Simultaneous Faults Research Articles

Multi-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition

Aiming at compound fault recognition, multi-label learning easily has a strong comprehension on relevance between simultaneous mechanism faults, such as bearing defect fault and tool wear fault. Moreover, compared with single data source, multiple data sources can more fully monitor the working status of equipment. Consequently, this paper proposes a multi-source information fusion (MSIF) feature to train the multi-label deep reinforcement learning (ML-DRL) model, and develops a multi-source information fusion deep self-attention reinforcement learning (MSIF-DSARL) framework. Firstly, compound fault samples with multiple data sources are transformed into 3D wavelet coefficient tensors. Then the MSIF features are extracted from 3D tensors, using a position self-attention fusion (PSAF) module and a channel self-attention fusion (CSAF) module. Especially, the PSAF module can excavate the internal time–frequency information in every source, and the CSAF module can integrate the information differences between multiple sources. Finally, the ML-DRL model is trained with the MSIF features. In a laboratory experiment and an engineering application, diagnostic results demonstrate powerfully that the proposed framework has better superiority and practicability in recognizing compound fault, than present popular multi-label learning methods.

Machine learning-based fault diagnosis for research nuclear reactor medium voltage power cables in fraction Fourier domain

Fault diagnosis of Medium Voltage power Cables (MVCs) research nuclear reactor, incredibly inaccessible/remote ones, has to be carefully identified, located, and fixed within a short time. Therefore, this paper proposes a perfect simultaneous fault diagnosis scheme based on Multiclass Support-Vector Machine (MCSVM) in the fractional Fourier domain. First, the three-phase sending currents are simulated under different conditions then their features are extracted using Discrete Fractional Fourier Transform (DFRFT). Afterward, the features reduction process occurs via the Singular Value Decomposition (SVD) approach. MCSVM scheme is used to diagnose faults (i.e., discover, categorize, and trace) using reduced features outcome from DFRFT and SVD stages. Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP) simulations have been carried out for 22 kV unreachable MVC. Different kernels of SVM, i.e., linear, quadratic, or polynomial, and diverse factors of DFRFT, i.e., α, are investigated in simulations to obtain the optimum performance (i.e., best α and kernel pair). Hence, performance analysis of the proposed diagnosis method under different conditions (i.e., various fault resistances, locations, and inception angles) concluded two highest accuracy and lowest time settings, which were found at α = 0.5 (for both) quadratic kernel, and linear kernel, respectively. Moreover, the linear kernel achieves 99.8% accuracy rate, the lowest execution time (10 ms), and fault tracing error rate of 0.525789%, which is proper for real-time applications. Besides, our proposed method is more reliable and accurate against variable operating conditions (fault resistances, distances, and inception angles), leading to more reliable power production systems.

Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool

This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods.

Simultaneous fault detection and control design for DC–AC converter with a neutral leg based on dynamic observer

In this paper, the problem of simultaneous fault detection and control (SFDC) for a 3-phase 4-wire converter with neutral bridge arm, which is commonly used in the application of smart grid and distributed generation, is considered. By employing a detector/controller module consisting of a dynamic observer and a state feedback controller based on the dynamic observer, a multi-objective H∞ optimization SFDC framework is established to achieve the fault detection and control objectives simultaneously. Extended linear matrix inequalities (LMIs) characterizations are obtained as the strict conditions for solvability of the SFDC problem. The proposed method can reduce the designing conservativeness and overcome the shortcoming of existing SFDC methods by applying the superiority of dynamic observer. The achievable performance is analyzed quantitatively and the effectiveness of our designing method was illustrated and verified both in simulation and the hardware-in-the-loop(HIL) simulation which is conducted in a real-time simulation platform built by StarSim and MT respectively.

Generic approach to calculation of short circuit currents in power transformers

Power transformer in service is exposed to various voltage and current stresses. The ability to withstand short circuit is an essential requirement for power transformers. There are different types of short circuit: single - phase to earth, double - phase with or without simultaneous earth fault and three - phase short circuit. These various short circuit conditions result in different stress conditions for the windings. Mesh analysis and symmetrical components are the two methods most commonly used for determining the magnitude of short circuit currents. In this paper, both methods will be presented with results compared on a real-case transformer. Also, a generic scheme using the symmetrical components approach is designed in order to standardize the short-circuit currents calculation for all power transformer types and to reduce the time required for obtaining results.

