An unknown input Kalman filter based component FDI algorithm and its application in automobiles

substantially from the prescribed or expected characteristics. To accommodate failures, the baseline inner-loop control is assisted by a fault detection and isolation algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop guidance is augmented with an adaptive neural network forming an adaptive outer-loop guidance system. These adaptive subsystems have been formulated for, and applied to, a transport aircraft model in a nonlinear, high-fidelity aircraft simulation environment to demonstrate the concept. Included results demonstrate that the fault detection and isolation and adaptive outer-loop functionality consistently improve tracking performance during both piloted maneuvers and autonomous waypoint path following. In some cases, these retrofit subsystems enable tasks to be completed that were not previously feasible. Adaptive enhancement of the robustness of existing non-adaptive systems has been an area of significant interest for many years. Although application in the aircraft community is of great benefit for maximizing performance and safety for unexpected changes in the dynamics due to flight control failures, damage, and adverse environmental conditions, certification of these technologies for safety-critical flight systems is challenging. The approach described here is developed under the constraint of maintaining a retrofit architecture thereby allowing the baseline, previously certified, software to remain. The augmented modules serve to assist systems already in place. This paper discusses the development and application of independent and modular adaptive subsystems that are retrofitted to existing guidance and control architectures. The baseline inner-loop control is assisted by a fault detection and isolation (FDI) algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop (BOL) guidance is augmented with an adaptive neural network collectively forming an adaptive outer-loop (AOL) guidance system. In general, an adequate inner-loop feedback control law is necessary to faithfully track guidance commands, and various inner-loop control design techniques can provide the requisite performance. Even in the case of degraded inner-loop tracking capability, the outer-loop guidance system may still meet its objectives by inflating inner-loop commands to compensate for inner-loop tracking errors. Nonetheless, faithful innerloop tracking is desirable to reduce the burden on other subsystems, and this is especially the case for piloted

Fault Detection and Isolation (FDI) techniques have captured extensive interest and attention in modern autonomous systems; in particular, they are of foremost importance in space applications, due to their scientific relevance, cost and current inability of doing on-orbit maintenance of space systems. In this scenario, FDI strategies are required to counteract possible failure events that, if not properly handled, can reduce system performance or compromise the realization of the mission objectives. In this paper, a model-based FDI strategy is implemented onboard a satellite equipped with a very large mesh reflector on which a distributed network of smart actuators/sensors is mounted to actively counteract undesired elastic vibrations. In particular, the detection and isolation of a possible piezo-actuator failure occurring in the Active Vibration Control (AVC) system of the antenna is addressed by a bank of Unknown Input Observers (UIOs). The design of the proposed UIOs is derived by solving a Linear Matrix Inequality (LMI) problem, which provides the conditions for their existence, and it is based on the linearized 3D state-space model of the controlled spacecraft, under the assumption that all the uncertainties, exogenous disturbances and measurement noises are neglected. Furthermore, pole assignment in the sense of D-stability is integrated in the standard formulation of the UIO to guarantee an adequate transient behaviour of the observers. Finally, an extensive Monte Carlo simulation campaign is conducted to assess the effectiveness of the proposed FDI architecture and its robustness against modelling uncertainties and measurement noise.

The Orbital Cygnus vehicle is being developed to perform autonomous rendezvous with International Space Station (ISS) and to provide cargo services to the faculty. Safety through fault management has been a primary consideration in the design of the trajectories, GN&C algorithms, and software. This paper describes the approach used to design and validate a fault management system required to meet the ISS visiting vehicle safety requirements. The nominal mission trajectory and abort maneuvers were designed using linear covariance analysis and were validated to provide a combination of passive and active collision avoidance and requirement satisfaction through semi-analytical methods and extensive simulation. Hardware faults, such as IMU, LIDAR, and GPS sensor faults, are managed using a highly reliable backup propagation system and Fault Detection and Isolation (FDI) algorithms. These algorithms ensure that key sensor systems are fault tolerant and that faulty measurement sources are detected and identified before they significantly corrupt the navigation system. Critically, the FDI system ensures that sufficient measurements will be available to execute an abort maneuver safely at any time. The IMU, LIDAR and GPS FDI algorithms employ standard parity methods to detect sensor measurement errors and a Maximum Likelihood Estimation (MLE) approach for fault identification when sufficient measurement redundancy exists. In the case of GPS FDI, parity space algorithms are utilized for GPS receiver redundancy as well as measurement redundancy within a particular GPS receiver. This allows independent detection and identification of receiver faults and measurement faults arising from GPS satellite faults or a degraded multipath environment. Fault thresholds for FDI were validated using Monte Carlo analysis in a high fidelity 6DOF simulation.

Application of data reconciliation for fault detection and isolation of in-core self-powered neutron detectors using iterative principal component test

Novel concepts of designs for diagnosability and reliability are defined and developed. A diagnosable design of VLSI system is presented, in which fault isolation is realized by minimal additional hardware instead of traditional software diagnostic procedures such that the computation space and time for fault isolation are saved. The presented fault-tolerant design uses online fault detection and isolation techniques, yields higher reliability with minimized hardware overhead of no more than 125% as opposed to over 200% in classical redundancy fault-tolerant designs. Using this scheme, the ability to isolate intermittent faults is a significant improvement over existing fault isolation methods because faults could be isolated right after they are detected. >

Fault Detection and Isolation of an Aircraft Using Set-Valued Observers

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, defined as the measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms within smart infrastructure systems. This research employs a comprehensive methodology, starting with an MSA process for data-quality evaluation and leading to the development and evaluation of fault detection and isolation (FDI) algorithms. During the MSA phase, the repeatability of a water distribution system’s measurement system is examined to characterise variations within the system. A data-quality process is defined to gauge data quality. Synthetic data are introduced with varying data-quality levels to investigate their impact on FDI algorithm development. Key findings reveal the complex relationship between data quality and FDI algorithm performance. Synthetic data, even with lower quality, can improve the performance of statistical process control (SPC) models, whereas data-driven approaches benefit from high-quality datasets. The study underscores the importance of customising FDI algorithms based on data quality. A framework for instantiating the MSA process for IIoT applications is also suggested. By bridging data-quality assessment with data-driven FDI, this research contributes to the design of digital twins for IIoT-enabled smart infrastructure systems. Further research on the practical implementation of the MSA process for edge analytics for PHM applications will be considered as part of our future research.

