Showing papers on "Stuck-at fault published in 2022"
TL;DR: In this paper , the performance of the impedance-based fault location methods is compared in which the impedance parameter of the faulted line section is calculated as a measure of the distance to the fault.
Abstract: Electric transmission lines play a very essential role in transmitting power energy from generation centers to consumption regions. They can be exposed to fault occurrences due to various reasons, such as lightning strikes, malfunction of components, and human errors. Since fault is unpredictable, a fast fault location method is required to minimize the impact of fault in power systems. This paper presents a research work for comparing the performance of the impedance-based fault location methods, in which the impedance parameter of the faulted line section is calculated as a measure of the distance to the fault. To evaluate the capability of the methods for correctly detecting and locating the fault locations, comprehensive simulation results are carried out. This computation is based on modeling and simulating a three-phase 220kV overhead transmission line in the Matlab/Simulink software. Short circuits which occur in various fault resistances and locations along the transmission line are emulated to investigate several case studies and the accuracy of fault location determination is calculated to compare the performance among these fault location methods.
35 citations
TL;DR: Zhang et al. as discussed by the authors proposed a zero-shot learning Compound Fault Diagnosis Model of bearings (ZLCFDM) based on a convolutional neural network to extract the time-frequency features of the compound fault signal.
Abstract: Due to the concurrency and coupling of various types of faults, and the number of possible fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault samples to identify unknown compound faults is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearings (ZLCFDM). We design an encoding method to express the semantics of single faults and compound faults according to the fault characteristics. A convolutional neural network is developed to extract the time–frequency features of the compound fault signal. Then we embed the semantic feature of the fault into the visual space of the fault data. The Euclidean distance is used to measure the distance between the signal features and the semantic features of the compound faults to identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on a self-built testbed. The results demonstrate that the accuracy of identifying compound fault reached 77.73% when the model was trained without any compound fault samples. • A novel compound fault diagnosis method based on zero-shot learning. • Model trained with the vibration data of single faults to identify unknown compound fault. • A semantic vector for the single and compound faults that require no expert knowledge.
25 citations
TL;DR: Zhang et al. as discussed by the authors proposed a zero-shot learning Compound Fault Diagnosis Model of bearings (ZLCFDM), which uses a convolutional neural network to extract the time-frequency features of the compound fault signal.
Abstract: Due to the concurrency and coupling of various types of faults, and the number of possible fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault samples to identify unknown compound faults is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearings (ZLCFDM). We design an encoding method to express the semantics of single faults and compound faults according to the fault characteristics. A convolutional neural network is developed to extract the time-frequency features of the compound fault signal. Then we embed the semantic feature of the fault into the visual space of the fault data. The Euclidean distance is used to measure the distance between the signal features and the semantic features of the compound faults to identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on a self-built testbed. The results demonstrate that the accuracy of identifying compound fault reached 77.73% when the model was trained without any compound fault samples.
25 citations
TL;DR: In this article , a fault feature extraction method based on variational mode decomposition (VMD) and Hilbert-Huang transform (HHT) is designed for fault location and type identification in power grid.
25 citations
TL;DR: The proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances, and does not require high sampling frequency and has good robustness against measuring noise.
Abstract: Existing nonunit protection schemes inevitably require setting, which is a serious problem in practical engineering. Faults occurred at different fault zones will result in different equivalent models, therefore, the fault zone can be determined by recognizing which equivalent model the fault fits well with. In this article, this “model recognition” idea is introduced in fault identification and a “setting-less” protection method is proposed. First, the Peterson equivalent circuits when faults occur at backward external zone, internal zone, and forward external zone are presented, respectively. Accordingly, the corresponding three fault voltage expressions are derived, which are defined as three fault modes. Then, the three fault modes are used to approximate the measured fault voltage using Levenberg-Marquardt optimal approximation method. The fault mode that best fits the measured fault voltage is recognized as the final determined fault mode, which is used for fault identification without setting threshold value. Numerous test studies carried out in Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) and real-time digital simulator have demonstrated that the proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances. The proposed method does not require high sampling frequency and has good robustness against measuring noise.
17 citations
TL;DR: In this article , the authors proposed a model recognition approach for fault identification and a setting-less protection method, which does not require high sampling frequency and has good robustness against measuring noise.
