Showing papers on "Stuck-at fault published in 2023"

A zero-shot fault semantics learning model for compound fault diagnosis

Abstract: Compound fault diagnosis of bearings has always been a challenge, due to the occurrence of various faults with randomness and complexity. Existing deep learning-based methods require numerous compound fault samples for training. However, sufficient training samples for every type of compound fault are usually unavailable in realistic industrial scenarios. In this paper, we propose a zero-shot fault semantics learning model trained on single fault samples to identify unknown compound faults. First, we propose a convolutional autoencoder-based fault semantics construction method to generate the fault semantics for single and compound faults. Second, a CNN-based feature extractor is designed to extract fault features from time–frequency domain vibration signals. Then we design an autoencoder-based fault semantics embedding module to embed the compound fault semantic vectors into the fault feature space as the category centroids. Finally, by the similarity measurement between the compound fault features and the category centroids, the model is able to identify unknown compound faults. Extensive comparative results demonstrate that our method outperforms a series of state-of-the-art models, with the accuracy of 78.40% for three categories of compound faults in the case of 2000 single-fault training samples per category.

Advanced fault location scheme for superconducting cables based on deep learning algorithms

Abstract: This paper addresses the challenge of fault localization in power grids which incorporate Superconducting Cables (SCs) and presence of inverter-connected generation, by proposing a novel data-driven fault location scheme. The developed fault location algorithm utilizes the transformation of time domain fault current and voltage signatures, to time–frequency domain and exploits the advantages of a Convolutional Neural Network (CNN) to estimate the fault position along SCs. The proposed algorithm has been tested using a verified model of SC, and the results revealed that it can provide precise fault localization for a wide range of fault scenarios, including different fault types, fault resistance values and fault inception angles. Furthermore, the proposed scheme robustness has been verified against different influencing factors accounting for very small increments of fault location, additive noise and different value of sampling frequency. For validation purposes, the effectiveness of the CNN-based algorithm has been compared with other data-driven algorithms and the relevant advantages have been highlighted.

Fault Isolation and Estimation in Networks of Linear Process Systems

Abstract: Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered in this paper with only a single conserved extensive quantity but with a network structure containing loops. These loops make fault detection and isolation challenging to perform because the effect of fault is propagated back to where it first occurred. As a dynamic model of network elements, a two input single output (2ISO) LTI state-space model is proposed for fault detection and isolation where the fault enters as an additive linear term into the equations. No simultaneously occurring faults are considered. A steady state analysis and superposition principle are used to analyse the effect of faults in a subsystem that propagates to the sensors’ measurements at different positions. This analysis is the basis of our fault detection and isolation procedure that provides the position of the faulty element in a given loop of the network. A disturbance observer is also proposed to estimate the magnitude of the fault inspired by a proportional-integral (PI) observer. The proposed fault isolation and fault estimation methods have been verified and validated by using two simulation case studies in the MATLAB/Simulink environment.

Study of influence of transient resistance on distance fault location based on one-sided method

Abstract: Fault location on 110–220 kV overhead power lines is one of the main functions of modern relay protection devices. Currently, the actual errors of fault location based on emergency mode (EM) parameters in most cases are about 5 %. However, there are cases when they exceed 10–20 %. One-sided fault location based on EM parameters and improvement of its accuracy are important issues to study since there isn’t a communication channel on power lines for the transmission of emergency information everywhere. The main parameter that has a significant impact on the accuracy of fault location method is the transient resistance. The aim of the research is to study the effect of transient resistance on the one-sided fault location method based on EM parameters proposed by A.E. Arzhannikov. In the course of the study, the following tasks are set: assessment of the effect of transient resistance on the accuracy of the specified fault location method; determination of the polarizing value for fault location, which ensures greater accuracy of the method, including the presence of transient resistance at the place of a short circuit; defining a criterion to recognize the type of short circuit that does not depend on the transient resistance; development of a method to determine the value of the transient resistance at the fault location. To study and evaluate the errors of distance fault location based on EM parameters, a series of calculations of short circuit currents has been made for various transient resistances at the fault location and at various distances of the fault. The calculation of currents and data processing has been carried out in the ARM SRZA software package. A single overhead line with a voltage of 110 kV with a two-sided supply and a length of 70 km is chosen as the object under study. To perform the research, the primary converters are taken as ideal. The authors have obtained the estimation of the errors of the one-sided fault location method. It is proposed to use the zero-sequence current as a polarizing quantity in the indicated fault location method, which provides greater accuracy than the use of the negative sequence current. To ensure the stable operation of one-sided fault location based on EM parameters, especially in case of short circuit with significant transient resistances, the use of methods that are insensitive to transient resistances is justified. A parameter to identify the type of short circuit is proposed, the coefficient of the image Kobr. Its values are determined for each type of short circuit, a small dependence on transient resistances is found. A calculation substantiation is given for the value of the identification coefficient of two-phase faults (Phase-to-Phase and Phases-to-Earth) according to the ratio of negative and zero sequence currents. The authors have estimated the transient resistance at the fault site by the ratio of symmetrical components for the simulated power transmission line. The results of the study can be used to improve the existing methods of fault location based on EM parameters, namely: to improve their accuracy considering the transient resistance at the fault site; to improve their accuracy using a suitable polarizing value; to determine the type of short circuit more accurately by using the proposed identification parameter.

