12 research outputs found
Adaptive Kalman Filter for Actuator Fault Diagnosis
International audienceAn adaptive Kalman filter is proposed in this paper for actuator fault diagnosis in discrete time stochastic time varying systems. By modeling actuator faults as parameter changes, fault diagnosis is performed through joint state-parameter estimation in the considered stochastic framework. Under the classical uniform complete observability-controllability conditions and a persistent excitation condition, the exponential stability of the proposed adaptive Kalman filter is rigorously analyzed. The minimum variance property of the combined state and parameter estimation errors is also demonstrated. Numerical examples are presented to illustrate the performance of the proposed algorithm
On the optimality of Kalman Filter for Fault Detection
Kalman filter is widely used for residual generation in fault detection. It
leads to optimality in fault detection using some performance indices and also
leads to statistically sound residual evaluation and threshold setting. This
paper shows that these nice features do not necessarily imply an optimal fault
detection performance. Based on a performance index related to fault detection
rate and false alarm rate, several occasions where Kalman filter should not be
used are pointed out; further the residual evaluation and threshold setting are
discussed, in which it is pointed out that in stochastic setting an optimal
statistical test of Kamlan filter is not related to optimality of commonly used
detection performance indicators. The theoretical analysis is verified through
Monte Carlo simulations
Simultaneous Actuator and Sensor Faults Estimation for Aircraft Using a Jump-Markov Regularized Particle Filter
International audienceThe advances in aircraft autonomy have led to an increased demand for robust sensor and actuator fault detection and estimation methods in challenging situations including the onset of ambiguous faults. In this paper, we consider potential simultaneous fault on sensors and actuators of an Unmanned Aerial Vehicle. The faults are estimated using a Jump-Markov Regularized Particle Filter. The jump Markov decision process is used within a regularized particle filter structure to drive a small subset of particles to test the likelihood of the alternate hypothesis to the current fault mode. A prior distribution of the fault is updated using innovations based on predicted control and measurements. Fault scenarios were focused on cases when the impacts of the actuator and sensor faults are similar. Monte Carlo simulations illustrate the ability of the approach to discriminate between the two types of faults and to accurately and rapidly estimate them. The states are also accurately estimated
Fault Separation Based on An Excitation Operator with Application to a Quadrotor UAV
This paper presents an excitation operator based fault separation
architecture for a quadrotor unmanned aerial vehicle (UAV) subject to loss of
effectiveness (LoE) faults, actuator aging, and load uncertainty. The actuator
fault dynamics is deeply excavated, containing the deep coupling information
among the actuator faults, the system states, and control inputs. By explicitly
considering the physical constraints and tracking performance, an excitation
operator and corresponding integrated state observer are designed to estimate
separately actuator fault and load uncertainty. Moreover, a fault separation
maneuver and a safety controller are proposed to ensure the tracking
performance when the excitation operator is injected. Both comparative
simulation and flight experiments have demonstrated the effectiveness of the
proposed scheme while maintaining high levels of tracking performance
Regularized Adaptive Observer to Address Deficient Excitation
International audienceAdaptive observers are recursive algorithms for joint estimation of both state variables and unknown parameters. Usually some persistent excitation (PE) condition is required for the convergence of adaptive observers. However, in practice, it may happen that the PE condition is not satisfied, because the available sensor signals do not contain sufficient information for the considered recursive estimation problem, which is ill-posed. To remedy the lack of PE condition, inspired by typical methods for solving ill-posed inverse problems, this paper proposes a regularized adaptive observer for general linear time varying (LTV) systems. Two regularization terms are introduced in both state and parameter estimation recursions, in order to preserve the state-parameter decoupling transformation involved in the design of the adaptive observer. Like in typical ill-posed inverse problems, regularization implies an estimation bias, which can be reduced by using prior knowledge about the unknown parameters
Filtros de Kalman adaptativos para sistemas não-lineares
Dissertação (mestrado)—Universidade de BrasÃlia, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2019.O filtro de Kalman é amplamente utilizado para estimar estados em propostas de controle.