Abstract: Existing nonunit protection schemes inevitably require setting, which is a serious problem in practical engineering. Faults occurred at different fault zones will result in different equivalent models, therefore, the fault zone can be determined by recognizing which equivalent model the fault fits well with. In this article, this “model recognition” idea is introduced in fault identification and a “setting-less” protection method is proposed. First, the Peterson equivalent circuits when faults occur at backward external zone, internal zone, and forward external zone are presented, respectively. Accordingly, the corresponding three fault voltage expressions are derived, which are defined as three fault modes. Then, the three fault modes are used to approximate the measured fault voltage using Levenberg-Marquardt optimal approximation method. The fault mode that best fits the measured fault voltage is recognized as the final determined fault mode, which is used for fault identification without setting threshold value. Numerous test studies carried out in Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) and real-time digital simulator have demonstrated that the proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances. The proposed method does not require high sampling frequency and has good robustness against measuring noise.
13 citations
TL;DR: In this article , an adaptive fault diagnosis network framework is proposed to solve the equipment fault diagnosis problems with new fault types under multiple working conditions, which consists of a multi-scale feature extractor, adaptive fault discriminator, and a new fault cluster.
13 citations
TL;DR: In this article , the authors considered the nonlinear effect of controller saturation and proposed a low-order analytical model to estimate the practical working state of the controller within the current loop, which reveals a detailed transient transition of the fault current.
Abstract: Fault characteristics analysis normally provides a theoretical foundation for the protection design. Although conventional fault analysis of inverter-interfaced renewable energy sources (IIRESs) can characterize post-fault steady-state at the power frequency, it might not effectively present the fault transient. This can negatively affect the protection based on fault transient data within 20–30 ms after a grid fault. Thus, the fault transient of IIRESs needs to be fully investigated. Given this, the nonlinear effect of controller saturation is considered in this study. The domain of attraction (DOA) is first established to estimate the practical working state of the controller within the current loop. The transient fault current (TFC) analytical model is then proposed in accordance with the nonlinear control response of the controller under different working stages, which reveals a detailed transient transition of the fault current. Meanwhile, the proposed model is low-order and presents high applicability in different fault scenarios. Simulation results indicate that the TFC analytical accuracy obtained using the proposed method is improved by at most 50% relative to the accuracy reported in published studies. Field short-circuit test data are ultimately used to verify the performance of the proposed TFC analytical model.
10 citations
TL;DR: In this article , a fault-tolerant control problem for a class of discrete-time systems subjected to sensor fault is studied, and a fault detection method is constructed to detect sensor fault, which avoids obtaining residual signal by designing fault detection observer.
Abstract: The fault-tolerant control problem is studied for a class of discrete-time systems subjected to sensor fault. The data-based fault detection method is constructed to detect sensor fault, which avoids obtaining residual signal by designing fault detection observer. When the fault is detected, a discrete-time observer is built for estimating sensor fault. The fault estimation is employed to realize controller reconstruction. For the purpose of obtaining better control performance, a model-free adaptive fault-tolerant controller is developed by employing more past control information. Accordingly, the flexibility and adaptability of the fault-tolerant controller are improved by introducing more adjustable parameters. Finally, the developed method is validated to be effective by a simulation example.
9 citations
TL;DR: In this article , an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I.
Abstract: This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method.
7 citations
TL;DR: In this paper , a single-ended fault identification algorithm using a closed-form parametric model of the fault transient behavior is presented. But the model is not suited due to the transmission delays.
Abstract: The protection of meshed HVDC grids requires the fast identification of faults affecting the transmission lines. Communication-based methods are thus not suited due to the transmission delays. Many approaches involving a model of the transient behavior of the faulty line have recently been proposed. Nevertheless, an accurate description of the traveling wave phenomenon in multi-conductor lines such as overhead lines requires complex computations ill-suited for fast fault identification. This paper presents a single-ended fault identification algorithm using a closed-form parametric model of the fault transient behavior. The model combines physical and behavioral parts and depends explicitly on the parameters that characterize the fault, namely the fault distance and impedance. When a fault is suspected, the fault parameters are estimated so that the model fits best the received measurements. The confidence region of the estimated fault parameters is used to decide whether the protected line is actually faulty or not. The proposed algorithm is tested on a 4 station grid simulated with EMTP-RV software. The method is able to identify the faulty line using a measurement window of less than 0.5 ms. This allows ultra-fast fault clearing and can hence improve the overall reliability of future HVDC grids.