An intelligent technique for fault detection and localization of three-level ANPC inverter with NP connection for electric vehicles

Abstract: An intelligent technique for detecting and localizing an inverter switch fault or phase fault of a Three-Level Active Neutral Point Clamped (ANPC) inverter is proposed in this research. Moreover, a 3L-ANPC inverter can gain the controllability of EV's power train and not need to be stalled even after the occurrence of the fault. Hence, an efficient fault diagnosis methodology is required to identify the type of phase fault by a Support Vector Machine (SVM), a machine learning model consisting of sets of labeled training data with regression and classification challenges. Finally, when the fault occurs, the location of the switch fault can be identified by a Deep Neural Network (DNN), which consists of layers of neurons between the input and output layers which fuses the feature extraction process with increased accuracy. Thus, the detection and localization of the open-circuit fault of the switches in the ANPC inverter help overcome all single faults, hence gaining its current controllability without stopping the vehicle. The accuracy of fault detection is improved in a precise manner. Finally, the performance of the proposed work is evaluated over other conventional models concerning varied metrics like the accuracy of identification and localization.

Open circuit fault diagnosis and fault classification in multi-level inverter using fuzzy inference system

Abstract: Multi-level inverters (MLIs) have been successfully used to integrated the renewable energy sources (RES) into microgrids. However, the operation of MLI is affected when an open circuit fault (OCF) or a short circuit fault occurs. Among these kinds of faults, there is a high prevalence of open circuit faults in MLI. Any fault in MLI must be identified and classified as soon as possible to maintain the reliability of the power supply. This work is focused on developing a Fuzzy Inference System (FIS) for detecting and classifying the open circuit faults in Cascaded H-Bridge Multi-Level Inverter (CHMLI), thereby improving the fault diagnosis accuracy and efficiency. In CHMLI, the gate pulse is generated by pulse width modulation (PWM) technique. The Mamdani Fuzzy Logic Controller (FLC) identifies and categorizes the different OCFs. Fuzzy logic rules are designed for detecting and classifying open circuit faults simultaneously using the fundamental Discrete Fourier components of voltage and current. Several combinations of open circuit faults have been studied in different switches of the MLI, along with the effect of fault inception angle. Furthermore, the test results support the feasibility of the proposed fuzzy-based fault diagnosis and classification scheme in a practical context. A real-time simulation obtained with the help of FPGA-based OPAL-RT 4510 demonstrates the robustness and effectiveness of the designed topology. All types and fault locations are considered in multiple cases of switch failure.

Design and Analysis of Universal Natural Fault-Tolerant SVPWM Strategy With Simplified Fault Diagnosis for Multiphase Motor Drives

Abstract: Conventional space vector pulse width modulation (SVPWM) would degrade the operating performance of the multiphase motor drives under open-phase fault. To resolve this issue, existing solutions require the reconfiguration of transformation matrices. However, for the variable positions of faulty phases, the structure of matrices needs to be redefined, which is a restraint to the industrial implementation of such technologies. This study examined the offset of the voltage vectors after fault occurrence. Accordingly, a universal fault-tolerant SVPWM strategy is proposed for the purpose of natural fault-tolerance, as well as a simplified fault detection scheme. Compared with conventional fault-tolerant SVPWM, it is highlighted with the characteristics of minimum reconfiguration, torque-ripple free operation, and applicability to handle various types of open-phase faults using a single transformation matrix. Besides, proposed strategy eliminates the redundant controllers and also reduces the degree of difficulty for fault diagnosis. Finally, the effectiveness of proposed approach is evaluated experimentally with a seven-phase induction machine, which shows that the torque ripple could be reduced by about 40% in the presence of multiple faults compared to the conventional SVPWM strategy.

