DESIGN OF RESTRUCTURABLE ACTIVE FAULT-TOLERANT CONTROL SYSTEMS

A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states

Recursive Actuator Fault Detection and Diagnosis for Emergency Landing of UASs

This paper presents a new scheme for fault detection and diagnosis in spacecraft Attitude Determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the Fault Detection and Diagnosis (FDD) scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through Extended Kalman Filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations in presence of fault in one type of sensors.

In this paper, a fault detection and diagnosis (FDD) scheme is presented for general stochastic dynamic systems with time delays. Originated from some practical processes, it is supposed that the measured information is the probability density function (PDF) of the system output rather than its actual value, which is different from the classical FDD problem. A B-spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B-spline expansion. This leads to a dynamic model between the input and the weights where nonlinearities, uncertainties and time delays are included. As a result, the concerned FDD problem is transformed into a classic nonlinear FDD problem subject to an uncertain nonlinear system with time delays. Feasible criteria to detect the system fault are obtained and an adaptive fault diagnosis method is further presented to produce an estimate of the size of fault. Simple simulations are given to demonstrate the efficiency of the proposed approach.

A LTHOUGH the past three decades have witnessed several important developments in attitude stabilization control [1,2], there still remain certain open problems that are of great theoretical and practical interests, such as actuator constraint. Because of physical limitations, typical control actuators (e.g., reaction wheels and thrusters) for attitude control have an upper bound on the torque they can generate. If this issue is not solved during attitude maneuvering, then the attitude system will continue issuing its commands that may no longer be achievable by the satellite, and the required control will quickly saturate actuators. This will subsequently destabilize and even lead to loss of control of the satellite. Therefore, actuator constraint is one key issue that needs to be addressed. At present, a great deal of attention has been focused on the stability analysis and the attitude control design with saturated actuators [3–6]. Another problem that needs to be addressed is fault tolerant control for the attitude system. Any component or system fault can potentially cause a host of economic and safety problems. As a result, online and real-time fault tolerant control (FTC) design for satellites has received considerable attention [7]. Generally speaking, FTC can be classified into two types: passive and active [8]. The passive FTC is designed to be robust to a class of possible faults. It does not require fault detection and diagnosis (FDD) schemes. In an attempt to develop attitude controllers that can accommodate faults, extensive studies on the passive FTC control system for satellites have been performed [9–11]. The active FTC can react to fault events by using a reconfigurationmechanism. The active FTC relies on the availability of an FDD mechanism that gives, in real time, information about the nature and the intensity of the fault. The design of active FTC, especially the fault detection and diagnosis scheme for satellite attitude, has attracted considerable interest [12–14]. Although the methodologies including FDD and control law reconfiguration for the control-type faults of satellite attitude system have been studied extensively (as seen in the preceding references), the problem that is not solved yet is the effective and practical strategies for handling severe failure cases combining with actuators constraint. Lack of such effective FTC strategies may lead to substantial performance deterioration and even the loss of a satellite. The clear knowledge of the link between control reconfiguration and the actuator input constraint is still lacking. Because the control reconfiguration at different time instants with different types of faults has greatly different effects on control actuators saturation, there is no explicit and simple relationship between them, which makes this problem even more difficult. This leads to an important practical problem: how tomake use of the control reconfiguration on available control redundancy. Motivated by this problem, a novel attitude control approach is presented in this paper; an observer-based FDD scheme combining a sliding mode controller by using the estimated information is designed in a framework of active FTC system. The main contribution of this study, relative to otherworks, is that actuator faults, actuator constraints, and external disturbances are simultaneously addressed in an attitude FTC system design.

Data Driven Process Monitoring, from Fault Detection and Diagnosis Points of View, in Industry 4.0 Context

The success of a fault detection and diagnosis (FDD) scheme depends not alone on developing an advanced detection scheme. To enable successful deployment in industrial applications, an economically optimal development of FDD schemes are required. This paper reviews and discusses the gained experiences achieved by employing a combination of various techniques, methods, and algorithms, which are proposed by academia, on an industrial application. The main focus is on sharing the "lessons learned" from developing and employing fault-tolerant functionalities to a controlled process in order to meet the industrial needs while satisfying economically motivated constraints.

A new optimal fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (pdf's) of the system. The square-root B-spline functional approximation technique is used to formulate the output pdf's with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Feasible criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. In order to improve FDD performances, two optimization measures, namely guaranteed cost performance and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> performance, are applied to optimize the observer design. Simulations are given to demonstrate the efficiency of the proposed approach

A strategy of fault detection and diagnosis (FDD) for HVAC sub-systems at the system level is presented in this paper. In the strategy, performance indices (PIs) are proposed to indicate the health condition of different sub-systems including cooling tower system, chiller system, secondary pump systems before heat exchangers, heat exchanger system and secondary pump system after heat exchangers. The regression models are used to estimate the PIs as benchmarks for comparison with monitored PIs. The online adaptive threshold determined by training data and monitored data is used to determine whether the PI residuals between the estimation and calculation or monitoring are in the normal working range. A dynamic simulation platform is used to simulate the faults of different sub-systems and generate data for training and validation. The proposed FDD strategy is validated using the simulation data and proven to be effective in the FDD of heating, ventilating and air-conditioning (HVAC) sub-systems. Copyright © 2009 John Wiley & Sons, Ltd.