TL;DR: In this article , an active fault diagnosis method for a class of discrete-time closed-loop systems with stochastic noise has been proposed to detect and isolate the faults without cutting off the original input signal.
Abstract: This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.
01 Mar 2022
TL;DR: In this article , an open-switch fault and output current sensor fault diagnosis and identification method for the matrix converter (MC)-based permanent magnet synchronous motor (PMSM) drive system was proposed.
Abstract: Reliable fault diagnosis and identification are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. Since open-switch fault and output current sensor fault both affect the residual of output phase current, it is difficult to identify them apart from each other. Thus, this article proposes an open-switch fault and output current sensor fault diagnosis and identification method for the matrix converter (MC)-based PMSM drive system. The finite control set-model predictive control (FCS-MPC) method is applied in the MC-based PMSM drive system. The fault identification method is proposed based on the extracted different faulty features of the open-switch fault and the output current sensor fault. After the open-switch fault is separated from the current sensor fault, an error-voltage-based open-switch fault diagnosis strategy is applied to locate the faulty switch. Hardware-in-the-loop tests are carried out to verify the effectiveness of the proposed method.
TL;DR: In this paper , a new method of fault detection based on residual signal generation is presented, which is based on the reconstruction of the system output via an ultra-local model and a model-free controller.
Abstract: This paper presents a new method of fault detection based on residual signal generation. Most of the existing diagnostic methods that use the residual to detect a failure are often based on the knowledge of the system model. The developed method does not require a precise knowledge or deep information about the system model. It is based on the reconstruction of the system output via an ultra-local model and a model-free controller. The reconstructed/estimated output is used to build the residual signal which is the fault indicator. Several simulation tests have been performed to evaluate the potential of the proposed approach for fault diagnosis. A fault on an actuator of the system is simulated in linear, non-linear and multi-input non-linear case studies. The simulation results reveal that the fault is successfully detected for all these systems under a noisy environment.
TL;DR: In this article , a dual-guided adaptive decomposition method of fault information and fault sensitivity (FIFS-ADM) is proposed to address the multifault mutual interference.
Abstract: Fault diagnosis is essential for the safe operation and subsequent maintenance of mechanical equipment. However, multifault mutual interference brings great challenges to fault diagnosis when multicomponent faults occur simultaneously. Furthermore, since multiple components operate under different varying-speed conditions, it is difficult to match fault information from different components to their respective speeds, making fault diagnosis even more difficult. A dual-guided adaptive decomposition method of fault information and fault sensitivity (FIFS-ADM) is proposed to address the above issues. First, a fault information-driven empirical wavelet transform whose segmentation boundaries are optimized by fault richness index is presented to separate multicomponent faults, thereby eliminating multifault interference. Then, multiorder tracking that refers to using multiple speed signals to resample the decomposed subsignals, respectively, is performed to convert these speed-varying subsignals into angular-domain stationary signals. Finally, a health recognition matrix composed of multicomponent fault sensitivity indicators is introduced to achieve not only the matching of fault component and speed but also fault location and health assessment of multiple components. Furthermore, simulated signals and actual signals are built to validate the proposed method, which shows the effectiveness and superiority of FIFS-ADM.
TL;DR: In this article , a fault diagnosis method for the six-phase inverter based on vector space decoupling (VSD) is proposed to deal with the open phase fault and power switch fault.
Abstract: Compared with the three-phase motor, the six-phase motor has lower torque ripple and higher fault tolerance performance, which makes it widely used in aviation, ships, industrial manufacturing, and other application fields. However, the probability of failure of the polyphase motor system increases with the increase in the number of phases. In order to deal with the open phase fault and power switch fault of the six-phase inverter, a fault diagnosis method for the six-phase inverter based on vector space decoupling (VSD) is proposed. The open phase fault index is first determined according to the VSD decoupling inverse transform and the current constraints. The fault index is then optimized from the perspective of preventing misdiagnosis and improving reliability, and the open phase fault can be diagnosed in one fundamental cycle. In addition, the current trajectory of harmonic plane after switch fault is analyzed, and the back propagation (BP) neural network is used to identify the harmonic plane current trajectory of different types of switch fault. Finally, the correctness and feasibility of the proposed fault diagnosis method are verified by simulations and experiments. The obtained results demonstrate that the proposed method can quickly and accurately locate the open phase fault and switch fault without additional hardware. The proposed method is simple, efficient, and robust.