Research on fault diagnosis and comprehensive suppression function of airborne system

Abstract: At present, the aircraft fault decision-making function only deals with a single fault, but the aircraft fault has concurrency. The existing aircraft fault decision-making function lacks the ability to deal with multiple fault concurrence situations. How to trace the source of multiple fault alarm information and excavate the original fault is of great significance for simplifying the alarm display and improving the pilot’s fault handling efficiency. In this paper, a fault diagnosis and comprehensive suppression function is designed, which consists of a fast fault location method based on prior knowledge and a comprehensive diagnosis and suppression method based on fault tree knowledge. The fast fault location method based on prior knowledge is based on case reasoning, which writes the past troubleshooting cases and many elements into the fault case base. When new faults occur, the matching degree of similar cases in the case base is obtained through retrieval model, so as to quickly obtain the current fault processing method. The comprehensive diagnosis method based on fault tree knowledge converts the fault tree into a binary decision diagram, and uses Huffman coding to realize computer programming. The probability of each cut set event in the binary decision graph is the probability product of its contained bottom event, so as to determine the risk degree of the failure to locate the cause of the failure. The fault sup-pression method classifies and processes the alarm information when multiple faults occur in a single system and multiple faults occur in multiple systems. The original fault and derivative fault are filtered by using the fault correlation value, the original fault is displayed, and the corresponding derivative fault is suppressed. The fault diagnosis and comprehensive suppression function of the aircraft airborne system designed in this paper provides sup-port for the development of the large aircraft alarm system.

Research on Open Circuit Fault Location and Fault Tolerant Control of Inverter Power Transistor

Abstract: As the key equipment of power conversion, the stability of the inverter directly affects the working state of the equipment. Aiming at the open circuit fault of the power tube, this paper designs a fault diagnosis and fault tolerance scheme of a three-phase bridge inverter based on the current signal. Firstly, the amplitude and phase characteristics of the current vector are used to judge the fault and fault location of the power tube. Then the output fault characteristic signal is converted into a fault-tolerant control signal. The fault leg is switched into a fault-tolerant leg under fault-tolerant control of the inverter bridge. The simulation results show that the diagnosis method proposed in this paper can judge the health and fault status of the inverter with high accuracy and achieve fault tolerant control, improving the stability of the inverter.

Researched on Fault Location of Aircraft Distribution Lines Based on the Petri Net and Precise Integration

Abstract: Aiming at the problem that the distributed power grid is embedded in the power distribution system, which leads to the difficulty of system fault location, this paper proposes a line fault analysis mode based on petri nets. preprocessing. On this basis, the double-ended electrical signal of the faulty line is collected as the input signal of the fine integration method. Through the four-level Longo-Kuta iterative calculation, some and only the voltage values of the line at the fault point are equal, so as to realize the voltage value of the fault point. accurate locating. The fine integral fault location method based on petri net is feasible. In order to verify the fault location capability of this method, this paper establishes a single-phase line model with distributed power sources on the MATLAB platform, sets up ground faults, and conducts simulations. The simulation results show that the distribution network fault location model can accurately locate the line when the ground fault occurs in the system; at the same time, when the fault information is incomplete, the fault line can also be determined by the petri network fault model, and the precise integration method can achieve accurate location.

Fault diagnosis and fault‐tolerant control for bilinear stochastic distribution systems with actuator fault

Abstract: In the paper, the problem of fault diagnosis and fault-tolerant control for the bilinear stochastic distribution system with actuator fault is studied. Firstly, based on a grain processing device, a bilinear stochastic distribution system model is established. Then, a new unknown input observer is designed to diagnose the fault, and a model-predicted fault-tolerant controller is designed for the system on the basis of the estimated fault information, in order to keep the system stable or still maintain the control performance after the fault occurs. Finally, the validity of the proposed algorithm is verified by the grain processing process.

