Analytical Redundancy Research Articles

Decentralized diagnosis in a spacecraft attitude determination and control system

In model-based diagnosis (MBD), structural models can provide useful information for fault diagnosis and fault-tolerant control design. In particular, they are known for supporting the design of analytical redundancy relations (ARRs) which are widely used to generate residuals for diagnosis. On the other hand, systems are increasingly complex whereby it is necessary to develop decentralized architectures to perform the diagnosis task. Decentralized diagnosis is of interest for on-board systems as a way to reduce computational costs or for large geographically distributed systems that require to minimizing data transfer. Decentralized solutions allow proper separation of industrial knowledge, provided that inputs and outputs are clearly defined. This paper builds on the results of [1] and proposes an optimized approach for decentralized fault-focused residual generation. It also introduce the concept of Fault-Driven Minimal Structurally-Overdetermined set (FMSO) ensuring minimal redundancy. The method decreases communication cost involved in decentralization with respect to the algorithm proposed in [1] while still maintaining the same isolation properties as the centralized approach as well as the isolation on request capability.

Odabrana metoda dijagnosticiranja ergativnih sustava u zrakoplovstvu

Insufficiency in showing errors in aviation ergatic system is understood to be a situation when there is no available information about the system or some of the board information is unreliable. With the use of the method of automatic diagnosis it is possible to analyse the symptoms of analytical redundancy. Residual differences, i.e. residuents, which are the final value expression of the consequences (effects) of function observation, which are measured on obtained mathematical model, are the keystones of this method. The content of the mathematical model application contains generator of residuents, i.e. signals, which carry the information about the incorrectness (errors) and aviation ergatic system (AES) failures.

Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations

This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional diagnosability. Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajectories of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach.

Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming

In this paper, a novel approach of sensor placement is proposed for the purpose of maximizing fault detectability and isolability. This new approach rests on the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will change the consistency of the corresponding ARRs. Based on these basic facts, the minimal isolating (MI) set is introduced to formulate the full/maximal isolability which is the constraint for sensor placement. Consequently, the optimization problem for sensor placement is reformulated as searching an MI set which is related to the least number of candidate sensors. To find the optimal MI set, a low complexity dynamic programming (LCDP) algorithm is developed on the fault set F that consists of system faults and sensor faults. However, sensor faults are varied as different candidate sensors are used. Therefore, another dedicated procedure is proposed to handle this issue. A case study shows that the proposed approach outperforms an existing sensor placement approach in terms of efficiency.

Fault Detection and Isolation in Inertial Measurement Units Based on Bounding Sets

This work addresses the problem of Fault Detection and Isolation (FDI) for navigation systems equipped with sensors providing inertial measurements and vector observations. Assuming upper bounded sensor noise, two strategies are proposed: i) the first one takes advantage of existing hardware redundancy, requiring at least five sensor measurements to isolate faults; ii) the second approach exploits the analytical redundancy between the angular velocity measurements and the vector observations, by resorting to set-valued observers (SVOs). Necessary and sufficient conditions on the magnitude of the faults are provided, in order to guarantee successful detection and isolation, when hardware redundancy is available. Due to the set-based construction of the methods, none of the solutions generates false detections and no decision threshold is required. Using a simulation scenario, the proposed strategies are compared with two alternatives available in the literature.

Aircraft model-independent airspeed estimation without pitot tube measurements

This paper presents a novel analytical redundancy approach for pitot tube failure accommodation using nonlinear Kalman filtering. This approach utilizes information from other sensors that are commonly implemented on aircraft in order to obtain an estimate of the airspeed which is independent from the pitot tube(s). This method was demonstrated to be independent of the aircraft model by providing experimental estimation results from two different unmanned aerial vehicle (UAV) research platforms.

A Distributed Architecture for HVAC Sensor Fault Detection and Isolation

This paper presents a design and analysis methodology for detecting and isolating multiple sensor faults in heating, ventilation, and air-conditioning (HVAC) systems. The proposed methodology is developed in a distributed framework, considering a multizone HVAC system as a set of interconnected nonlinear subsystems. A dedicated local sensor fault diagnosis (LSFD) agent is designed for each subsystem, while it may exchange information with other LSFD agents. Distributed sensor fault detection is conducted using robust analytical redundancy relations of estimation-based residuals and adaptive thresholds. The distributed sensor fault isolation procedure is carried out by combining the decisions of the LSFD agents and applying a reasoning-based decision logic. The performance of the proposed methodology is analyzed with respect to robustness, sensor fault detectability, and isolability. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to an eight-zone HVAC system.

