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

Air Data Sensor Fault Detection with an Augmented Floating Limiter

Although very uncommon, the sequential failures of all aircraft Pitot tubes, with the consequent loss of signals for all the dynamic parameters from the Air Data System, have been found to be the cause of a number of catastrophic accidents in aviation history. This paper proposes a robust data-driven method to detect faulty measurements of aircraft airspeed, angle of attack, and angle of sideslip. This approach first consists in the appropriate selection of suitable sets of model regressors to be used as inputs of neural network-based estimators to be used online for failure detection. The setup of the proposed fault detection method is based on the statistical analysis of the residual signals in fault-free conditions, which, in turn, allows the tuning of a pair of floating limiter detectors that act as time-varying fault detection thresholds with the objective of reducing both the false alarm rate and the detection delay. The proposed approach has been validated using real flight data by injecting artificial ramp and hard failures on the above sensors. The results confirm the capabilities of the proposed scheme showing accurate detection with a desirable low level of false alarm when compared with an equivalent scheme with conventional “a priori set” fixed detection thresholds. The achieved performance improvement consists mainly in a substantial reduction of the detection time while keeping desirable low false alarm rates

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

Pilot in loop assessment of fault tolerant flight control schemes in a motion flight simulator

This research presents the pilot in the loop tests carried out in a Six-Degree of Freedom (6-DOF) motion flight simulator to evaluate failure detection, isolation and identification (FDII) schemes for an advanced F-15 aircraft. The objective behind this study is to leverage the capability of the flight simulator at West Virginia University (WVU) to carry out a performance assessment of neurally augmented control algorithms developed on a Matlab/Simulink RTM platform. The experimental setup features an interface setup of Gen-2 SimulinkRTM schemes with MOTUS Flight Simulator (MFS). The set up is a close substitute to a real flight and thus is helpful in evaluation of the schemes in a realistic manner. The graphics in X-plane is used to obtain visual cues and the motion platform is used to obtain motion cues in the simulator cockpit. The whole set-up enables the pilot to respond with a joystick in the advent of a failure as he would otherwise in a real flight. The pilot response in maintaining the mission profile is different for different neural network augmentations and thus an indication of performance comparison of these schemes. Secondly, FDII schemes are developed for a sensor and actuator failure using an adaptive threshold for cross-correlation coefficients of the angular rates of the aircraft. Failure detection, isolation and identification logic is formulated based on monitoring the cross-correlation parameters with their Floating Limiter (FL) bounds. The FDII scheme developed shows a good performance with desktop simulation because of no pilot activity but with a pilot in the loop significant cross-correlation of the rates occur and hence the scheme become more susceptible to wrongs FDII. In addition, the pilot might induce some coupling of the cross-correlation parameters between detection and identification time which may trigger false detections and may configure the controller differently based on incorrect detection. Thus it is necessary that FDII scheme accommodate real flight conditions. The performance of the FDII schemes is improved with a pilot in the loop by monitoring the cross-correlation parameters and fine tuning FDII algorithms for real situations. This study has set up an excellent example to effectively utilize the aural, visual and motion cues to create a higher level of simulation complexity in designing control algorithms

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

Exploring the role of system operation modes in failure analysis in the context of first generation cyber-physical systems

Typically, emerging system failures have a strong impact on the performance of industrial systems as well as on the efficiency of their operational and servicing processes. Being aware of these, maintenance and repair researchers have developed multiple failure detection and diagnosis techniques that allow early recognition of system or component failures and maintaining continuous system operation in a cost-effective way. However, these techniques have many deficiencies in the case of self-tuning first generation cyber-physical systems (1G-CPSs). The reason is that these systems compensate for the effects of emerging system failures until their resources are exhausted, and the compensatory actions not only mask the failures, but also make their recognition difficult. Late recognition of failures is however in contrast with the principles of preventive maintenance. Therefore, the promotion research concentrated on the issue of recognizing and forecasting failures under dynamic and adaptive behavior of 1G-CPSs. CPSs are enabled to compensate for failure symptoms by changing their system operation modes (SOMs). It was also observed that transitions of SOMs reduce the reliability of a signal-based failure diagnosis. It was hypothesized that the frequency and the duration of the changes of the operational states of the 1G-CPS may be strong indicators of the failure emergence phenomenon and that investigation of SOMs facilitates early detection of failures. Therefore, the completed exploratory studies were aimed at exploring how the frequency and duration of transitions of SOMs can be brought into correlation with specific types of failures, and how they can be computed as measures of failure occurrence. The obtained results revealed that system failures tend to induce unusual system operation modes that can be used as basis for failure characterization, and even for failure forecasting. The empirical research made use of a cyber-physical greenhouse testbed to get experimental data and was completed by the development of computational model. A failure injection strategy was implemented in order to induce failure occurrence in a controlled manner. The proposed approach can be applied as a basis of forecasting system failures of 1G-CPSs, but additional research seems to be necessary