17 Jun 2022
TL;DR: In this article , a blind fault detection method was proposed to detect faults in a position servo system using the Short Time Fourier Transform (STFT) and Principal Component Analysis (PCA).
Abstract: This work studies a blind fault detection method, which only analyses a system's output signal for any change in the characteristics from pre-fault to post-fault to identify the occurrence of faults. In our case the fault considered to develop the procedure is change in time constant of an aircraft's aileron-actuator system and its simplified version - a position servo system. The method is studied as an alternative to conventional fault detection and identification methods. The output signal is passed through a filter bank to enhance the effect of a fault. The Short time Fourier transform is performed on the enhanced pre-fault and post-fault signals components to obtain indicators. Fault detection is approached as a clustering problem determining distances to fault signatures. This work presents two techniques to create signatures from the indicators. In the first method, the mean of the indicators is the signature. Tests on a position servo system show that the method effectively classifies the indicators by more than 85 % and can be used for online classification. A second method uses Principal Component Analysis and defines vector sub-space signatures. It is observed that for the position servo system, the pre-fault indicators had 14 % of false alarms and post-fault indicators the missed the faults by 17%. This second method was also applied to one axis model of an F-14 aircraft's aileron-actuator system. The results obtained showed around 80 % of correctly identified pre-fault indicators and post-fault indicators. The blind fault detection method studies has potential but needs to be understood further by applying it to more varied cases of faults and systems.
TL;DR: In this paper , a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering was proposed to solve the problem of low protection sensitivity when a high resilience grounding fault occurs in a resonant grounding system.
Abstract: When a single-phase grounding fault occurs in a resonant grounding system, the determination of the fault location remains a significant challenge due to the small fault current and the instability of the grounding arc. In order to solve the problem of low protection sensitivity when a high-resistance grounding fault occurs in a resonant grounding system, this paper proposes a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering. By analyzing the characteristics of the zero-sequence current upstream and downstream of the fault point when a single-phase grounding fault occurs in the resonant grounding system, it is concluded that the waveform similarity on both sides of the fault point is low. DTW distance can be used to measure the similarity of two time series, and has the characteristics of good fault tolerance and synchronization error tolerance. According to the rule that the DTW value of faulty section is much larger than that of nonfaulty sections, FCM clustering is used to classify the DTW value of each section. The membership degree matrix and cluster centers are obtained. In the membership degree matrix, the section corresponding to the data in a class of their own is the faulty section, and all other data correspond to the nonfaulty section; otherwise, it is a fault occurring at the end of the line. The simulation results of MATLAB/Simulink and the field data test show that the method can accurately locate the faulty section.
TL;DR: A belief rule-based fault recognition and location model (BR-FRL) for transmission medium in bus network systems and a new model architecture is proposed, which integrates the belief rules, the group method of data handling (GMDH), and the cautious conjunctive rule (CCR).
Abstract: For a control system with bus network topology, it is significant while difficult to accurately recognize and locate the fault of the transmission medium. The reason is that the occurrence of one fault can lead to the abnormality of multiple terminal nodes. How to use these abnormal signals to realize effective fault recognition and fault location is the key to solving the problem. Inspired by the manual inference in engineering practice, this article proposes a belief rule-based fault recognition and location model (BR-FRL) for transmission medium in bus network systems. The core idea is fault location based on the results of fault recognition. To adapt to the flexible expansion of bus topology, the designed BR-FRL model has self-organization characteristics. The highlights of this article include three aspects: 1) the progressive reasoning of fault recognition and location based on belief rules is realized in one model; 2) a new model architecture is proposed, which integrates the belief rules, the group method of data handling (GMDH), and the cautious conjunctive rule (CCR); and 3) the parameter learning based on stochastic gradient descent (SGD) is applied to BR-FRL, and the sensitivity analysis of evidence weight in CCR of BR-FRL is performed. The effectiveness of BR-FRL is verified by an experiment on a gigabit-capable passive optical network (G-PON).