Experimental Evaluation of Data Fusion Algorithm for Residual Generation in Detecting UAV Servo Actuator Fault

Unmanned Aerial Vehicles used by military are generally designed with very high reliability and have multiple redundancy of software and hardware equipment because they are intended to operate in hostile environment. But, relatively low cost UAVs used commercially are not equipped with such systems. Usually, micro UAVs weight less than 2kg are equipped with on-board miniature sensor and operate without any hardware redundancy and thus could reduce their reliability. Some of these commercial UAVs that operate in populated areas will cause damage and fatality if faulty system occurred. Hence there is a need for on-board fault detection and isolation system without degradating the UAV flyability and its cost. Analytical redundancy or model reference method of fault detection algorithms could be implemented as most UAVs are microcontroller controlled. Together, with the availability of miniature sensors could provide an ideal platform for implementing fault detection. In this paper, the development of fault detection through residual generation algorithm is implemented with data fusion from miniature sensors. Some of these sensors are already installed within the autopilot system which reduce the amount of additional sensors needed. Identification of fault in the elevator is simulated experimentally and fault detection rate is monitored. From the implemented algorithm, the data fusion from additional sensors shows improvement in fault detection rate.

Decentralized Isolation of Multiple Sensor Faults in Large-Scale Interconnected Nonlinear Systems

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

Incident detection and isolation in drilling using analytical redundancy relations

Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian – they follow a multivariate t-distribution – hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 m horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability.

Improved diagnosis of hybrid systems using instantaneous sensitivity matrices

One approach to quantitative model-based fault detection and isolation (FDI) is based on analytical redundancy relations (ARRs) and fault signatures. Numerical evaluation of ARRs creates residuals, which then, provide online information of consistency between the system and its nominal model. An inconsistency is represented by a signature. Traditionally in the quantitative approach, these signatures are binary vectors, where the term 0 means a residual is consistent and 1 means inconsistent. In this paper, the measured trend of residuals is utilized for FDI by a different signature type, called sensitivity signature. In this signature, the consistency of ARRs is represented by three terms; the term +1 indicates a residual is crossing an upper threshold, the term −1 indicates a residual is crossing a lower threshold and 0 means otherwise. The expected sensitivity signature related to a certain fault or to a mode change is taken from partial derivative of residuals. Since consistency, in the sensitivity approach, is represented by three terms (instead of two), more distinguished signatures are generated and improved fault and mode change isolation abilities are achieved. Issues related to practical implementation of the proposed diagnosis method are extensively discussed and experimental results are presented.

A Probabilistic Method for Certification of Analytically Redundant Systems

Abstract Analytical fault detection algorithms have the potential to reduce the size, power and weight of safety-critical aerospace systems. Analytical redundancy has been successfully applied in many non-safety critical applications. However, acceptance for aerospace applications will require new methods to rigorously certify the impact of such algorithms on the overall system reliability. This paper presents a theoretical method to assess the probabilistic performance for an analytically redundant system. Specifically, a fault tolerant actuation system is considered. The system consists of dual-redundant actuators and an analytical fault detection algorithm to switch between the hardware components. The exact system failure rate per hour is computed using the law of total probability. This analysis requires knowledge of the failure rates for the hardware components. In addition, knowledge of specific probabilistic performance metrics for the fault detection logic is needed. Numerical examples are provided to demonstrate the proposed analysis method.

Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

AbstractComputational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm (SCARA) robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are implemented on Matlab/Simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer.

