Fault Isolation Research Articles

Impact of cyber failures on operation and adequacy of Multi-Microgrid distribution systems

The primary objective of this paper is to create a suitable reliability assessment framework for Cyber–Physical Multi-MicroGrid (CPMMG) distribution systems, considering the device-level failures of control and protection systems. For this purpose, the typical structure of a CPMMG distribution system with a centralized protection system is first exemplified. Then, the interdependencies between cyber and power infrastructures are investigated in such systems. Regarding the protection system, a Markov model is presented to investigate hidden failures of fault location, isolation, and service restoration processes. Possible operation modes in a CPMMG distribution system – normal, islanding, joint, and shutdown modes – are then explained and modeled. Finally, a suitable Monte Carlo simulation-based reliability assessment framework is developed to quantify well-known reliability indices. In addition, two new adequacy indices are proposed: Interrupted but Gained Compensation (IbGC) and Supplied by Expensive Resources (SbER). A comprehensive case study is conducted to reveal the salient features of the proposed framework. The result showed that the impact of cyber failures in such systems is highly dependent on the design of the cyber system. Therefore, the adverse impact of cyber failures of control and protection systems can be effectively mitigated by proper design.

Enhanced EBAC localization of gate oxide defects after high voltage electron beam irradiation

Electron Beam (EB) irradiation with high acceleration voltage is widely reported to have resulted in significant degradation on transistor parametric performances. Therefore, high voltage EB should be avoided during nanoprobing transistor device characterization. There are many studies that mainly emphasize on prevention, cause, and consequences of such detrimental effects but rarely focus on the exploitation of these EB effects. In fact, high voltage EB could possibly leverage to enhance nanoscale fault isolation. In this paper, it was discovered that high voltage EB effects can be utilized to enhance the EBAC localization of Gate Oxide breakdown defects. Three successful case studies are described to demonstrate these enhancements on P-type and N-type MOSFET gate oxide defect isolation through the suppression on the gate leakage by high voltage EB induced charge trapping mechanism.

Deep Learning-Based Robust Actuator Fault Detection and Isolation Scheme for Highly Redundant Multirotor UAVs

This article presents a novel approach for detecting and isolating faulty actuators in highly redundant Multirotor UAVs using cascaded Deep Neural Network (DNN) models. The proposed Fault Detection and Isolation (FDI) framework combines Long Short-Term Memory (LSTM)-based fault detection and faulty actuator locator models to achieve real-time monitoring. The study focuses on a Hexadecarotor multirotor UAV equipped with sixteen rotors. To tackle the complexity of FDI resulting from redundancy, a partitioning technique is introduced based on system dynamics. The proposed FDI scheme is composed of a region classifier model responsible for detecting faults and fault locator models that precisely determine the location of the failed actuator. Extensive training and testing of the models demonstrate high accuracy, with the regional classifier model achieving 98.97% accuracy and the fault locator model achieving 99.107% accuracy. Furthermore, the scheme was integrated into the flight control system of the UAV, before being tested via both real-time monitoring in the simulation environment and analysis of recorded real flight data. The models exhibit remarkable performance in detecting and localizing injected faults. Therefore, using DNN models and the partitioning technique, this research offers a promising method for accurately detecting and isolating faulty actuators, thereby improving the overall performance and dependability of highly redundant Multirotor UAVs in various operational scenarios.

A Coordinated Reconfiguration Strategy for Multi-Stage Resilience Enhancement in Integrated Power Distribution and Heating Networks

The tight coupling of power distribution system (PDS) and district heating system (DHS) makes it imperative to devise a coordinated strategy for the resilience enhancement of integrated electric and heating system (IEHS) in natural disasters. The PDS reconfiguration serves as a critical tool for service restoration by the optimal adjustment of PDS topology with designated tie and sectionalizing switches. Similarly, the DHS reconfiguration provides a viable solution for countering disasters by the optimal adjustment of heating supply topology with targeted tie and sectionalizing valves in DHS. In this paper, a multi-stage resilience enhancement strategy considering the coordinated reconfiguration of PDS and DHS is proposed, which coordinates degradation, fault isolation, and service restoration stages after disasters. The proposed model is formulated as a mixed-integer nonlinear problem, which addresses the coordinated regulation of PDS switches and DHS valves to achieve efficient fault isolation and provide a better service restoration. To make the proposed model tractable, a linearized Distflow model is proposed by introducing current-oriented auxiliary variables and the approximation of power flow and voltage drop equations. Extensive case studies are conducted to validate the efficiency and accuracy of the proposed model and illustrate the performance of coordinated reconfiguration of PDS and DHS on resilience enhancement.

