used to analyze the vibration acceleration response of the gearbox under different conditions of impact load. Compare the actual acceleration response which is collected from fault simulation experiment with the finite element simulation results to verify the accuracy of the parametric models. Meantime, the vibration response of the gear box impact failure is analyzed, and the sensor layout optimization scheme is proposed, which reduces the blindness of vibration measuring point arrangement and reduces the necessary channels for the monitoring and diagnosis. As a result, the cost of monitoring could be reduced. [ABSTRACT FROM AUTHOR]