Modelling and Fault Diagnosis of Stator Inter-Turn Short Circuit in Doubly Fed Induction Generators

This paper is devoted to the design of a nonlinear active fault-tolerant tracking control (AFTTC) scheme for an unmanned quadrotor helicopter (UQH) under sensor faults. The proposed AFTTC scheme is divided into two loops, where the outer loop and inner loop controllers are designed for the position control and the attitude control, respectively. The sliding mode control (SMC) algorithm is introduced to make the UQH track the desired trajectory and yaw angle, and to stabilize the pitch and roll motions. For implementing the active fault-tolerant control, a fault detection and diagnosis (FDD) scheme is constructed by utilizing a robust three-step unscented Kalman filter. Based on the precise fault information, the SMC-based control laws are updated online to mitigate the adverse effects of sensor faults. Simulation results show that the proposed AFTTC scheme works well in both normal and different faulty scenarios.

In this paper, a fault detection and diagnosis (FDD) scheme is developed for a class of intensified HEX/reactor, in which faults caused by sensor, actuator, and process are taken into account in the unified framework. By considering overall heat transfer coefficient as a function of fouling and fluid flow rate, a dynamic model which is capable of identifying these two faults simultaneously is derived. Sensor measurements, together with estimation by adaptive high gain observers, are processed, aimed at identifying sensor faults and providing adequate estimation to substitute faulty measurements. Then reliable measurements are fed to several banks of interval filters to generate several banks of residuals; each bank of residuals is sensitive to a particular process parameter/actuator. By evaluating these residuals, process/actuator fault isolation and identification are achieved. The proposed strategy is applied to actual data retrieved from a new intensified heat exchanger reactor. Simulation results confirm the applicability and robustness of the proposed methodology.

The control of systems that involve friction presents interesting challenges. Recent research has focused on detailed modelling of friction phenomena as a very complex and difficult modelling challenge. However, the friction effects acting in a dynamic system can be viewed as actuator faults with time-varying characteristics to be estimated and compensated within a Fault Detection and Diagnosis (FDD) scheme, so that the limitations arising from the use a friction model are obviated. This work is motivated by the utilisation of robust Linear Parameter Varying (LPV) estimation approach providing effective and robust fault estimation. The approach is illustrated using a two-link manipulator system with Stribeck friction. Results show that the time-varying friction forces on each joint can be simultaneously and robustly estimated through the online measurement of the varying parameters.

This paper presents a methodology for satellite sensors' fault detection and diagnosis (FDD) in a fault-tolerant attitude determination system using a nonlinear interacting-multiple-model approach (IMM). Moreover, the fault-tolerant attitude determination system utilizes the IMM approach in the presence of sensors' total fault to estimate faulty sensors and attitude states. This methodology relies on the most conventional satellite attitude sensors including magnetometer, sun sensor, and gyroscope measurements. The presented scheme leads to a significant computational time-saving method due to considering a pre-filter Kalman for fault detection, hence, the proposed FDD approach consists of two stages. In the first stage, the pre-filter Kalman detects any sensor fault and attitude states. In contrast, in the next stage, fault diagnosis and optimal attitude estimation are obtained through the IMM approach. Therefore, the IMM algorithm will be activated to extract the optimal estimation and identify faulty sensor(s). A Monte Carlo type approach is used to verify this technique for various initial conditions. Through numerical and Monte Carlo simulations, it is shown that the proposed scheme is robust against sensors' total fault, and the system is capable of detecting and isolating accurately the faulty sensor. Hardware in the loop tests using satellite dynamics simulator are also used to validate the presented system performance.

In this paper, a high-gain nonlinear observer based fault detection and diagnosis (FDD) approach is proposed for a general class of nonlinear uncertain systems with measured output probability density functions (PDFs). The objective of the presented FDD algorithm is to use the measured output probability density functions (PDFs) and the input of the system to construct a exponential observer-based residual generator such that the fault can be detected and diagnosed. The main result is given in a constructive manner by developing a novel nonlinear observer, without resort to any linearization. By a coordinates transformation, the design of the proposed observer does not necessitate the resolution of kind of linear matrix inequalities (LMIs) and its expression is explicitly given. The exponential convergence of the errors in the presence of parameters uncertainties is proved to guarantee the fastness of the proposed fault diagnosis scheme. Furthermore, the bound of the estimation errors in the presence the faults is minimized by appropriately choosing the parameters of the presented observer. Finally a simulation example is given to illustrate the efficiency of the proposed fault detection and diagnosis method.