LPV Model-Based Fault Detection and Isolation in DC Microgrids Through Signature Recognition

LPV Model-Based Fault Detection and Isolation in DC Microgrids Through Signature Recognition

Distributed model predictive control based on the alternating direction method of multipliers for branching open canal irrigation systems

Most studies of open canal automation concentrated on the control of single canal pool or cascaded multiple canal pools, neglecting the hydraulic coupling effects between the main canal and lateral canals. Low transmission efficiency and poor equity of agriculture water delivery may be caused in the practical application of large-scale irrigation networks. Considering the high computation and communication requirements for large-scale operating systems, a distributed model predictive control (DMPC) algorithm based on the alternating direction method of multipliers (ADMM) is developed for the branching open canal irrigation systems (BOCISs). A simplified BOCIS example, originating from Test case 2 proposed by the American Society of Civil Engineers, is used for control tests. Moreover, for control performance comparisons and evaluations, centralized model predictive control (CMPC) is employed by modifying the state space of the integrator delay (ID) model. The results show that the control performance of the proposed DMPC converges to the global optimal solution of the CMPC under the normal scenario, and the degradation is less than 0.35 %. The communication and coordination process of DMPC contributes to more powerful robustness and interference immunity than CMPC. Furthermore, in case of sudden accidents occurring in some subsystems, the proposed DMPC is more convenient and timely to execute fault isolation. The improvements on water level oscillations and overshoots can reach 20.3 % and 52.1 % in the other subsystems which need normal water delivery, respectively. Equipped with outstanding control characteristics, the proposed ADMM-based DMPC framework enables the water authorities of large-scale BOCISs to promote surface water distribution in a practicable, flexible, and secure way, showing great potential in developing intelligent irrigation districts.

An independent component analysis based correlation coefficient method for internal short-circuit fault diagnosis of battery-powered intelligent transportation systems

Battery-powered intelligent transportation systems (BPITS) are growing at an alarming rate, and a problem that cannot be ignored is that the safety of battery packs is currently the weak link in the security of BPITS. However, developing a fast and reliable diagnostic method is not easy due to the large number of battery cells and the difficulty to guarantee consistency among the individual cells. To better resolve this problem, a new fault diagnosis scheme based on voltage correlation coefficient (VCC) and independent component analysis (ICA) model is proposed in this paper. Where VCC is used to extract the fault signature of the battery pack independent of the cell inconsistency; and ICA is used to model the non-Gaussian distributed high-dimensional VCC signals in parallel and derive a statistical-based fault detection index. The kernel density estimation-based algorithm is used to calculate the theoretical threshold and to determine the optimal window width for VCC. In particular, a variable reconstruction-based fault isolation algorithm is proposed for accurate and reliable determination of the location of the faulty cell to prevent the smearing effect. Experimental results conducted on a real battery testbed under different conditions and configurations show that the new method provides significant improvements in both detection latency and positioning reliability in diagnosing internal short-circuit (ISC) faults with different failure grades.

Robust Gas-Path Fault Diagnosis with Sliding Mode Applied in Aero-Engine Distributed Control System

The technology of aero-engine gas-path fault diagnosis is an important way to improve flight safety and reliability and reduce maintenance costs. With the maturity of the new-generation engine distributed control system (DCS), uncertainties such as bus packet loss, time delay, and node function degradation have increasingly highlighted new challenges to engine fault diagnosis. At present, linear Kalman filter (LKF) is widely researched and used in engine fault detection and isolation (FDI), but its robustness has proved to be not strong. However, the sliding mode observer (SMO) is not only capable of fault reconstruction but also robust to system uncertainties and disturbances due to its unique discontinuous switching term, which tends to be an effective way to achieve robust fault diagnosis for aero engines and DCS with many uncertainties. This paper initially develops a distributed bus packaging model that supports time-delay and packet-loss simulating and timing planning based on SimEvents, providing a basis for the model-based design and verification. Then the SMO is adopted to design a robust gas-path diagnosis method for engine DCS, and the robust observing accuracy is improved by combining high-order sliding mode theory, LMI optimized observation matrix, and variable gain. The simulation results show the effectiveness and advantages in engine DCS application scenarios.