Hierarchical Structure-Based Fault-Tolerant Tracking Control of Multiple 3-DOF Laboratory Helicopters

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

Multiple Open-Circuit Fault Diagnosis Method in NPC Rectifiers Using Fault Injection Strategy

This article proposes an open-circuit fault diagnosis method in power semiconductors of three-level neutral-point-clamped (3L-NPC) rectifiers by using a fault injection strategy. To enhance the anti-interference ability of the diagnosis method, an improvement is made in the traditional calculation method of phase-to-phase pole voltage residuals by selectively adopting the quantifying operation according to the current level. Then, the normalized and quantified residuals are obtained as the diagnosis variables. All signals for calculations are derived from the controller and pre-existing sensors, avoiding the requirement of extra hardware. To improve the diagnosis accuracy and speed, a fault injection strategy is designed. A preidentification method provides guidance for choosing the type of fault to inject. On this basis, the fault injection is implemented by revising the switching signals sent from the modulation module for a short time. In this way, inner-switch faults and outer-switch faults are easier to be differentiated, and the misdiagnosis of simultaneous multiple-switch faults is avoided. Therefore, the proposed diagnosis method is accessible to detect single-switch faults as well as multiple-switch faults in 3L-NPC rectifiers more accurately and fast. The experiments are carried out to confirm the effectiveness and the robustness of the proposed fault diagnosis method.

Analysis of Simultaneous Broken and Fallen to Earth Conductor Fault Condition in Power Systems

In power systems, analysis of critical faults, mainly, different types of simultaneous faults on transmission lines, is a sensitive issue. To identify the accurate fault place during a simultaneous fault condition, all major algorithms fail to provide the desired results. In this paper, an analysis of simultaneous faults called broken and fallen to earth conductor fault conditions is carried out. With the help of an equivalent sequence components network, generalized impedance equations are derived to determine the accurate fault place on the transmission line during the simultaneous broken and fallen to earth conductor’s fault condition. For authentication of the derived method, simulation has been done using MATLAB tool with two different medium transmission lines. Simulation outputs manifest the accuracy of the proposed generalized impedance equations by showing 98% accuracy for identifying precise fault places. The derived fault analysis method uses the sequential network formation technique, which can be easily understood by researchers.

Simultaneous fault diagnosis of proton exchange membrane fuel cell systems based on an Incremental Multi-label Classification Network

Fault diagnosis plays an important role in the operation of proton exchange membrane fuel cell (PEMFC) systems. In some certain working conditions, multiple faults can occur simultaneously. And, to the best of our knowledge, very few studies have yet to design an algorithm specifically for simultaneous fault diagnosis in PEMFC systems. Therefore, a novel simultaneous fault diagnosis algorithm, based on multi-label classifier chain named Incremental Multi-label Classification Network (IMCN), is proposed. To develop and optimize IMCN, a PEMFC model is constructed based on the commercial software AVL CURISE M to simulate data when simultaneous multiple faults occur. To further verify the generalization performance and practical effect of IMCN, a bench experiment using a high-power PEMFC system is conducted, which has similar boundary conditions as the boundary conditions embedded in simulation model. And, whether in experiment or simulation, corresponding verification methods are adopted to verify the success of simultaneous multiple faults embedding. Experimental data testing shows that, the subset accuracy, Hamming loss, Jaccard index, precision and recall of IMCN reaches 0.973, 0.029, 0.921, 0.961 and 0.956 respectively (better than Multi-Label MLP classifier, Label powerset MLP classifier, etc.), and the proposed simultaneous fault diagnosis method has achieved excellent results.

Improved Adaptive Fault-Tolerant Control of a Variable Structure Fighter with Multiple Faults Based on an Extended Observer

In this paper, an improved adaptive fault-tolerant control (FTC) scheme is presented based on an extended state observer (ESO) for a highly maneuverable variable structure fighter (VSF) with simultaneous faults of the rudder surface and sensor. The feedback linearization model of the attitude system with faults and external disturbances is introduced. In order to realize the real-time observation of the rudder and sensor faults, a neural network estimator is used to approximate sensor faults, and the ESO is designed to estimate the rudder surface fault and attitude variables. On this basis, the estimated value is used to replace the measured value for state variables to design the adaptive fault-tolerant controller for the inner and outer loop, combined with the second-order command filter to offset the phenomenon of backstepping differential amplification system noise. Finally, the proposed FTC scheme realizes the variable structure self-repairing of multiple positions, multiple types of faults in VSF using fault estimation. The Lyapunov theory and Barbalat’s lemma prove the stability of the designed observer and controller. The effectiveness of the FTC scheme is verified by a simulation experiment.

