Analytical Redundancy Research Articles

A probabilistic model-based diagnostic framework for nuclear engineering systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells

Fuel cells are considered a highly efficient and eco-friendly alternative for energy production. However, for successful commercialization of fuel cells, it is necessary to ensure the reliability of fuel cell systems. This study proposes a real-time fault detection and diagnosis method for the thermal management system, one of the important balance of plants in proton exchange membrane fuel cell systems. A hierarchical method is developed for fault detection and diagnosis using the residuals which are differences between the analytical redundancies and the measured values as indicators. The analytical redundancies are calculated with regression models. The proposed method consists of two steps. After faults are classified into the thermal management system at the subsystem level diagnosis in the first step, specific component faults are diagnosed in the second step. The residual of the heat transfer rate is used in the first step, and the additional residuals related to two coolant flow rates and one coolant temperature are used in the second step. When faults occurred in the pumps, sensors, and heat exchanger, the experimental results showed that the residual-based method accurately diagnosed the causes and locations of problems in fuel cell systems.

Fault Diagnosis and Prognosis of a Brushless DC motor using a Model-based Approach

This paper proposes a model-based fault diagnosis and prognosis approach applied to brushless DC motors (BLDC). The objective is an early detection of mechanical and electrical faults in BLDC motors operating under a variety of operating conditions. The proposed model-based method is based on the evaluation of a set of residuals that are computed taking into account analytical redundancy relations. Fault diagnosis consist of two steps: First, checking if at least one of the residuals is inconsistent with the normal operation of the system. And, second, evaluating the set of the residuals that are inconsistent to determine which fault is present in the system. Fault prognosis consists of the same two steps but instead of considering current inconsistencies evaluates drift deviations from nominal operation to predict futures residual inconsistencies and therefore predict future fault detections and diagnosis. A description of various kinds of mechanical and electrical faults that can occur in a BLDC motor is presented. The performance of the proposed method is illustrated through simulation experiments.

Model-Aided State Estimation of HALE UAV With Synthetic AOA/SSA for Analytical Redundancy

This paper proposes a novel dynamic model-aided navigation filter to estimate the safety-critical states of a high-altitude long-endurance (HALE) UAV without measurement of angle of attack (AOA) and sideslip angle (SSA). The major contribution of the proposed algorithm is that the synthetic AOA and SSA measurements are newly formulated for analytical redundancy. In the proposed filter, aerodynamic coefficients and control signals are utilized along with inertial measurement unit (IMU), Global Positioning System (GPS), and pitot tube measurements to estimate the navigation states as well as the steady and turbulent effects of 3D wind using random walk (RW) and Dryden wind models, respectively. Flight test results of a HALE UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, AOA, SSA, attitude, angular rates, and 3D wind states, demonstrating its effectiveness for analytical redundancy.

АНАЛІЗ МЕТОДІВ ПІДВИЩЕННЯ ВІДМОВОСТІЙКОСТІ СИСТЕМ КЕРУВАННЯ ЛІТАЛЬНИХ АПАРАТІВ

The issues related to the design of stabilization systems for aviation and rocket and space technology from the point of view of ensuring the main target function are considered: the system in operation must satisfy all the specified quality indicators, provided that it provides fault tolerance for a specified period of time. The general principles of designing fault-tolerant systems are analyzed in relation to the areas of Continuum, Fault Tolerant, Control by Diagnosis, which include modularity, independence of failures, redundancy, and quick failure detection. At the same time, the basic principles of improving fault tolerance are summarized and tabulated for clarity, among which are highlighted such as optimization, analytical redundancy, functional redundancy, reconfiguration, as well as the principles of quick failure detection based on self-monitoring and redundancy results. For systems with active fault-tolerance, examples are given of constructing diagnostic systems and making decisions about the serviceability of angular velocity sensors (AVS) used in aircraft stabilization channels. Specific schemes are presented and analyzed from the point of view of obtaining diagnosis results: schemes for constructing diagnostic systems based on two AVS by comparing their output signals, a hardware implementation diagram of a gyroscopic type AVS diagnostic unit with a potentiometric output transducer and a system for automatically switching a failed AVS to a similar backup one, and the scheme and principle of diagnostics with one working AVS in the autopilot scheme and one working outside the autopilot scheme. In order to reduce the overall mass characteristics during the design of aircraft stabilization systems with PD controllers, a reconfiguration scheme of the original system is proposed for failures of both the angle sensor (AS) and the angular velocity sensor. In addition, a directly algorithmic method was proposed for identifying faulty of AS and AVS in static-type autopilots, for which purpose components of the failure vector were introduced into the equations describing the dynamics of the aircraft stabilization system in the state space, by the state of which it is easy to determine the failed sensor.