Fault Diagnosis in Gas Lift System Using PDF Data

Fault detection and isolation in the gas lift system were implemented assuming the gas lift variables are stochastic. Injection valve coefficient (Civ), production choke coefficient (Cpc), annulus pressure (Pa), and wellhead pressure (Pwh) were observed to show variations with faults presence. By simulating these gas lift variables as stochastic, the probability density function (PDF) data were used to generate decision functions for both the detection and isolation of the gas lift valve faults. The scheme accurately detected and isolated faults in the injection valve coefficient (Civ) and production choke coefficient (Cpc). The result of this diagnosis will aid the proper implementation of fault tolerant control in the gas lift system which will lead to its optimal operation.

High Availability and Fault Tolerance in AWS

With the increasing adoption of cloud computing, ensuring high availability and fault tolerance has become paramount for organizations. Amazon Web Services (AWS) offers a robust infrastructure for hosting applications, but it requires careful architectural design and implementation to achieve desired levels of availability and fault tolerance. This research paper explores two innovative concepts, namely Cloud Fractal and Decentralized Replication and Orchestration, and their application in achieving high availability and fault tolerance in AWS. We present a comprehensive analysis of these concepts and provide practical guidelines for their implementation in real-world scenarios. Our findings demonstrate the effectiveness of Cloud Fractal and Decentralized Replication and Orchestration in enhancing the reliability and resilience of AWS deployments. Keywords: Fault Isolation, High Availability, Load Balancing, Auto-scaling, Disaster Recovery, Data Replication, Backup and Restore, Fault Tolerance, Cloud Computing, Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), Amazon Simple Storage Service (S3), AWS Elastic Load Balancer (ELB), Complexity, Operational Management, Cloud Fractal, AWS Lambda, Amazon SQS, Decentralized Replication and Orchestration (DOI).

Disturbance Decoupling for a Single-Phase Pulse Width Modulation Rectifier Based on an Extended H-Infinity Filter

The growing utilization of single-phase pulse width modulation (PWM) rectifiers in various applications has spurred interest in detecting and monitoring faults in these devices. In particular, voltage and current sensors play a crucial role in the control loop of these rectifiers. However, sensor faults can significantly affect the converter’s performance and availability. This paper introduces a novel and efficient method for detecting and decoupling sensor faults in single-phase PWM rectifiers. The proposed method utilizes residual generation and incorporates an extended filter within the rectifier. Unlike conventional filters, the presented fault detection and isolation (FDI) method effectively eliminates the influence of disturbances on the residual signal. This feature helps prevent false alarms in the monitored system, ensuring reliable fault detection. To evaluate the effectiveness of the approach, hardware-in-the-loop and simulation tests were conducted. The results from these tests provide substantial evidence supporting the efficacy of the proposed method. The hardware-in-the-loop experiments involved real-world implementation, validating the practicality and reliability of the approach. Meanwhile, simulation tests allowed for a comprehensive analysis of system behavior and performance under various fault scenarios. The findings demonstrate the rapid and dependable nature of the proposed method for detecting and decoupling sensor faults in single-phase PWM rectifiers. By effectively mitigating the impact of disturbances on the residual signal, false alarms are minimized, ensuring accurate fault detection. The experimental validation highlights the practical applicability and effectiveness of the proposed approach, making it a valuable contribution to fault detection in single-phase PWM rectifiers.