Decision-Making Under Uncertainty on Preventive Actions Boosting Power Grid Resilience

The growing impact of weather-related power outages on economy and society in the last decades underlines the rising need for power system resilience. Power system resilience can be boosted through adoption of probabilistic approaches and preventive actions building on smart grid capabilities. Decisions on the best-performing preventive action, however, are nontrivial and must consider the expected impact of an upcoming event, weather forecasts, fault probabilities, and their corresponding uncertainties. This article presents a three-stage decision-making methodology that is based on assessing weighted preevent and postevent performance loss and considers spatial uncertainty of fault probabilities, modeled by probability distributions. The methodology is demonstrated using preventive actions, such as additional network constraints and islanding, aiming to mitigate cascading failures in transmission networks. Their performance loss is compared to the traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -1 criterion. Simultaneous faults of up to three lines are considered as initiating cause in the IEEE 30-bus network and the 489-bus German transmission network to verify potential and scalability of the methodology. Results show that the decision-making methodology effectively identifies the best-performing action to reduce the risk of cascading failures for any level of uncertainty.

Simultaneous reduced‐order control and fault detection for discrete‐time switched systems with relaxed persistent dwell time regularity

AbstractThis paper focuses on the problem of simultaneous control and fault detection (FD) for discrete‐time switched systems. The main goal is to develop a control/detection unit (CDU) associated with a switching law to control the system and detect faults simultaneously. When the switched systems are with partial measurable states, we directly use these states to construct partial control and FD signals. Next, the reduced‐order CDU is designed to generate the other control and FD signals. Compared with existing results based on full‐order CDU, the proposed results lead to less conservatism and reduce design complexity. The switching law is constructed in the frame of persistent dwell time (PDT) switching. A novel switching number constraint condition is introduced, which further relaxes the restrictions on the dwell time of switching processes of PDT. The less restriction on dwell time degrades the performance requirement of each subsystem and upgrades the degree of freedom for switching law design. Based on the proposed results, a class of nonweighted performance indexes is introduced to characterize the fault sensitivity and robustness. Finally, the effectiveness of the proposed method is illustrated by an example.

A Dual w-Test Based Quality Control Algorithm for Integrated IMU/GNSS Navigation in Urban Areas

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively.

Whole‐line fault analysis method for unbalanced distribution networks with inverter interfaced distributed generators

The integration of the inverter interfaced distributed generators (IIDGs) challenges the conventional fault analysis in distribution networks. However, the existing analysis methods mainly focus on the fault occurring at the network buses and little research has been done to analyse the whole-line faults. To extend the fault analysis methods, this paper proposed a phase domain estimation method. The new method was designed to analyse the whole-line faults in unbalanced distribution networks, considering the control strategy and fault-ride-through (FRT) capability of IIDG. A canonical method was developed and a compensation method was utilised, avoiding constructing the entire bus matrix and predefining the fault boundary conditions. Moreover, the new method is robust and can be used to analyse the simultaneous faults as well. Finally, the proposed method was programmed in MATLAB and different scenarios were tested on an unbalanced network. The calculation results were examined by the simulations, which verified the effectiveness of the new method. The new method can analyse whole-line fault in unbalanced networks with IIDGs connection, extending the current fault analysis methods and providing foundations for relay protection.

A two‐dimensional RC network topology for fault‐tolerant design of analog circuits

This paper proposes a novel one-port passive circuit topology consisting of a two-dimensional network of resistors and capacitors, which can be used as a fault-tolerant building block for analog circuit design. Through an analytical procedure, the network is shown to follow simple first-order admittance dynamics. A Monte Carlo method is employed to describe the effect of simultaneous faults (short or open circuit) in random network elements in terms of confidence bounds in the frequency-domain admittance profile. Faults in 10% of the elements resulted in only minor changes of the frequency response (up to 3.9 dB in magnitude and 12.5 ∘ in phase in 95% of the cases). An example is presented to illustrate the use of the proposed RC network in the fault-tolerant design of a low-pass filter.

Numerical and experimental study on quantitative assessment of multiple fault parameters in a warped internally damped rotor with a transverse fatigue crack integrated with an active magnetic bearing

This work aims to apply experimental data in the multi-fault identification algorithm developed for an internally damped warped cracked rotor system integrated with an auxiliary active magnetic bearing (AMB).A quantitative assessment of additive stiffness due to crack, unbalance, and residual bow with their orientations, multiplicative fault due to residual bow and crack, external and internal (rotating) damping, and AMB parameters that affect the dynamics of the system is done. The rotor experiences multiple harmonics in rotation due to the effect of crack and multiplicative bow-crack fault. To overcome the drawback of removal of residual bow at only 1X harmonic in signal processing, the present work addresses the simultaneous estimation of crack, bow, and multiplicative faults. Measurements of vibrational signalsof shaftand control current from PID controller are taken from the rotordynamic test bench. The multi-harmonic full spectrum amplitudes and phases of forward and backward whirl extracted from the measured test data are fed to theidentification algorithm which estimates fault and system uncertain parameters of fatigue cracked rotor system.This technique can effectively identify simultaneous rotor faults with prior knowledge of some characteristics of the rotor system. The novelty of this work is the identification of shaft residual bow with additive stiffness of crack where the additive crack stiffness is the direct indicator of crack and the magnitude of residual bow is of the permanent bend in rotor defining their severity.

