Nader Meskin

Actuator Fault Detection and Isolation for a Network of Unmanned Vehicles

This technical note investigates development, design and analysis of actuator fault detection and isolation (FDI) filters for a network of unmanned vehicles. It is shown that actuator fault signatures in a network of unmanned vehicles are dependent and the network can be considered as an over-actuated system. An isolability index mu is defined for a family of fault signatures and a new structured residual set is developed that is selectively capable of properly detecting and isolating mu multiple faults in linear systems with dependent fault signatures, such as over-actuated systems. Our proposed algorithm is then applied to the actuator FDI problem in a network of unmanned vehicles configured according to centralized, decentralized and semi-decentralized architectures. A comparative analysis in terms of the capabilities and limitations of these architectures is performed. Simulation results presented for the formation flight of multiple satellites demonstrate the effectiveness of our proposed FDI algorithm.

A Hybrid Fault Detection and Isolation Strategy for a Network of Unmanned Vehicles in Presence of Large Environmental Disturbances

In this brief, the problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a network of unmanned vehicles (NUVs) that is subject to large environmental disturbances is investigated. The proposed FDI algorithm is a hybrid architecture that is composed of a bank of continuous-time residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that the DES fault diagnoser empowered by incorporating appropriate combinations of the residuals and their sequential features will robustly detect and isolate faults in the NUVs. Our proposed hybrid FDI algorithm is then applied to actuator fault detection and isolation in a network of quad-rotors. Simulation results demonstrate and validate the performance capabilities of our proposed hybrid FDI algorithm.

A Multiple Model-Based Approach for Fault Diagnosis of Jet Engines

In this brief, a novel real-time fault detection and isolation (FDI) scheme that is based on the concept of multiple model is proposed for aircraft jet engines. A modular and a hierarchical architecture is developed which enables the detection and isolation of both single faults as well as multiple concurrent faults in the jet engine. The nonlinear dynamics of a dual spool jet engine is linearized and a set of linear models corresponding to various operating modes of the jet engine (namely healthy and different faulty modes) at each operating point is obtained. Using the multiple model approach the probabilities corresponding to each operating point of the jet engine are generated and the current operating mode of the system is detected based on evaluating the maximum probability criteria. It is shown that the proposed methodology is also robust to the failure of pressure and temperature sensors and extensive levels of noise outliers in the sensor measurements. Simulation results are presented that demonstrate the effectiveness and capabilities of our proposed multiple model FDI algorithm for both structural faults and an actuator fault in the aircraft jet engine.

Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach

In this paper, a fault detection and isolation (FDI) scheme for an aircraft jet engine is developed. The proposed FDI system is based on the multiple model approach and utilizes dynamic neural networks (DNNs) to accomplish this goal. Towards this end, multiple DNNs are constructed to learn the nonlinear dynamics of the aircraft jet engine. Each DNN corresponds to a specific operating mode of the healthy engine or the faulty condition of the jet engine. Using residuals obtained by comparing each network output with the measured jet engine output and by invoking a properly selected threshold for each network, reliable criteria are established for detecting and isolating faults in the jet engine components. The fault diagnosis task consists of determining the time as well as the location of a fault occurrence subject to presence of unmodeled dynamics, disturbances, and measurement noise. Simulation results presented demonstrate and illustrate the effectiveness of our proposed dynamic neural network-based FDI strategy.

Simultaneous fault detection and consensus control design for a network of multi-agent systems

The problem of simultaneous fault detection and consensus control (SFDCC) of linear continuous-time multi-agent systems is addressed in this paper. A mixed H ∞ / H - formulation of the SFDCC problem is presented and distributed detection filters are designed using only relative output information among the agents. With our proposed methodology, all agents reach either a state consensus or a model reference consensus while simultaneously collaborate with one another to detect the occurrence of faults in the team. Indeed, each agent not only can detect its own fault but also is capable of detecting its neighbors faults. It is shown that through a decomposition approach the computational complexity of solving the distributed problem is significantly reduced as compared to an optimal centralized solution. The extended linear matrix inequalities (LMIs) are used to reduce the conservativeness of the SFDCC results by introducing additional matrix variables to eliminate the couplings of Lyapunov matrices with the system matrices. It is shown that under a special condition on the network topology the faulty agent can be isolated in the team. Simulation results corresponding to a team of autonomous unmanned underwater vehicles (AUVs) demonstrate and illustrate the effectiveness and capabilities of our proposed design methodology.

Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.

Fault Detection and Isolation of Distributed Time-Delay Systems

This paper investigates the development of fault detection and isolation (FDI) filters for distributed time-delay systems. The notion of a finite unobservability subspace is introduced for distributed time-delay systems and an algorithm for its construction is presented. A bank of residual generators is designed based on our developed geometric framework so that each residual is affected by one fault and is decoupled from the others while the H infin norm of the transfer function between the disturbance and the residual signals are kept at less than a prespecified value. Simulation results for a combustion system in a rocket motor chamber demonstrate the effectiveness and capabilities of our proposed FDI algorithm.

Transient Gas Turbine Performance Diagnostics Through Nonlinear Adaptation of Compressor and Turbine Maps

Gas turbines are faced with new challenges of increasing flexibility in their operation while reducing their life cycle costs, leading to new research priorities and challenges. One of these challenges involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis, and prognosis schemes, which will be able to handle and address the gas turbines ever-growing flexible and dynamic operational characteristics. Predicting accurately the performance of gas turbines depends on detailed understanding of the engine components behavior that is captured by component performance maps. The limited availability of these maps due to their proprietary nature has been commonly managed by adapting default generic maps in order to match the targeted off-design or engine degraded measurements. Although these approaches might be suitable in small range of operating conditions, further investigation is required to assess the capabilities of such methods for use in gas turbine diagnosis under dynamic transient conditions. The diversification of energy portfolio and introduction of distributed generation in electrical energy production have created need for such studies. The reason is not only the fluctuation in energy demand but also more importantly the fact that renewable energy sources, which work with conventional fossil fuel based sources, supply the grid with varying power that depend, for example, on solar irradiation. In this paper, modeling methods for the compressor and turbine maps are presented for improving the accuracy and fidelity of the engine performance prediction and diagnosis. The proposed component map fitting methods simultaneously determine the best set of equations for matching the compressor and the turbine map data. The coefficients that determine the shape of the component map curves have been analyzed and tuned through a nonlinear multi-objective optimization scheme in order to meet the targeted set of engine measurements. The proposed component map modeling methods are developed in the object oriented MATLAB/SIMULINK environment and integrated with a dynamic gas turbine engine model. The accuracy of the methods is evaluated for predicting multiple component degradations of an engine at transient operating conditions. The proposed adaptive diagnostics method has the capability to generalize current gas turbine performance prediction approaches and to improve performance-based diagnostic techniques. Copyright © 2015 by ASME.

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

Fault Detection and Isolation of Actuator Faults in Spacecraft Formation Flight

This paper investigates the development of fault detection and isolation (FDI) filters for spacecraft formation flight. A MIMO architecture for formation flying control is considered. By utilizing a geometric FDI methodology, a local/decentralized detection filter is developed for detecting faults in other spacecraft by determining the required unobservability subspace of the local system. In the case when such an unobservability subspace does not exist, the required communication links between the spacecraft are specified. Simulation results presented demonstrate the effectiveness of our proposed methodology for FDI of formation flying systems