FAULT DETECTION AND ISOLATION
Model-based fault diagnosis for aerospace systems: a survey (2012)
This 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
modelling 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.
Keywords: aerospace systems, aircraft, analytical redundancy, fault diagnosis, fault detection and
isolation, flight control systems, health monitoring, non-linear systems.
A Comparative Study into Observer based Fault Detection and Diagnosis in DC Motors: Part-I
A model based fault detection and diagnosis technique for DC motor is proposed in this paper. Fault
detection using Kalman filter and its different variants are compared. Only incipient faults are
considered for the study. The Kalman Filter iterations and all the related computations required for
fault detection and fault confirmation are presented. A second order linear state space model of DC
motor is used for this work. A comparative assessment of the estimates computed from four
different observers and their relative performance is evaluated.
Keywords—DC motor model, Fault detection and diagnosis Kalman Filter, Unscented Kalman Filter
A Statistical Method for the Detection of Sensor Abrupt Faults in Aircraft Control Systems
Aircraft sensors are important for proper operation and safety, and their condition is conventionally
monitored based upon the hardware redundancy principle. In this work a statistical method capable
of independently monitoring a single sensor, and thus enhancing reliability and overall system safety,
is introduced. The method’s main advantages are simplicity, applicability to a wide variety of aircraft
operating conditions, the handling of uncertainties, no need for additionally monitored signals, and
no need for physics based aircraft dynamics models. The method is based on a statistical time series
framework accounting for random effects and uncertainties, and exploits the fact that abrupt faults
are characterized by time constants smaller than those of the aircraft. It employs monitored signal
nonstationarity removal, signal whitening via novel pooled autoregressive modeling, statistical
decision making, as well as electronic spike/glitch removal logic. The method effectiveness is
demonstrated within the simulation environment of a small commercial aircraft via test cases and
Monte Carlo experiments with abrupt faults occurring in an angle-of-attack sensor.
Index Terms—Aircraft control systems, angle-of-attack sensor, fault diagnosis, sensor abrupt faults,
statistical methods.
Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications
Sensor faults continue to be a major hurdle for systems health management to reach its full
potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult
to seed particular sensor faults. Therefore, research is underway to better understand the
different fault modes seen in sensors and to model the faults. The fault models can then be
used in simulated sensor fault scenarios to ensure that algorithms can distinguish between
sensor faults and system faults. The paper illustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and
position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were
simulated and injected into the experimental data, with the goal of making these simulations as
realistic as feasible. A neural network based classifier was then created and tested on both
experimental data and the more challenging randomized data sequences. Additional studies
were also conducted to determine sensitivity of detection and disambiguation efficacy to
severity of fault conditions.
Index Terms— modeling, transducers, fault diagnosis
Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults
This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying
thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive
observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying
faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform
fault estimation instead of using integrators that are commonly used in classical adaptive
observers. The stability of estimation-error dynamics is established and proved. An illustrative
example clearly shows that time-varying thruster faults can be accurately identified.
Keywords: iterative learning observer, time-varying thruster faults, fault identification, satellite
systems.
On board fault detection and identification in railway vehicle suspensions via a Functional Model
Based Method
Abstract: The combined problem of fault detection, identification, and magnitude estimation in
railway vehicle suspensions is tackled via a novel Functional Model Based Method. This method is
based upon a new class of stochastic functional models, which are capable of accurately representing
the system in a faulty state for its continuum of fault magnitudes, as well as statistical decision
theory tools. The method’s effectiveness in properly detecting, identifying, and estimating the
magnitude of suspension faults, by using only two measured vibration signals, is demonstrated via
Monte Carlo experiments with a six degree-of-freedom railway vehicle model.
Sensor fault detectıon and analytıcal redundancy satellıte launcher flıght control system
Abstract: A study on the inclusion of analytical redundancy and of an instrument fault detection
scheme ( IFD ) into a flightcontrol system has been performed for a satellite launcher using the
longitudinal model. The study was mainly focused in the fault diagnosis aspect, and it reports the
conclusions obtained for this kind of control system. A fault diagnosis logic has been created based
on nonlinear functions, its derivatives with respect to time, and on the control rate effort. Several
simulations were run to assess the system performance, and a study about the robustness of the
system with respect to system parameters uncertainties was also performed and both are reported
here. It was found that the system is able to reconfigure the control law safely in almost all
the situations and the false alarm rate presented was also very low. The system is simple as the
same observers are used for the decision logic and for the alternative observer-based control
laws. The main feature of the work is the inclusion of the derivatives of the nonlinear functions into
the decision logic.
Wind turbine fault detection and fault tolerant control - a second challenge
Abstract— In this updated edition of a previous wind turbine fault detection and fault tolerant
control challenge, we present a more sophisticated wind turbine model and updated fault
scenarios to enhance the realism of the challenge and therefore the value of the solutions. This
paper describes the challenge model and the requirements for challenge participants. In
addition, it motivates many of the faults by citing publications that give field data from wind turbine
control tests.