A fault detection method for hysteretic base-isolation systems

Postprint (published version

Multisensor Fault Identification Scheme Based on Decentralized Sliding Mode Observers Applied to Reconfigurable Manipulators

This paper concerns with a fault identification scheme in a class of nonlinear interconnected systems. The decentralized sliding mode observer is recruited for the investigation of position sensor fault or velocity sensor fault. First, a decentralized neural network controller is proposed for the system under fault-free state. The diffeomorphism theory is utilized to construct a nonlinear transformation for subsystem structure. A simple filter is implemented to convert the sensor fault into pseudo-actuator fault scenario. The decentralized sliding mode observer is then presented for multisensor fault identification of reconfigurable manipulators based on Lyapunov stable theory. Finally, two 2-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in numerical simulation. The results demonstrate that one joint’s fault does not affect other joints and the sensor fault can be identified precisely by the proposed decentralized sliding mode observer

Model-Based Parameter Estimation for Fault Detection Using Multiparametric Programming

Fault detection has become increasingly important for improving the reliability and safety of process systems. This paper presents a model-based fault detection methodology for nonlinear process systems. The objective of this work is to detect faults by estimating the model parameters using multiparametric programming. The parameter estimates are obtained as an explicit function of the measurements by using multiparametric programming. The diagnosis of fault is carried out by monitoring the changes in the residual of model parameters. Case studies of fault detection for a single stage evaporator system and quadruple tank system are presented. A number of faulty and fault-free scenarios are considered to show the effectiveness of the presented approach. The proposed approach successfully estimates the model parameters and detects the faults through a simple function evaluation of explicit functions

Robust Adaptive Fault-Tolerant Control of Stochastic Systems with Modeling Uncertainties and Actuator Failures

This paper deals with the problem of fault-tolerant control (FTC) of uncertain stochastic systems subject to modeling uncertainties and actuator failures. A robust adaptive fault-tolerant controller design method based on stochastic Lyapunov theory is developed to accommodate the negative impact on system performance arising from uncertain system parameters and external disturbances as well as actuation faults. There is no need for on-line fault detection and diagnosis (FDD) unit in the proposed FTC scheme, which not only simplifies the design process but also makes the implementation inexpensive. Numerical examples are provided to validate and illustrate the benefits of the proposed control method

Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

In this work, a methodology for fault detection in wastewater treatment systems, based on parameter estimation, using multiparametric programming is presented. The main idea is to detect faults by estimating model parameters, and monitoring the changes in residuals of model parameters. In the proposed methodology, a nonlinear dynamic model of wastewater treatment was discretized to algebraic equations using Euler’s method. A parameter estimation problem was then formulated and transformed into a square system of parametric nonlinear algebraic equations by writing the optimality conditions. The parametric nonlinear algebraic equations were then solved symbolically to obtain the concentration of substrate in the inflow, Scin , inhibition coefficient, Ki , and specific growth rate, µo, as an explicit function of state variables (concentration of biomass, X; concentration of organic matter, Sc; concentration of dissolved oxygen, So; and volume, V). The estimated model parameter values were compared with values from the normal operation. If the residual of model parameters exceeds a certain threshold value, a fault is detected. The application demonstrates the viability of the approach, and highlights its ability to detect faults in wastewater treatment systems by providing quick and accurate parameter estimates using the evaluation of explicit parametric functions

Predictive control methods to improve energy efficiency and reduce demand in buildings

Abstract This paper presents an overview of results and future challenges on temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through sophisticated simulation examples. The paper concludes with effective results from model predictive control solutions and identification of important directions for future work

Diseño de un sistema de detección y diagnóstico de fallas basado en modelo para una planta desalinizadora de agua de mar

Los sistemas de diagnóstico de fallas cumplen un papel importante en los sistemas de control en la actualidad, ya que los distintos elementos que componen la planta son susceptibles a fallas. Las fallas que ocurren en una planta desalinizadora por ósmosis inversa pueden traer como consecuencia una mala calidad de agua desalinizada, reducir considerablemente la eficiencia de producción de la planta, poner en peligro la integridad de los operadores de la planta o provocar problemas con el medio ambiente. En este trabajo se desarrolla un modelamiento matemático de parámetros concentrados de una planta desalinizadora con un módulo de por ósmosis inversa en configuración enrollamiento en espiral, con el objetivo de obtener un conjunto de ecuaciones analíticas que describan el comportamiento dinámico de la planta y comprender las principales variables que intervienen en el modelo. A partir del modelo obtenido, se hace un análisis estructural del proceso para luego obtener el conjunto de relaciones de redundancia analítica (RRA) que servirán de base para la detección de fallas. Se definen las principales fallas en sensores, actuadores y variables internas que intervienen en el proceso de desalinización por OI, luego se desarrolla un algoritmo para la simulación del modelo dinámico de la planta. Se realiza la simulación de fallas en los distintos componentes del sistema y se compara el sistema en modo normal de funcionamiento con el sistema sometido a fallas. Se realizan pruebas de detectabilidad y diagnosticabilidad de fallas mediante un algoritmo de emparejamiento de restricciones (matching) a partir de las RRA obtenidas, además mediante el cálculo de posibles mínimos subconjuntos de prueba MTES, se realizaron pruebas adicionales de aislabilidad de fallas simultáneas. De las pruebas de simulación, se consiguió detectar todas las fallas consideradas y mediante el análisis de diagnosticabilidad se logró aislar adecuadamente la mayoría de ellas, se logró determinar la forma de generar RRA a partir del cálculo de MTES para aislar fallas simultáneas. Finalmente se realizó la propuesta de implementación del sistema de diagnóstico de falla en el Software Rslogix5000 con interfaz Scada FactoryTalk View de Rockewell Automation

