Systematic Process for Building a Fault Diagnoser Based on Petri Nets Applied to a Helicopter

This work presents a systematic process for building a Fault Diagnoser (FD), based on Petri Nets (PNs) which has been applied to a small helicopter. This novel tool is able to detect both intermittent and permanent faults. The work carried out is discussed from theoretical and practical point of view. The procedure begins with a division of the whole system into subsystems, which are the devices that have to be modeled by using PN, considering both the normal and fault operations. Subsequently, the models are integrated into a global Petri Net diagnoser (PND) that is able to monitor a whole helicopter and show critical variables to the operator in order to determine the UAV health, preventing accidents in this manner. A Data Acquisition System (DAQ) has been designed for collecting data during the flights and feeding PN diagnoser with them. Several real flights (nominal or under failure) have been carried out to perform the diagnoser setup and verify its performance. A summary of the validation results obtained during real flight tests is also included. An extensive use of this tool will improve preventive maintenance protocols for UAVs (especially helicopters) and allow establishing recommendations in regulations. © 2015 Miguel A. Trigos et al.This work has been supported by the project RoboCity2030- III-CM (Robotica Aplicada a la Mejora de la Calidad de Vida ´ de los Ciudadanos; Fase III; S2013/MIT-2748), funded by the I+D program at Comunidad de Madrid and cofunded by Fondos Estructurales of European Union and by the project Proteccion Robotizada de Infraestructuras Críticas, DPI2014- 56985-R, by Ministerio de Economía y Competitividad of Spain.Peer Reviewe

Design of fault tolerant control system for individual blade control helicopters

This dissertation presents the development of a fault tolerant control scheme for helicopters fitted with individually controlled blades. This novel approach attempts to improve fault tolerant capabilities of helicopter control system by increasing control redundancy using additional actuators for individual blade input and software re-mixing to obtain nominal or close to nominal conditions under failure. An advanced interactive simulation environment has been developed including modeling of sensor failure, swashplate actuator failure, individual blade actuator failure, and blade delamination to support the design, testing, and evaluation of the control laws. This simulation environment is based on the blade element theory for the calculation of forces and moments generated by the main rotor. This discretized model allows for individual blade analysis, which in turn allows measuring the consequences of a stuck blade, or loss of the surface area of the blade itself, with respect to the dynamics of the whole helicopter. The control laws are based on non-linear dynamic inversion and artificial neural network augmentation, which is a mix of linear and nonlinear methods that compensates for model inaccuracies due to linearization or failure. A stability analysis based on the Lyapunov function approach has shown that bounded tracking error is guaranteed, and under specific circumstances, global stability is guaranteed as well. An analysis over the degrees of freedom of the mechanical system and its impact over the helicopter handling qualities is also performed to measure the degree of redundancy achieved with the addition of individual blade actuators as compared to a classic swashplate helicopter configuration. Mathematical analysis and numerical simulation, using reconfiguration of the individual blade control under failure have shown that this control architecture can potentially improve the survivability of the aircraft and reduce pilot workload under failure conditions

Systematic Process for Building a Fault Diagnoser Based on Petri Nets Applied to a Helicopter

This work presents a systematic process for building a Fault Diagnoser (FD), based on Petri Nets (PNs) which has been applied to a small helicopter. This novel tool is able to detect both intermittent and permanent faults. The work carried out is discussed from theoretical and practical point of view. The procedure begins with a division of the whole system into subsystems, which are the devices that have to be modeled by using PN, considering both the normal and fault operations. Subsequently, the models are integrated into a global Petri Net diagnoser (PND) that is able to monitor a whole helicopter and show critical variables to the operator in order to determine the UAV health, preventing accidents in this manner. A Data Acquisition System (DAQ) has been designed for collecting data during the flights and feeding PN diagnoser with them. Several real flights (nominal or under failure) have been carried out to perform the diagnoser setup and verify its performance. A summary of the validation results obtained during real flight tests is also included. An extensive use of this tool will improve preventive maintenance protocols for UAVs (especially helicopters) and allow establishing recommendations in regulation

Development of Fault Detection and Diagnosis Techniques with Applications to Fixed-wing and Rotary-wing UAVs