Design A Combinational Circuit Consists of 10 Logic Gates Using Quartos

Abstract: Fault models are designed to forecast the expected behaviors (reaction) from the IC when faults are present, and this is what allows automated test pattern generation (ATPG) software to generate the test patterns. In order to identify these faults at every node in the circuit, the ATPG tool first consults the fault models to establish the patterns that will be necessary for doing so. In this article, ten logic gates are developed using the Quartos program to test for faults in a circuit, and the output waveform was simulated to determine the outcomes of the fault-free output using a variety of test patterns. This fault-free output is compared to the output when a fault injection occurs. It may then be determined if the various test patterns can be utilized to discover the fault in the simulation. The findings demonstrated that the fault can be discovered flawlessly.

Fault logic circuit design of switching power supply based on soft reference circuit module

Abstract: The basic idea of the circuit fault diagnosis method based on fault logic is to establish a computer expert database system first, connect the computer to the GPIB interface card, and then connect each intelligent instrument through the GPIB standard bus. Through the connection of the tested object adapter and the tested object, a circuit fault expert diagnosis and test platform are formed. Based on the traditional fault simulation model, this paper improves the fault logic circuit of switching power supply, designs the system model of fault simulation, deals with the logic structure and circuit hierarchy respectively, and produces the final output through the logic gate circuit by comparing the results of the fault logic circuit and the fault-free logic circuit. The designed switching power supply fault logic circuit works in the switching state, which has the characteristics of low loss and high energy conversion efficiency. Through the simulation analysis, the output is connected to the comparator, which plays the role of the whole wave. The simulation results are in line with the parameter indicators. The functional test results of the chip show that the function of the module is basically normal, which has practical application value and good portability and can be used as a reference for similar electrical designs.

Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults

Abstract: In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.

Analog circuit fault analysis and feature extraction using hardware in loop simulation methodology using LabVIEW

Abstract: Analog circuits behavioral analysis are quite complex in comparison to digital circuits due to its non linear characteristics, making it difficult during fault analysis. Extracting the fault features will simplify such difficulties. Simulation Before Test (SBT) using computer-aided simulators is one of the traditional methods widely used in fault analysis. Due to computation and runtime limitations, this paper discusses fault analysis and feature extraction, using Hardware-In-Loop (HIL) simulation technique with a computer-based instrumentation tool. The circuit under test is modeled using a first-order differential equation, and output responses are derived using a numerical method-based differential equation solver and deployed in a Real-time Field Programmable Gate Array (FPGA) target for real-time simulation using LabView. Catastrophic failure is a major fault that occurs commonly in analog circuits. This paper concentrates on the circuit modeling and fault injection (simulation) in a DC-to-DC converter at steady state condition, to extract the fault feature using a real-time interactive HIL simulator. The fault scenarios are injected and the fault output responses are analyzed by comparing with a real-time fault output response.

Fault Line Selection and Section Location in Distribution Network Based on Sparse Representation

Abstract: Distribution network lines are widely distributed with high failure probability. Accurate identification of fault lines and fault areas can shorten the troubleshooting time. Based on the time-domain information of three-phase current(TPC) and zero-sequence current(ZSC) before and after faults, a fault line selection(FLS) and fault section location(FSL) method based on sparse representation is proposed, which is applicable to all kinds of short-circuit faults in distribution network. The proposed method does not rely on Shannon Theory for fault current collection. The TPC and ZSC of each feeder of the bus and each measuring point of the fault line before and after the fault are collected, and spliced according to the sequence of A phase, B phase, C phase and zero sequence. The K-Singular Value Decomposition algorithm (K-SVD) is used for dictionary learning to construct an over complete dictionary that accurately matches the fault current characteristics; The Orthogonal Matching Pursuit algorithm (OMP) is used for adaptive dictionary sparse decomposition. The analysis shows that the maximum sparse coefficients (MSC) of fault line (FL) and non-fault line (NFL), fault section (FS) and non-fault section (NFS) fault current splicing signal have different characteristics. Therefore, the MSC of fault current splicing signals at each feeder of the bus bar and each measurement point of the fault line is used to construct the fault line selection and fault location criteria. MATLAB/Simulink simulation verifies the effectiveness of the proposed method.

Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML

Abstract: Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.