Fault-tolerant control of variable speed limits for freeway work zone using likelihood estimation

Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.

Active Fault-Tolerant Control for Electro-Hydraulic Systems With an Independent Metering Valve Against Valve Faults

Aiming at the tough requirements for safety and maintainability in mobile hydraulic systems, in this article, an active fault-tolerant control (FTC) system is proposed against the valve faults of an independent metering valve. Not only moderate faults of activated valves, but also significant faults and unactivated valve faults are considered. Without additional hardware redundancy, analytical redundancy is derived by the coordinate control of other fault-free valves. Accordingly, an FTC system in parallel with a normal controller is designed based on the pressure feedback. It consists of a set of reconfigurable controllers and a decision mechanism. The control signals, control loops, and operating modes can all be precisely reconfigured to adaptively match unmodeled fault dynamics. A bumpless transfer controller based on a latent tracking loop is designed to smooth the switching between the normal controller and FTC. Consequently, random valve faults can be tolerated with minor degradations in motion tracking and energy-saving performance. The feasibility of the FTC system is evaluated by a 2-ton excavator.

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.

Actuator Fault-Tolerant Control Applied to Three-Tank System

This paper focuses on the application of an active fault-tolerant control (AFTC) to a real nonlinear system using analytic redundancy in case of actuator faults. This approach is composed of the fault detection, isolation, and estimation (FDIE) module and a control compensation module. A high-gain observer (HGO) is used for FDIE; a new control law is then reconstructed by the addition of the estimated actuator fault magnitude to the nominal control law. Experimental results highlight the performance of the proposed approach when it is applied to the hydrographic plant.

Aerodynamic Model-Aided Estimation of Attitude, 3-D Wind, Airspeed, AOA, and SSA for High-Altitude Long-Endurance UAV

This article proposes a novel dynamic model-aided navigation filter to estimate the safety-critical states of an aircraft including the effect of wind. Aerodynamic coefficients and control signals are used to predict the angular rates. Experimental flight results of a high-altitude long-endurance unmanned aerial vehicle (UAV) demonstrated improvement in attitude estimation compared to a model-based navigation algorithm that does not consider wind, as well as accurate attitude estimation without using gyroscope signals, demonstrating its effectiveness for analytical redundancy.

Fault Detection and Isolation of a Three-tank System Using Analytical Temporal Redundancy – Parity Space/Relation Based Residual Generation

This paper investigates the fault detection and isolation technique in measurement datasets from a three-tank system using analytical model-based temporal redundancy. It is based on residual generation using a parity equations/space approach. Thepaper further briefly outlines other approaches of model-based residual generation. The basic idea of parity space residual neration in temporal redundancy is a dynamic relationship between sensor outputs and actuator inputs (input-output model).These residuals were then used to detect whether or not the system is faulty and indicate the location of the fault when it is faulty.The method obtains good results by detecting and isolating faults from the considered measurement datasets generated from the system.

Decision Making Method for Nonparametric Diagnosis in Nonlinear Dynamic Systems

The problem of robust fault diagnosis in nonlinear dynamic systems under presence of disturbances is studied within the scope of analytical redundancy conception. Solution of the problem assumes residual generation by checking redundancy relations existing among system inputs and outputs measured over a finite time window followed by decision making through evaluation of the residuals. Nonparametric method is considered for residual generation. To make decision, the unified method is developed whose feature is it combines the threshold logic of decision making and the residuals comparing with the fault syndromes evaluated on-line. Joint application of both nonparametric method for residual generation and unified method for decision making gives the possibility to develop the universal diagnostic platform for different systems described by ordinary nonlinear differential equations of the same structure but distinguished by these equations coefficients values.

Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation

An online model-based fault detection and isolation method for salient permanent magnet synchronous motors is proposed using the parity-space approach. Given the polynomial model equations, Buchberger’s algorithm is used to eliminate the unknown variables (e.g. states, unmeasured inputs) resulting in analytic redundancy relations for residual generation. Furthermore, in order to obtain the derivatives of measured signals needed by such a residual generator, robust exact differentiators are used. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.

Fault detection for PEM fuel cell using kalman filter

Fault detection plays a key role in high cost and safe-critical systems to avoid the occurrence of an abnormal event. This work presents a model-based fault detection in the water management system of proton-exchange membrane (PEM) fuel cell stack using Kalman filter (KF). The KF is a model-based FD method relies on analytical redundancy to predict the related fault disturbed by some noise. Then, it corrected the faults with its mathematical model to minimize the error covariance between the normal and faulty scenarios by a repetitive process. Two faulty scenarios; flooding and drying which happen at anode and cathode side of PEM fuel cell are investigated. The proposed FD method using KF has successfully detected flooding and drying faults in PEMFC.

A Robust State Estimation Method for Unknown, Time-Varying and Featureless Aircraft Sensor Failures

Fault-tolerant state estimation is necessary for analytical redundancy in aviation safety, but complicated fault conditions pose a major challenge to reliable estimations. In this paper, we propose a new state-estimation method based on an intermittent-measurement Kalman filter, a maximum-likelihood estimation rule, and the Gaussian mixture reduction. This method is robust to time-varying and featureless faults, and requires no past history of innovation errors to perform fault detection and isolation, leading to the easy implementation and improved accuracy under severe sensor-failure conditions. With an example application of monitoring the rotation speeds of a two-spool jet engine in simulations, the effectiveness of the proposed method is verified.

Uncertain fault estimation using bicausal bond graph: Application to intelligent autonomous vehicle

This article addresses the fault detection and isolation problem of uncertain systems using the bond graph model–based approach. The latter provides through its causal and structural properties an automatic analytical redundancy relations generation. The numerical evaluation of analytical redundancy relations yields residuals, which are used to verify the coherence between the real system and reference behaviors describing the nominal operation. In fact, the residual is compared to its thresholds to detect the fault. In addition, the comparison between all fault signatures allows making a decision on fault isolation. Moreover, to isolate the faults that activate the same set of residuals, an additional residual must be calculated for each fault. This additional residual is the comparison between two estimations of the considered fault obtained using the sensitivity relations. However, due to the presence of uncertainties, errors can occur in the fault estimation allowing false decisions on fault isolation. The novelties and innovative interests in the present work are (1) to improve the fault estimation procedure based on the uncertainties modeling and bicausality notion, in order to overcome the problem related to errors in the estimated fault and (2) to suitably generate the isolation thresholds in a systematic way using the uncertain fault estimation procedure proposed in this article so that fault can be isolated successfully. The proposed methodology is studied under various scenarios via simulations over an electromechanical traction system corresponding to a quarter of intelligent autonomous vehicle, named RobuCar.

An Active Actuator Fault-Tolerant Control of a Quadrotor Based on Analytical Redundancy Relations

In order to maintain the performance of the control systems affected by the fault, a fault-tolerant control (FTC) method is used. This controller tries to minimize the effect of the fault by modifying or correcting the control law of the system at the time of fault occurrence, so that the system can continue its normal operation. In this paper, we present an analytical method for controlling the actuator faults in quadrotor. In this method, called analytical redundancy relations, we try to estimate the system states using the analytic relations, and therefore, we do not need an observer to estimate system states. So we can achieve the faulty system states without using the observer, and we can reconfigure or modify the control laws using these estimated states. This approach is an active FTC that can handle any kind of unforeseen faults that may affect the system. In this research, the control law of the main system in the normal operation is the sliding mode control that has a good performance in dealing with nonlinear complex dynamical system models.