Simultaneous fault diagnosis for aircraft engine using multi-label learning

Fault detection and isolation system is crucial for the safety and reliability of aircraft engine. Traditional techniques of data-driven fault diagnosis for aircraft engine mainly focus on single fault diagnosis problems by means of the single-label learning strategy. However, the simultaneous fault diagnosis problems cannot be ignored in reality. In this research, two data-driven approaches based on multi-label learning and support vector machine are proposed to address the simultaneous fault diagnosis for an aircraft engine. Given that the simultaneous fault data are more difficult to obtain than single fault data, the proposed approaches have the ability to diagnose both single fault and simultaneous fault for aircraft engine when the fault diagnosis system is trained using single fault data only. The experimental results show that the proposed approaches can diagnose the simultaneous fault for an aircraft engine with high accuracy requiring low computation burden and a small number of single fault training data. In addition, the supplementary experiment confirms that the diagnosis accuracy of the proposed methods can be further improved by adding a small amount of the simultaneous fault data into the training dataset.

A novel robust fault estimation observer design for semi‐Markovian jump systems with partially bounded transition rate

AbstractThis article addresses the simultaneous fault estimation for a class of semi‐Markovian jump system with partially bounded known transition rates. A new variable is introduced with a dependence on output information, and then two robust variable‐based observers are designed to gauge the actuator fault and sensor fault concurrently. Significantly, the problem for partially bounded known transition rates is solved by virtue of mathematic techniques. In this way, the existing conditions of the designed observers are given in terms of linear matrix inequality. Furthermore, two examples are implemented to demonstrate the feasibility and effectiveness of the scheme.

Detection and experimental investigation of open conductor and single-phase earth return faults in distribution systems

This paper presents a new and reliable detection approach for different crucial faults in distribution systems. These faults are one-open conductor faults at the far side of the feeder or simultaneous faults that are an open conductor followed by an earth fault at either the source side or load side (downed conductor-return faults). Under downed conductor-return faults, there is no zero-sequence current at the conventional protection point. Furthermore, the negative-sequence component is less than the normal unbalance component of the conventional protection point under one-open conductor faults at the far side of the feeder. Therefore, the presented detection approach is based on the measurements at the low voltage side of the load transformers. The proposed method detects such faults by monitoring only the magnitude of the measured three-phase voltage at the load side without installing voltage transformers. A robust threshold value is proposed to guarantee dependability against all fault conditions and security against the normal unbalance. Also, the security of the proposed approach against external faults (at the low voltage side) is verified by monitoring the magnitude of the three-phase current. Fortunately, the proposed system identifies the faulted section. The faulted feeder isolation and the faulted section isolation are accomplished by a proposed autonomous control method with the aid of the infrastructure of smart grids (available communication system). Extensive investigations are done based on a detailed simulation of the selected distribution feeder using the ATP-EMTP program. For verification purposes, recorded faulted cases are experimentally applied on a real distribution feeder to visualize such fault conditions and confirm the detection impossibility of earth-return faults by conventional protection, significantly if the fault faraway the substation. On the other hand, all applied test results corroborate the efficacy of the proposed detection method under the considered faults.

Multiple-Actuator Fault Isolation Using a Minimal ℓ1-Norm Solution with Applications in Overactuated Electric Vehicles.

A multiple-actuator fault isolation approach for overactuated electric vehicles (EVs) is designed with a minimal -norm solution. As the numbers of driving motors and steering actuators increase beyond the number of controlled variables, an EV becomes an overactuated system, which exhibits actuator redundancy and enables the possibility of fault-tolerant control (FTC). On the other hand, an increase in the number of actuators also increases the possibility of simultaneously occurring multiple faults. To ensure EV reliability while driving, exact and fast fault isolation is required; however, the existing fault isolation methods demand high computational power or complicated procedures because the overactuated systems have many actuators, and the number of simultaneous fault occurrences is increased. The method proposed in this paper exploits the concept of sparsity. The underdetermined linear system is defined from the parity equation, and fault isolation is achieved by obtaining the sparsest nonzero component of the residuals from the minimal -norm solution. Therefore, the locations of the faults can be obtained in a sequence, and only a consistently low computational load is required regardless of the isolated number of faults. The experimental results obtained with a scaled-down overactuated EV support the effectiveness of the proposed method, and a quantitative index of the sparsity condition for the target EV is discussed with a CarSim-connected MATLAB/Simulink simulation.