Most of the failures in the industrial systems are due to motor faults which can be catastrophic and cause major downtimes. Hence, continuous health monitoring, precise fault detection and advance failure warning for motors are pivotal and cost-effective. The identification of motor faults requires sophisticated signal processing techniques for quick fault detection and isolation. This paper presents a real time health monitoring technique for induction motor using pattern recognition method. The proposed fault detection and isolation scheme comprises three stages: data acquisition, feature extraction and multiclass support vector machine classifier. This paper investigates single and multiple faults in single-phase induction motor including bearing fault, load fault and their combination. The testbed consists of 1/2 hp, 220V squirrel cage induction motor with load, vibration sensor, current sensor, data acquisition system and controller. Two features standard deviation and average value are computed for each sensor's data. Multiclass support vector machine classifier is implemented using a low-cost arduino controller for fault detection and isolation. The performance analysis of the classifier with real time sensor's data is presented which shows superior capabilities of developed method.

<div class="htmlview paragraph">The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present. In particular, the local approach is shown to simplify and reduce the complicated FDI problem to the standard problem of detecting changes in the mean value of a Gaussian vector with a constant covariance matrix. In doing so, the least square score is implemented as our primary residual, and the statistical properties of the so-called improved residuals are used to detect changes in the parameters of a parametric model. Applying the local approach, in both offline and online configurations, to a mean-value model of a spark-ignition engine, component faults in the air-intake system, namely small abrupt changes in the volumetric efficiency coefficient <i>η<sub>o</sub></i> and the throttle discharge coefficient <i>C<sub>d</sub></i>, have been successfully detected and isolated. The FDI methodology shows robustness to modeling errors and noise, proving the fact that a perfect model is not required to monitor a given system. Since unknown plant parameters are usually determined by identification techniques, which never yield perfect results, modeling errors are indeed expected in practice. Consequently, the local approach sheds promises for robust on-board diagnostics in the face of the overall nonlinear powertrain problem.</div>

Energy and exergy interactions in industrial systems hold meaning across physical domains. This paper builds on the notion that capturing the energy and exergy interactions of a system, while retaining physical structural context, enables fault detection and isolation. To this end, three energy graph-based visualisation methods were developed for the purpose of fault detection and isolation. This paper presents a comparative study of the three analysis methods designated the 1) distance parameter method, 2) eigenvalue decomposition method, and 3) residual method. The study utilises data from a physical lignite plant in Janschwalde, Germany, in combination with simulation data of specific faults in order to compare the sensitivity and robustness of the three methods. The comparison is done firstly in terms of detection and secondly in terms of isolation. The distance parameter and eigenvalue decomposition methods showed high sensitivity and robustness for fault detection, while the residual method showed moderate comparative performance. In terms of fault isolation, the distance parameter method showed high sensitivity and robustness, while the eigenvalue decomposition method had irregular isolation performance. The residual method isolation results proved inconclusive.

SummaryThis paper proposes an integrated actuator and sensor active fault‐tolerant model predictive control scheme. In this scheme, fault detection is implemented by using a set‐valued observer, fault isolation (FI) is performed by set manipulations, and fault‐tolerant control is carried out through the design of a robust model predictive control law. In this paper, a set‐valued observer is used to passively complete the fault detection task, while FI is actively performed by making use of the constraint‐handling capability of robust model predictive control. The set‐valued observer is chosen to implement fault detection and isolation (FDI) because of its simple mathematical structure that is not affected by the type of faults such as sensor, actuator, and system‐structural faults. This means that only one set‐valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the fault detection and isolation task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust model predictive control is that it can effectively deal with system constraints, disturbances, and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well‐known two‐tank system is used to illustrate the effectiveness of the proposed fault‐tolerant model predictive control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

The problem of optimal integrity monitoring for navigation systems is discussed in the paper. Recently, this problem has received extensive research attention. Numerous fault detection and isolation (FDI) algorithms have been designed. However, the problems of criteria suitable for navigation purposes, optimality of the FDI algorithms in mathematical sense remain unsolved. The goal of this theoretical paper is to give a uniform statistical approach to the problem.

Distributed Fault Detection and Isolation Algorithm Using Modified Gath-Geva Clustering Technique

<p>In the recent years, wireless sensor network technology (WSN) has been widely adopted in precision agriculture for determining the needs of the soil in term of water by monitoring some environmental parameters. To do this, WSN is constructed using several sensor nodes; these small sensing devices are prone to failure and may produce erroneous measurements. To ensure good management of freshwater, the network service quality is necessary. To avoid the degradation of service, the detection of the faulty sensor in WSN is required. In this paper, a fault detection and isolation (FDI) algorithm derived from a parity space approach and based on direct redundancy is proposed toward detecting and isolating sensor fault in WSN. In laboratory experiments, the proposed FDI algorithm proved its effectiveness.</p>