Fault Diagnosis Techniques for Linear Sampled Data Systems and a Class of Nonlinear Systems

This thesis deals with the fault diagnosis design problem both for dynamical continuous time systems whose output signal are affected by fixed point quantization,\ud referred as sampled-data systems, and for two different applications whose dynamics are inherent high nonlinear: a remotely operated underwater vehicle and a scramjet-powered hypersonic vehicle.\ud Robustness is a crucial issue. In sampled-data systems, full decoupling of disturbance terms from faulty signals becomes more difficult after discretization.\ud In nonlinear processes, due to hard nonlinearity or the inefficiency of linearization, the “classical” linear fault detection and isolation and fault tolerant control methods may not be applied.\ud Some observer-based fault detection and fault tolerant control techniques are studied throughout the thesis, and the effectiveness of such methods are validated with simulations. The most challenging trade-off is to increase sensitivity to faults and robustness to other unknown inputs, like disturbances. Broadly speaking, fault detection filters are designed in order to generate analytical diagnosis functions, called residuals, which should be independent with respect to the system operating state and should be decoupled from disturbances. Decisions on the occurrence of a possible fault are therefore taken on the basis such residual signals

Multivariable Robust Fault Tolerant Control For Work-Class Remotely Operated Vehicle

To deal with complex disturbances and the presence of partial loss of propeller effectiveness in work-class remotely operated vehicles (ROVs), a method of robust fault tolerant control is proposed, which is based on adaptive sliding mode control. In this approach, adaptive technique is employed to estimate the bounds’ information of external complex disturbances and the effectiveness loss of the propeller. And a sliding mode controller is then designed to achieve fault tolerant control and external disturbance rejection. Corresponding stability of the closed-loop control system is analyzed using Lyapunov stability theory. Apply this method to trajectory tracking control of work-class ROVs, the simulation results validate that great fault tolerant capability and a good performance of external disturbance rejection can be achieved even under partial loss of propeller effectiveness

Navigation System Fault Diagnosis for Underwater Vehicle

This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using different approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal based to cope with non-ideal properties seen in real data. Detection of both sensor and thruster failures are demonstrated. Isolation is performed using the residual signature of detected faults and the change detection algorithm is used to assess severity of faults by estimating their magnitude. Numerical simulations and sea trial data show results with very favorable balance between detection and false alarm probabilities.(c) IFAC 2014. This is the authors' accepted and refereed manuscript to the article

Fault-Tolerant Control For A Remotely Operated Vehicle (Rov) Propulsion System

Remotely Operated Vehicle (ROV) propulsions system is frequently exposed to harsh operating and underwater environments. Faults and undesired working conditions contribute to performance degradation thus repair actions are required. Stop of operation causes operational cost to increase. Therefore, a Fault-Tolerant Control System (FTCS) is introduced to deal with this situation. This method aims to ensure reliability, sustainability and safety of a dynamical system. This thesis presents a fault-tolerant control specifically designed for ROV electric propulsion system with brushed DC motor thrusters. There are two components in FTCS which are the Fault Detection and Diagnosis (FDD) and Controller Re-Design (CRD). The FDD is done by monitoring two thruster parameters i.e. armature voltage and current load and compare between actual and reference process parameters. Via statistical design of experiment techniques, an offline experiment is performed to simulate possible event of faults. Analysis of variance (ANOVA) methods such as two-factor factorial design and Tukey’s Kramer rule are used to analyze the faults and provides the reference model to implement the controller re-design i.e. fault accommodation. A Takagi-Sugeno (T-S) fuzzy system is used to design the fault accommodation and ROV motion controller. The FTCS method has been tested in fresh water pool and proved to be fast in handling the thruster faults. It takes about 500 ms for a fault in a single thruster to be detected, isolated and new thruster command to be initiated. The FTCS method causes the ROV degree of freedom (DOF) to be reduced to a minimum but the ROV still able to continue the operation

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults. If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and based on a large number of data logged during flights, diagnostic methods are employed to diagnose faults and the performance of these fault detectors are evaluated against flight data. The paper demonstrates a significant potential for reducing the risk of unplanned loss of remotely piloted vehicles used by the Danish Navy for target practice.This is the authors' accepted and refereed manuscript to the article. Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives License

Adaptive and Online Health Monitoring System for Autonomous Aircraft

Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour based on the progression of its sensory values produced by the aircraft's modules, components and/or subsystems. The AHMS indicates erroneous flight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are affecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer flight behaviour or fault tolerance to the aircraft. This allows the aircraft to fly safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

Fault Detection and Fail-Safe Operation with a Multiple-Redundancy Air-Data System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83640/1/AIAA-2010-7855-622.pd