Digital integrated circuit test generation algorithm based on neural network technology

Abstract: As digital integrated circuit(IC) design techniques and corresponding product manufacturing processes improve, more and more electronic products are moving towards miniaturisation and high concentration, but this is what makes circuit test generation difficult. Since there is a strong link between digital IC test generation and fault diagnosis of digital systems, neural network techniques are applied to fault diagnosis to enable test generation algorithms to accomplish goals such as fault activation and fault propagation. This paper is based on a neural network algorithm to solve circuit fault test sets, generate test vectors and simulate the fault detection process until all fault points have been detected. By comparing the circuit test simulation experiments of the neural network algorithm and the traditional algorithm, it is found that the number of circuit faults increases and the test time of the neural network algorithm is longer than that of the traditional algorithm, but the fault coverage rate is higher than that of the traditional algorithm, indicating that the neural network algorithm can effectively improve the correct fault detection rate although it increases the test time.

Precise Fault Location in Active Distribution Systems Using Unsynchronized Source Measurements

Abstract: This article presents a phase-domain based fault location algorithm for unbalanced power distribution systems with distributed generation. Unsynchronized voltage and current measurements, taken from the sources prior and during the fault, are utilized to calculate the equivalent source impedances and the incremental current quantities required. The latter are substituted into the fault location equations, which are solved to determine the fault resistance(s), the synchronization angles, and the possible fault positions. Multiple possible fault position estimations are eliminated by applying an index-based methodology to find the exact fault position. Extensive simulations are performed for a benchmark distribution system with conventional and inverter-interfaced generation units, considering all types of common short-circuit faults, several fault resistance magnitudes, varying load levels, different distributed generation (DG) mixture, and the impact of line segment length. The results show that fault distance is accurately estimated by the proposed algorithm.

A Novel Fault Identification and Localization Scheme for Bipolar DC Microgrid

Abstract: Traditional fault detection and location schemes become ineffective for detecting and locating the fault in DC microgrid systems due to integrating various types of power electronic-based DC loads and generators. To resolve this issue, advanced, intelligent, specialized fault detection and location schemes are necessary. This article suggests a novel fault detection scheme based on the difference in the teager energy available in the DC current wave at sending and receiving ends of lines. After the detection of the fault, the location is calculated by estimating the resistance of the cable up to the fault point as well as the total resistance of the cable, with the help of least square technique. The proposed scheme decides fault is internal if the estimated fault location is less than 1 p.u. Otherwise, the proposed scheme decides fault is external. A DC microgrid with different types of generating units and loads is simulated using MATLAB/SIMULINK to evaluate the developed algorithm. Internal and external faults, pole-to-ground and pole-to-pole faults with changing fault resistance and fault location are some of the fault scenarios that have been simulated. The obtained simulation results prove that the suggested algorithm can discriminate between internal and external faults and locate the fault. The proposed technique is also examined on a DC microgrid hardware testbed and results show the efficiency of the suggested approach.

FIS Based Fault Identification and Classification in IEEE RTS96 System

Abstract: This paper proposes fuzzy inference system (FIS) to identify and classify power transmission line faults. Testing of the suggested fuzzy logic-based protection strategy has been conducted for a variety of fault scenarios and fault parameter ranges. When a fault arises in the power system, current waveforms are altered by fault conditions, and the magnitude varies based on the type of fault. The measured instantaneous current and zero-sequence current signals are processed for identification of fault. The findings demonstrate that the proposed method is effective at determining and grouping the class of fault in an IEEE RTS96 system. The analysis has determined the effectiveness of the suggested system for identifying line faults.

Enhanced two-terminal impedance-based fault location using sequence values

Abstract: Fault at transmission line system may lead to major impacts such as power quality problems and cascading failure in the grid system. Thus, it is very important to locate it fast so that suitable solution can be taken to ensure power system stability can be retained. The complexity of the transmission line however makes the fault point identification a challenging task. This paper proposes an enhanced fault detection and location method using positive and negative-sequence values of current and voltage, taken at both local and remote terminals. The fault detection is based on comparison between the total fault current with currents combination during the pre-fault time. While the fault location algorithm was developed using an impedance-based method and the estimated fault location was taken at two cycles after fault detection. Various fault types, fault resistances and fault locations have been tested in order to verify the performance of the proposed method. The developed algorithms have successfully detected all faults within high accuracy. Based on the obtained results, the estimated fault locations are not affected by fault resistance and line charging current. Furthermore, the proposed method able to detect fault location without the needs to know the fault type.