TL;DR: In this paper , two alternative fault location algorithms based on two-point measurements are proposed for unbalanced medium voltage overhead distribution systems of radial configuration with or without distributed generation, which are suitable for unbalance medium voltage over distribution systems.
TL;DR: In this article , a universal method for single parametric and catastrophic fault diagnosis of analog linear circuits is presented, which is based on fundamental laws governing linear circuits and methods of their analysis.
Abstract: A universal method for single parametric and catastrophic fault diagnosis of analog linear circuits is presented in this paper. The methodology is based on fundamental laws governing linear circuits and methods of their analysis. The method involves creating models of the faulty elements, both passive and active, including current and voltage sources and applying an appropriately modified node method. This enables the creation of simple formulas to define the parameters of the faulty elements. Some measurement data must be collected during the course of the diagnostic test performed in the frequency domain and certain computation results obtained in the before-test-process. The method achieves all of the objectives of fault diagnosis: detection, location, and estimation of the faulty value. In 88.48%, the method correctly identifies the fault and estimates its value, but in 6.47%, the actual fault is accompanied by a virtual fault. The method is adapted to real conditions to improve the practical relevance and can be directly extended to double fault diagnosis. Six numerical examples are presented to illustrate the method.
TL;DR: In this article , a protection application case-study for rapid fault identification, exploiting the natural frequency oscillation of a network, has been proposed for a standalone lowvoltage DC-microgrid system (LV DCmG) network.
TL;DR: Based on graph theory, the adjacency matrices of the electrical components and circuit breakers are constructed in this paper , and the tripping path of the circuit breaker connecting each protected electrical component is searched by the Floyd-Warshall algorithm to formulate the fault isolation set for all protected electrical components.
Abstract: The flexible and dynamic operation condition of the power system requires that the fault isolation scheme has sufficient adaptability. A novel fault isolation scheme based on wide-area information is proposed in this article. Based on graph theory, the adjacency matrices of the electrical components and circuit breakers are constructed. The real-time switch status data is exploited to reflect the dynamic changes of the network topology. Then, the tripping path of the circuit breaker connecting each protected electrical component is searched by the Floyd–Warshall algorithm to formulate the fault isolation set for all protected electrical components. The proposed approach is applied to fault isolation from a wide-area perspective, and it is not affected by system coordination and selectivity compared with the one based on local information. The effectiveness of the proposed scheme is verified on a power system with the typical electrical connection under the cases of a single fault, multiple faults, circuit breaker failure, and substation with the outage of dc power supply, and the case where the circuit breakers cannot trip. Since the tripping sequence of the circuit breakers is determined before the fault occurs, even with dynamic changes of the power system conditions, the proposed scheme can minimize the fault isolation zone with low fault clearance time.
TL;DR: In this paper , the authors proposed a generalized method to detect and identify the open-circuit faults (including open-switch and open-phase faults) in star-connected symmetrical multiphase drives with different phase numbers.
Abstract: Multiphase drives with fault-tolerant capability are favored in high-reliability applications. In most fault-tolerant multiphase drives, the fault types and locations must be known prior to the fault-tolerant control. Therefore, fault diagnosis is an indispensable procedure. This paper proposes a generalized method to detect and identify the open-circuit faults (including open-switch and open-phase faults) in star-connected symmetrical multiphase drives with different phase numbers. The selected fault diagnosis signals are calculated solely from simple arithmetical operations of measured phase currents, resulting in easy real-time implementation. As fault detection signals are derived from the physical constraint imposed by the neutral point, the proposed method is naturally robust and not sensitive to operating points, machine transients and harmonics in stator currents. Therefore, a relatively small fault detection threshold can be used and fast detection can also be achieved. After the fault has been detected, the fault type is further identified according to the polarity of the integration of phase currents. Experimental results on nine-phase and five-phase induction machine drives verify the generality, fast diagnosis speed and robustness of the proposed method.
TL;DR: In this article , a fault location algorithm for three-terminal lines is proposed, which does not need synchronization of terminals data but also does not require line parameters values and does not depend on pre-fault conditions and network structure.
TL;DR: In this article , a single-ended fault location method based on control and protection coordination is proposed to solve the problem of insufficient equations in principle, and the simulation results show that the proposed method has high accuracy and is immune to fault resistance and fault location.