On the design of an unknown input observer to fault detection, isolation, and estimation for uncertain multi-delay nonlinear systems

In this article, an unknown input observer (UIO) is proposed for fault detection and isolation (FDI) for uncertain nonlinear time-delay systems with multiple discrete delays. The presented UIO can be able to isolate both actuator and sensor faults and estimate them to identify the shape, time of occurrence, amplitude, and other features of the faults. Furthermore, the proposed FDI method is robust to both input/state and output disturbances. At first, the system dynamics and faults are augmented and then UIO is driven. A delay-independent Lyapunov–Krasovskii functional is utilized to guarantee the stability of the proposed UIO-based FDI method. The terms of stability and UIO gains are obtained by solving some feasible linear matrix inequalities (LMI). The proposed method is simulated on two interconnected well-mixed non-isothermal continuous stirred tank reactors (CSTRs) with recycling where there are three parallel, irreversible elemental exothermic reactions. Two point-wise time delays in the output temperature and concentration in both CSTRs are considered. Furthermore, parametric uncertainties in the modeling are investigated. The simulation and numerical results show the superiority of the proposed UIO for fault detection, isolation, and estimation in the presence of multiple discrete time delays, model uncertainty, and input/state and output disturbances.

A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity

Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solution integrates bilinear Koopman model realization, deep learning, and bilinear parity-space. A deep neural network realizes a bilinear model, enabling bilinear parity-space sensor fault detection and isolation. This yields a reliable, accurate, and interpretable data-driven framework. The method requires no prior HVAC dynamics knowledge, relying solely on normal operation data. It diagnoses additive, multiplicative, and complete failure sensor faults while minimizing false alarms, even with severe faults. A four-zone HVAC system is simulated in TRNSYS as a case study to demonstrate the performance and efficacy of the proposed approach. The proposed bilinear deep Koopman model realization is utilized to develop a bilinear model for the four-zone HVAC system. The bilinear model is then used for designing the bilinear parity-space. Further, considering various failure scenarios, the proposed sensor fault detection and isolation framework demonstrates promising diagnosis performance. Finally, a comparison is conducted to showcase the advantages of the proposed method over earlier works based on PCA and neural networks.

Research on circuit breaker failure protection and the secondary accelerated fault isolation scheme of VSC-HVDC grids

When the direct current circuit breaker (DCCB) fails to operate, the fault range will further expand, endangering the safe operation of the power gird. Therefore, this study proposes a DCCB failure protection method and a secondary accelerated fault isolation scheme of voltage source converter-based high-voltage direct current (VSC-HVDC) grids. On the basis of considering the influence of DCCB disconnection, characteristics of fault current after the failure of the DCCB are analyzed, and a circuit breaker failure protection method based on “increase current” discrimination is proposed. By analyzing the logical relationship among failed breaker re-tripping, adjacent DCCB action, blocking of the converter station, and AC circuit breaker action, a secondary accelerated fault isolation scheme is proposed to minimize the fault isolation area. The scheme achieves the accelerated isolation of DC faults after the failure of the DCCB and avoids the trip of the AC circuit breaker effectively. Simulation results verify the effectiveness of the proposed scheme.

Simultaneous fault detection and isolation based on multi-task long short-term memory neural networks

Fault detection and isolation are two significant problems in process monitoring. However, due to the complicated relationships between the process variables, it becomes a challenging problem to build a model to capture the complicated relationship between process variables and perform fault detection and isolation based on the model. This paper proposes an advanced model, called multi-task long short-term memory (MTLSTM) neural networks, that can capture the most complicated relationships between process variables and a simultaneous fault detection and isolation approach based on MTLSTM neural networks. The proposed simultaneous fault detection and isolation approach takes a sparse autoencoder (SAE) to extract features from the measurements of process variables. Then, a long short-term memory neural network is trained on the extracted features with a multi-task learning strategy. Compared with the traditional multivariate statistical models (MVSM) such as principal component analysis and independent component analysis models, the MTLSTM neural networks can capture the nonlinear autocorrelations of process variables and correlations between process variables and perform fault detection and isolation for an industrial process simultaneously. To demonstrate the advantages and superiority of the proposed simultaneous fault detection and isolation approach, a case study on fault detection and isolation for the penicillin fermentation process is carried out. Case study results show that the proposed approach significantly outperforms existing fault detection and isolation approaches.