ABSTRACT Development of Fault Detection and Diagnosis Techniques with Applications to Fixed-wing and Rotary-wing UAVs Ling Ma Fault Detection and Diagnosis (FDD), as the central part of a Fault Tolerant Control System (FTCS), detects and diagnoses the source and the magnitude of a fault when a fault/failure occurs either in an actuator, sensor or in the system itself. This thesis work develops an applicable procedure for a FDD scheme to both fixed-wing and rotary-wing UAVs (Unmanned Aerial Vehicles) in the discrete-time stochastic domain based on the Kalman filter techniques. In particular, the proposed techniques are developed in highly nonlinear and 6 degree-of-freedom equations of Matlab/Simulink simulation environment for a quad-rotor helicopter UAV, a Boeing 747, and a NASA Generic Transport Model (GTM) fixed-wing UAV. A key development in this thesis is that an Adaptive Two-Stage Extended Kalman Filter (ATSEKF) algorithm and a Dual Unscented Kalman Filter (DUKF) algorithm are applied for simultaneous states and fault parameters estimation of these UAVs. The statistical decision-making techniques for fault detection and diagnosis are also discussed in the presence of partial faults in the UAVs. The measured system outputs and control signals are used as inputs of the ATSEKF and DUKF, and the estimated states and parameters are used for comparison and analysis in the fault detection and diagnosis. The simulation results show that the effectiveness and performance of ATSEKF and DUKF for the purpose of fault detection and diagnosis of both fixed- and rotary-wing UAVs are satisfactory

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Self-Healing Control Framework Against Actuator Fault of Single-Rotor Unmanned Helicopters

Unmanned helicopters (UHs) develop quickly because of their ability to hover and low speed flight. Facing different work conditions, UHs require the ability to safely operate under both external environment constraints, such as obstacles, and their own dynamic limits, especially after faults occurrence. To guarantee the postfault UH system safety and maximum ability, a self‐healing control (SHC) framework is presented in this chapter which is composed of fault detection and diagnosis (FDD), fault‐tolerant control (FTC), trajectory (re‐)planning, and evaluation strategy. More specifically, actuator faults and saturation constraints are considered at the same time. Because of the existence of actuator constraints, usable actuator efficiency would be reduced after actuator fault occurrence. Thus, the performance of the postfault UH system should be evaluated to judge whether the original trajectory and reference is reachable, and the SHC would plan a new trajectory to guarantee the safety of the postfault system under environment constraints. At last, the effectiveness of proposed SHC framework is illustrated by numerical simulations

Fault tolerant flight control system design for unmanned aerial vehicles

Safety and reliability of air vehicles is of the utmost importance. This is particularly true for large civil transport aircraft where a large number of human lives depend on safety critical design. With the increase in the use of unmanned aerial vehicles (UAVs) in our airspace it is essential that UAV safety is also given attention to prevent devastating failures which could ultimately lead to loss of human lives. While civil aircraft have human operators, the pilot, to counteract any unforeseen faults, autonomous UAVs are only as good as the on board flight computer. Large civil aircraft also have the luxury of weight hence redundant actuators (control surfaces) can be installed and in the event of a faulty set of actuators the redundant actuators can be brought into action to negate the effects of any faults. Again weight is a luxury that UAVs do not have. The main objective of this research is to study the design of a fault tolerant flight controller that can exploit the mathematical redundancies in the flight dynamic equations as opposed to adding hardware redundancies that would result in significant weight increase. This thesis presents new research into fault tolerant control for flight vehicles. Upon examining the flight dynamic equations it can be seen, for example, that an aileron, which is primarily used to perform a roll manoeuvre, can be used to execute a limited pitch moment. Hence a control method is required that moves away from the traditional fixed structure model where control surface roles are clearly defined. For this reason, in this thesis, I have chosen to study the application of model predictive control (MPC) to fault tolerant control systems. MPC is a model based method where a model of the plant forms an integral part of the controller. An optimisation is performed based on model estimations of the plant and the inputs are chosen via an optimisation process. One of the main contributions of this thesis is the development of a nonlinear model predictive controller for fault tolerant flight control. An aircraft is a highly nonlinear system hence if a nonlinear model can be integrated into the control process the cross-coupling effects of the control surface contributions can be easily exploited. An active fault tolerant control system comprises not only of the fault tolerant controller but also a fault detection and isolation subsystem. A common fault detection method is based on parameter estimation using filtering techniques. The solution proposed in this thesis uses an unscented Kalman filter (UKF) for parameter estimation and controller updates. In summary the main contribution of this thesis is the development of a new active fault tolerant flight control system. This new innovative controller exploits the idea of analytical redundancy as opposed to hardware redundancy. It comprises of a nonlinear model predictive based controller using pseudospectral discretisation to solve the nonlinear optimal control problem. Furthermore a UKF is incorporated into the design of the active fault tolerant flight control system