Sequential optimal fault detection and isolation in multisensor navigation systems

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

<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>

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.

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.

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.

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014

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.

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

Model-based fault isolability and isolation of persistent faults: Centralized and distributed implementations

This paper deals with the problem of fault detection and isolation (FDI) of a networked control system (NCS). In this configuration a linear time invariant(LTI) system communicates with controller/FDI over a shared medium. The medium supports a limited number of simultaneous connections between sensors and FDI system. Furthermore, it is assumed that there is the random and unknown packet dropouts in the controller-to-actuator link. Objective is to find a strategy for residual signals generating which network effects are taken into account. In addition, by means of these residuals fault detection and isolation of system is possible. A set of structured residual based on parity relation approach is proposed. It guarantees robustness to system disturbances and packet dropout while each residual is designed to be sensitive to some sensor faults. The efficiency of the proposed approach will be illustrated with a simulation example.

Model-based fault detection and isolation for a gas–liquid separation unit

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

An Unknown Input Kalman Filter (UIKF) based Component Fault Detection and Isolation (CFDI) technique for a dynamical system, affected by both plant and measurement noise, is presented. The Fault Detection and Isolation (FDI) algorithm, which consists of two steps, is developed with the assumption that the fault occurs in a single component of the system. In step 1, the detection of the fault and the isolation of the faulty region are achieved. In the next step, the faulty parameter is isolated from the faulty region. The method is applied on a road vehicle model to show the effectiveness of the algorithm.

<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>

Degradation monitoring is of paramount importance to safety and reliability of aircraft operations and also for timely maintenance of its critical components. This two-part paper formulates and validates a novel methodology of degradation monitoring of aircraft gas turbine engines with emphasis on detection and isolation of incipient faults. In a complex system with multiple interconnected components (e.g. an aircraft engine), fault isolation becomes a crucial task because of possible input—output and feedback interactions among the individual components. This paper, which is the first of two parts, presents the underlying concepts of fault detection and isolation (FDI) in complex dynamical systems. The FDI algorithms are formulated in the setting of symbolic dynamic filtering (SDF) that has been recently reported in literature. The underlying concept of SDF is built upon the principles of symbolic dynamics, statistical pattern recognition, and information theory. In addition to abrupt large faults, the SDF-based algorithms are capable of detecting slowly evolving anomalies (i.e. deviations from the nominal behaviour) based on analysis of time series data of critical process variables of different engine components. The second part, which is a companion paper, validates the concept, laid out in the first part, on the simulation test bed of a generic two-spool turbofan aircraft engine model for detection and isolation of incipient faults.