A passive isolation of sensor faults from un-stealthy attacks in uncertain nonlinear systems

In this paper, a novel nonlinear observer-based approach is established to cast light on the problem of isolating sensor faults from un-stealthy attacks for Lipschitz affine nonlinear systems subjected to unknown uncertainties and disturbances. A structure consisting of a multi-line transmission mechanism, a random number generator (RNG), and Dedicated Observer Scheme is developed to make the abnormal behaviors distinguishable. A group of two interlinked nonlinear Luenberger-Like observers is designed in nonlinear coordinates to detect abnormal behaviors. Two banks of detection observers and an incidence matrix are introduced to make the final decisions. Adaptive threshold techniques are exploited to guarantee the robustness against the model uncertainties and disturbances. Compared with the existing results, the proposed approach isolates the abnormal behaviors without the need for any redundant hardware components. Finally, the performance of the proposed approach is evaluated on a continuous stirred tank reactor (CSTR).

Stochastic restoration of distribution networks with high penetration of solar PV systems through a generic switching order mechanism

The process of fault isolation and service restoration (FISR) is an essential component of the implementation of distribution automation. By using FISR to determine the switching order, whether manually or remotely, the network's reliability is significantly enhanced in comparison to that of conventional switching. On the other hand, it is impossible to refute the exponential development of distributed energy resources such as photovoltaic (PV) generators. This paper introduces a general FISR for distribution networks with high penetration of solar PV systems. The proposed FISR strategy is an optimum switching sequence with the goal of reaching the best restoration hour before any likely fault occurrence. The proposed framework takes into account the inherent uncertainty in network demand and renewable generation in addition to the innovative mathematical programming model for the switching order. In addition, this stochastic programming handles the radiality constraint in the restoration process by creating a fictional layer as a means of reducing the complexity of the modeling. Moreover, the impact of PV penetration level on the optimal switching order for heavy-loaded distribution feeders is addressed and investigated.

LADA methodologies to localize embedded memory failure

Bitmap tool is commonly used by semiconductor design house to debug embedded memory failure as it is able to localize down to bit cell resolution with faster turnaround time as compared to hardware fault isolation methods. However, bitmap enablement requires tedious effort and high cost as it requires accurate logical-to-physical address scrambling information from memory IP provider to be successful. In this work, usage of hardware-based LADA fault isolation technique to debug system-on-chip (SOC) memory failure is discussed in term of implementation difficulty and success rate.

Improved energy graph-based visualisation fault detection and isolation — A spectral theorem approach

This paper illustrates how the energy graph-based visualisation (EGBV) fault detection and isolation (FDI) method may be interpreted in terms of the spectral theorem to gain insight into the sensitivity and robustness performance of the method. It is shown that the EGBV monitoring structure can be decomposed into components of varying importance. A formula is proposed as a guideline for informed component removal. These principles are applied to a practical heated two-tank process. It is shown that lesser-weighted components exhibit noisy behaviour and, when removed, increase the robustness of EGBV. Additionally, the computational requirements for the EGBV method and its fault signatures are reduced. It is also shown that retaining smaller components provides the benefit of improved sensitivity. Therefore, a trade-off exists between sensitive and robust process monitoring. Furthermore, it is acknowledged that component removal may compromise the resolution of EGBV’s fault signatures and so, a formula is derived to verify its resolution integrity.

General fuzzy adaptive fault-tolerant control based on Nussbaum-type function with additive and multiplicative sensor and state-dependent actuator faults

The present paper devotes to propose a general active fault-tolerant control (GAFTC) for a class of unknown multi-input multi-output (MIMO) nonlinear systems that are subject to simultaneous additive and multiplicative sensor and nonaffine nonlinear state-dependent actuator faults, exogenous disturbances and multiple unknown control directions. By combining fuzzy logic systems and back-stepping technique, an adaptive control unit along with a robust control unit are developed. In the proposed approach, the unknown dynamics along with sensor and non-affine nonlinear state-dependent actuator faults are approximated to avoid developing fault detection and isolation unit. Furthermore, the difficulty caused by the unknown control gain directions is solved by using the Nussbaum-type function. The adaptation mechanism is proposed to overcome fuzzy approximation errors and exogenous disturbances, and the Butterworth low-pass filter is adopted to get rid of the algebraic loop appearing in the actuator faults approximation. The stability of the closed-loop system is proved by Lyapunov method where all the signals involved in the resulting closed-loop system are shown to be bounded, and the tracking errors converge asymptotically to the origin. Quadrotor attitude dynamics stabilization is proposed in the simulation study to test the accuracy and the effectiveness of the proposed control scheme.