Abstract: Traditional single-ended fault location method is influenced by the fault resistance, and the remote in feed, which reflects as the two equations built by fault phase have four unknowns. And in the grid-connected converter system, the weak feedback characteristic further reduces the performance of the method. To solve it, a single-ended fault location method based on control and protection coordination is proposed in this paper. First, using the high controlled-ability of the converter, the control strategy for fault ride-through (FRT) operation is divided into two stages by switching control modes. Then, according to the electrical quantities under two stages, four uncorrelated equations can be established, and the equations still have four unknown parameters. The fault distance can be calculated by solving them. The simulation results show that the proposed method has high accuracy and is immune to fault resistance and fault location, it solves the problem of insufficient equations in principle.
TL;DR: The research shows that the proposed method can accurately diagnose faults, and can accurately locate faulty components in complete information, and give the probability of occurrence of potentially faulty components under partial information, which can effectively assist in the location of faulty components.
Abstract: To study the typical fault diagnosis and fault location technology of the hydraulic system of the domestic civil aircraft, the logic data of the typical fault is constructed according to the formation conditions of the fault. The operation data of the typical fault is collected, and the Bayesian network is used to realize the fault diagnosis and fault components position. First, according to the fault formation conditions in the unit operation manual of a certain type of domestic civil aircraft, referring to the construction method of the logic data, taking the typical fault of the hydraulic system as an example, the fault logic data of the domestic civil aircraft is established to intuitively reflect the logical relationship of the fault formation; secondly, based on the constructed logic data, considering the formation conditions of the fault, a Bayesian network corresponding to the logic data is established, and the logical relationship formed by the fault is represented by the value of the conditional probability distribution; obtain quick access recorder (QAR) data and its parameter information according to the input information of the logic data; finally, according to the established Bayesian network and the obtained QAR data, apply forward reasoning to realize the diagnosis of typical faults of the hydraulic system. Under the condition of partial information, reverse reasoning is applied to locate the faulty components of hydraulic system. The research shows that the proposed method can accurately diagnose faults, and can accurately locate faulty components in complete information, and give the probability of occurrence of potentially faulty components under partial information, which can effectively assist in the location of faulty components. The research work has certain reference significance for improving the fault diagnosis function of the airborne health management system and the ground health management system of domestic civil aircraft.
TL;DR: In this paper , the authors apply the thinning method, using intensity functions of the delayed S-shaped and inflection S -shaped software reliability growth models (SRGMs) to generate sample data of the fault detection time from the fault-counting data.
Abstract: Fault-counting data are collected in the testing process of software development. However, the data are not used for evaluating the efficiency of fault correction activities because the information on the fault detection and correction times of each fault are not recorded in the fault-counting data. Furthermore, it is difficult to collect new data on the detection time of each fault to realize efficiency evaluation for fault correction activities from the collected fault-counting data due to the cost of personnel and data collection. In this paper, we apply the thinning method, using intensity functions of the delayed S-shaped and inflection S-shaped software reliability growth models (SRGMs) to generate sample data of the fault detection time from the fault-counting data. Additionally, we perform simulations based on the infinite server queuing model, using the generated sample data of the fault detection time to visualize the efficiency of fault correction activities.
TL;DR: In this paper , a single-terminal fault location method for transmission lines integrated by the inverter-type source is proposed, which combines the boundary condition and the sequence impedance difference, two uncorrelated equations can be built.
Abstract: Accurate fault location plays an important role in the fault clearance of the transmission line. However, because of the influence caused by fault resistance and the remote end system, there are four unknowns in the two equations built by the fault phase. Thus, the equations cannot be solved. In this paper, a single-terminal fault location method for transmission lines integrated by the inverter-type source is proposed. For a single-phase to ground fault or phase-to-phase fault, due to the specific control strategy, the equivalent positive and negative-sequence impedances of the converter are unequal in most cases. However, the positive and negative-sequence impedances of other elements are approximately equal, and the boundary condition at the fault branch is definite. Combining the boundary condition and the sequence impedance difference, two uncorrelated equations can be built. Substituting them to the equations built by the fault phase, the fault location can be calculated accurately. And the influence caused by fault resistance and the remote end system is eliminated. The simulation results show that the proposed method can achieve fault location accurately. Moreover, it has higher reliability and is independent of control strategy, fault distance and fault resistance.