Analytical redundancy based fault diagnosis scheme for satellite attitude control systems

This paper presents an integrated fault detection and diagnosis (FDD) scheme based on analytical redundancy for satellite attitude control systems (ACS). The faults of actuators, angular rate sensors and attitude sensors are all taken into account, and the nonlinear ACS considered is subject to both space disturbance torques and sensor uncertainties. The proposed scheme is developed in two phases. In the first phase, two nonlinear observers are designed, and based on the proposed strategy which called observer redundancy the fault source (actuators, angular rate sensors or attitude sensors) can be verified. In the second phase, two banks of fault isolation observers (FIOs) activated by the previous detection results are designed to perform the diagnosis within the fault part. The first bank of FIOs are obtained based on nonlinear unknown input observers to isolate the fault actuator and the second bank of FIOs are obtained based on nonlinear adaptive observers to isolate and estimate the fault of angular rate sensor. The effectiveness of the proposed scheme is simulated on a closed-loop satellite attitude control system with some typical faults of these components.

Functional Safety of Electrified Vehicles Through Model-Based Fault Diagnosis

Functional safety is of great importance for electric and hybrid electric vehicles (EV/HEVs). One way to improve functional safety of EV/HEVs is developing reliable and robust fault diagnosis and fault tolerant control systems so that, once a component failure is detected, effective remedial actions are taken to avoid system failures. Robust fault diagnosis is a necessary precursor to fault tolerant control strategies. In this paper, we use a V-diagram to present a systematic process for conducting automotive fault diagnosis, in connection with fault tolerant control development. We also introduce a diagnostic approach based on structural analysis that can form the basis of fault tolerant control. The structural analysis approach makes it possible to evaluate a system's analytic redundancy by its structural model, using the mathematical model of the system in matrix form, from which it is possible to derive a set of analytic redundant relations for fault detection and isolation. Structural analysis is useful in the early stages of diagnostic design because specific knowledge of the system parameters in numerical form is not required. In the paper, we demonstrate the application of this methodology, and its broad usefulness, by carrying out the design of a diagnostic strategy for an electric vehicle equipped with a permanent magnet synchronous machine (PMSM) drive system. The EV case study focuses on sensor faults, but the methodology is applicable to any component faults.

An approach for diagnosability analysis and sensor placement for continuous processes based on evolutionary algorithms and analytical redundancy

In this work we propose to use an approach based on genetic algorithms to obtain analytical redundancy relations to study the diagnosability property on a given continuous system, and if this not fulfill, our approach allows studying the sensor placement problem in order to fulfill it. The redundancy relations are based on the minimal test equation support and in a structural analysis over a bipartite graph. The faults analysis is studied using a multi-objective fitness function in two genetic algorithms which describe the different constraints to be covered in order to reach the diagnosability property on the system. Additionally, our approach allows studying the sensors placement problem on systems that do not fulfill the detectability or isolability properties, using another genetic algorithm. 2126 Ruben Leal et al.

Automotive Wire Control Steering Sensor Fault Detection Algorithm Research

This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.

Distributed Adaptive Estimation Scheme for Isolation of Sensor Faults in Multi-zone HVAC Systems

This paper presents a model-based distributed scheme with emphasis on the isolation of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local sensor fault detection and isolation agents are designed to diagnose sensor faults in a HVAC system, modeled as a set of interconnected, nonlinear subsystems. Each agent consists of the local sensor fault detection and adaptive estimation scheme for isolation of sensor faults. Detection and isolation signals are generated based on analytical redundancy relations. These signals are provided to a local decision logic in order to distinguish between local and propagated sensor faults. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a four-zone HVAC system.

Fault Diagnosis of Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bounded description of the model parametric uncertainty and noise using the the so-called set-membership approach. This approach leads to formulate the fault detection test by means of checking if the measurements fall inside the estimated output interval, obtained from the mathematical model of the wind turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind turbine using the National Renewable Energy Laboratory FAST simulator. The obtained results are presented and compared with that of other approaches proposed in the literature.

Robust neural-network-based fault detection with sequential D-optimum bounded-error input design

A growing demand for technologically advanced systems has contributed to the increase of the awareness of systems safety and reliability. Such a situation requires the development of novel methods of robust fault diagnosis. The application of the analytical redundancy based methods for system fault detection causes that their effectiveness depends on model quality. In this paper, a new methodology for the improvement of the neural model with a D-optimum sequential experimental design technique combined with outer bounding ellipsoid algorithm is proposed. Moreover, a novel method of robust fault detection against neural model uncertainty and disturbances is developed. Such an approach is used for modelling and robust fault detection of the three-screw spindle oil pump.