An innovative analytic redundancy approach to air data sensor fault detection

ABSTRACTThis article presents a potential analytic redundancy approach to detect faults in the air data sensor of an aircraft. In modern aircraft, fault detection of air data sensors is performed using a complex voting mechanism, which requires the availability of redundant air data sensor in all situations. However, to continuously monitor operation and performance of these sensors, the analytic redundancy-based air data estimation and fault detection is highly preferred than estimation with air data probe measurements. The proposed algorithm uses the kinematics of aircraft to estimate air data and detect air data sensor fault. In this paper, a simple mathematical model is developed, which does not consider the forces and moments acting on aircraft and uses measurements only from the Inertial Measurement Unit (IMU) and Navigation System Data (NSD). In order to implement this approach, the Iterated Optimal Extended Kalman Filter (IOEKF) is developed to estimate air data, which provides an accurate and stable estimation. With the estimated states, the physical air data sensor measurements are compared and the residual is calculated to track each sensor performance and to detect the occurrence of a fault. The key advantage of this approach is that it does not require complex dynamic equations and is free from system uncertainties. The proposed algorithm is simulated in MATLAB software using flight simulator flight data and validated using the real-time flight data of Cessna Citation II transport aircraft.

RadiationGeneSigDB: a database of oxic and hypoxic radiation response gene signatures and their utility in pre-clinical research.

Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. In this era of personalized radiation medicine, high-throughput data now provide the means to investigate novel biomarkers of radiation response. Large-scale efforts have identified several radiation response signatures, which poses two challenges, namely, their analytical validity and redundancy of gene signatures. To address these fundamental radiogenomics questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database by gaining an understanding of hypoxia-associated miRNA by applying a penalized multivariate model; by comparing breast cancer oxic signatures in cell line data vs patient data; and by comparing the similarity of head and neck cancer hypoxia signatures at the pathway level in clinical tumour data. We obtained a set of miRNA highly associated both positively and negatively to the hypoxia gene signatures, across pan-cancer. In addition, we identified moderate correlations between breast cancer oxic signatures in patient data, and significant differences across molecular subtypes. Moreover, we also found that different set of pathways to be enriched using the head and neck hypoxia signatures, although, they are found to be concordant when applied on the patient data. This valuable, curated repertoire of published gene expression signatures provides motivating case studies for how to search for similarities in radiation response for tumours arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel biological hypotheses about the functions of non-coding RNA. We envision that RadiationSigDB database will help accelerate preclinical radiotherapeutic discovery pipelines in terms of analytical validity of novel biomarkers of radiation response and the need for ensemble approaches to clinical genomic biomarkers.

Hybrid Qualitative and Quantitative Diagnosis Approach

Parametric faults are detected and isolated using parameter tracking algorithms based on optimization algorithms or filtering techniques (e.g., Kalman filter, particle filter). Online, simultaneous tracking of all parametric faults can fails since there may be too many combinations of parameter values that explain the observed behavior. Hence, a correct diagnosis solution is not obtained. An alternative in the single fault case is to track separately each parametric fault in parallel and choose the one that best explains the observed behavior according to some chosen metric (e.g., mean square error). This approach is feasible but computationally expensive, since there may be too many tracking algorithms running in parallel. We propose using analytic redundancy relations (ARRs) to reduce the number of parametric faults that are tracked simultaneously. ARRs qualitatively point to a set of possible explanations but usually require a large number of sensors to achieve good isolability of fault causes. They induce a fault signature matrix (FSM) that can be derived offline. The parameter tracking algorithms will be instantiated for the faults in the set of possible explanations produced by the ARRs. By combining ARRs with online parameter tracking algorithms we can obtain a good tradeoff between computational effort and fault isolability. We demonstrate our approach by diagnosing faults in a rectifier circuit.