Entretanto, ele requer correto conhecimento das estatÃsticas de incertezas para o bom desempenho
em implementações em sistemas reais. Deste modo, este trabalho apresenta nova proposta de
adaptação em covariância de incertezas de processo aplicada em filtro de Kalman estendido e
filtro de Kalman unscented para sistemas não-lineares. A covariância de incertezas de processo é
estimada em tempo real através de média móvel exponencial. Simulações numéricas de sistema
massa-mola-amortecedor não-linear, bola e barra (instável), e quatro tanques (múltiplas entradas
múltiplas saÃdas e fase não mÃnima) foram realizadas para ilustrar o bom desempenho com boas
estimativas e baixos tempos de execução obtido dos algoritmos propostos.CAPESThe Kalman filter is one of the most widely used methods for state estimation and control
purposes. However, it requires correct knowledge of noise statistics in order to obtain optimal
performance in real-life applications. Therefore, this work presents a novel approach to adapt
the process noise covariance applied in nonlinear systems by using the extended Kalman filter
and unscented Kalman filter. The changes of process noise covariance are estimated in real-time
based on exponential moving average. The great performance of the proposed algorithms shown
by good estimations with low execution time is demonstrated with numerical simulations through
examples: nonlinear mass-spring-damper system, ball beam (unstable), and four tank (multiple
input multiple output and non minimal phase)
Approaches for diagnosis and prognosis of asset condition: application to railway switch systems
This thesis presents a novel fault diagnosis and prognosis methodology which is applied to railway switches. To improve on existing fault diagnosis, energy-based thresholding wavelets (EBTW) are introduced. EBTW are used to decompose sensor measurement signals, and then to reconstruct them within a lower dimensional feature vector. The extracted features replace the original signals and are fed into a neural network classifier for fault diagnosis. Compared to existing wavelet-based feature extraction methods, the new EBTW method has the advantage of an intrinsic energy conservation property during the wavelet transform process. The EBTW method localises and redistributes the signal energy to realise an efficient feature extraction and dimension reduction.
The presented diagnosis approach is validated using real-world switch data collected from the Guangzhou Metro in China. The results show that the proposed diagnosis approach can achieve 100% accuracy in identifying a railway switch overdriving fault with various severities, improving upon existing methods of conventional discrete wavelet transform (C-DWT) and soft-thresholding discrete wavelet transform (ST-DWT) by 8.33% and 16.67%, respectively.
The presented prognosis approach is constructed based on traditional data-driven prognosis modelling. The concept of a remaining maintenance-free operating period (RMFOP) is introduced, which transforms the usefulness of sensor measurement data that is readily available from operations prior to failure. Useful features are then extracted from the original measurement data, and modelled using linear and exponential regression curve fitting models. By extracting key features, the original measurement data can be transformed into degradation signals that directly reflect the variations in each movement of a switch machine. The features are then fed into regression models to derive the probability distribution of switch residual life. To update the probability distribution from one operation to the next, Bayesian theory is incorporated into the models.
The proposed RMFOP-based approach is validated using real-world electrical current sensor measurement data that were collected between January 2018 and February 2019 from multiple operational railway switches across Great Britain. The results show that the linear model and the exponential model can both provide residual life distributions with a satisfactory prediction accuracy. The exponential model demonstrates better predictions, the accuracy of which exceeds 95% when 90% life percentage has elapsed. By applying the RMFOP-based prognosis approach to operational data, the railway switch health condition that is affected by incipient overdriving failure is predicted
Sensors Fault Diagnosis Trends and Applications
Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis
Control predictivo tolerante a fallos aplicado a sistemas de energÃa
Los sistemas de energÃa distribuidos están adquiriendo un papel cada vez más importante en la
evolución del sistema eléctrico tradicional. En la actualidad, se está trabajando en el desarrollo
de aplicaciones que aseguren la disponibilidad y su correcto funcionamiento. El sistema de
gestión de energÃa debe ser capaz de mitigar los efectos provocados por fallos y, por tanto,
llevar al sistema a un escenario seguro. En este contexto, el diagnóstico y mitigación de los
fallos son temas principales a tratar.
En esta tesis se estudia la integración de sistemas de control tolerantes a fallos en sistemas de
gestión de energÃa haciendo uso de una planta experimental situada en la Universidad de
Sevilla. Con este propósito, se presenta un enfoque de control predictivo basado en modelos
para la gestión de energÃa desde el punto de vista de la mitigación de fallos. Inicialmente, se
realiza un diseño previo de una estrategia de control tolerante a fallos utilizando métodos de
diagnóstico y mecanismos de tolerancia a fallos tradicionales. Posteriormente se desarrollan
métodos que mejoran el desempeño del sistema de diagnóstico propuesto inicialmente y se
desarrollan aplicaciones que utilizan mecanismos de tolerancia en sistemas de gestión de
energÃa. Además, se desarrolla un mecanismo de tolerancia especÃfico para este tipo de
sistemas basado en técnicas de respuesta a la demanda que aplica la reducción de carga para
mitigar las consecuencias del fallo. Para mostrar la eficacia de las contribuciones realizadas se
llevan a cabo ensayos en la planta experimental, citada anteriormente, y se realiza una
comparación de los distintos métodos desarrollados para mejorar la robustez del diagnóstico
de fallos