Diseño de un sistema de detección y diagnóstico de fallas basado en modelo para una planta desalinizadora de agua de mar

Los sistemas de diagnóstico de fallas cumplen un papel importante en los sistemas de control en la actualidad, ya que los distintos elementos que componen la planta son susceptibles a fallas. Las fallas que ocurren en una planta desalinizadora por ósmosis inversa pueden traer como consecuencia una mala calidad de agua desalinizada, reducir considerablemente la eficiencia de producción de la planta, poner en peligro la integridad de los operadores de la planta o provocar problemas con el medio ambiente. En este trabajo se desarrolla un modelamiento matemático de parámetros concentrados de una planta desalinizadora con un módulo de por ósmosis inversa en configuración enrollamiento en espiral, con el objetivo de obtener un conjunto de ecuaciones analíticas que describan el comportamiento dinámico de la planta y comprender las principales variables que intervienen en el modelo. A partir del modelo obtenido, se hace un análisis estructural del proceso para luego obtener el conjunto de relaciones de redundancia analítica (RRA) que servirán de base para la detección de fallas. Se definen las principales fallas en sensores, actuadores y variables internas que intervienen en el proceso de desalinización por OI, luego se desarrolla un algoritmo para la simulación del modelo dinámico de la planta. Se realiza la simulación de fallas en los distintos componentes del sistema y se compara el sistema en modo normal de funcionamiento con el sistema sometido a fallas. Se realizan pruebas de detectabilidad y diagnosticabilidad de fallas mediante un algoritmo de emparejamiento de restricciones (matching) a partir de las RRA obtenidas, además mediante el cálculo de posibles mínimos subconjuntos de prueba MTES, se realizaron pruebas adicionales de aislabilidad de fallas simultáneas. De las pruebas de simulación, se consiguió detectar todas las fallas consideradas y mediante el análisis de diagnosticabilidad se logró aislar adecuadamente la mayoría de ellas, se logró determinar la forma de generar RRA a partir del cálculo de MTES para aislar fallas simultáneas. Finalmente se realizó la propuesta de implementación del sistema de diagnóstico de falla en el Software Rslogix5000 con interfaz Scada FactoryTalk View de Rockewell Automation

Robust Fault Estimation for a Class of T-S Fuzzy Singular Systems with Time-Varying Delay via Improved Delay Partitioning Approach

The problem of delay-dependent robust fault estimation for a class of Takagi-Sugeno (T-S) fuzzy singular systems is investigated. By decomposing the delay interval into two unequal subintervals and with a new and tighter integral inequality transformation, an improved delay-dependent stability criterion is given in terms of linear matrix inequalities (LMIs) to guarantee that the fuzzy singular system with time-varying delay is regular, impulse-free, and stable firstly. Then, based on this criterion, by considering the system fault as an auxiliary disturbance vector and constructing an appropriate fuzzy augmented system, a fault estimation observer is designed to ensure that the error dynamic system is regular, impulse-free, and robustly stable with a prescribed H∞ performance satisfied for all actuator and sensor faults simultaneously, and the obtained fault estimates can practically better depict the size and shape of the faults. Finally, numerical examples are given to show the effectiveness of the proposed approach

An Inversion-Based Approach to Fault Detection and Isolation in Switching Electrical Networks

Abstract-This paper proposes a framework for fault detection and isolation (FDI) in electrical energy systems based on techniques developed in the context of invertibility of switched systems. In the absence of faults-the nominal mode of operation-the system behavior is described by one set of linear differential equations or more in the case of systems with natural switching behavior, e.g., power electronics systems. Faults are categorized as hard and soft. A hard fault causes abrupt changes in the system structure, which results in an uncontrolled transition from the nominal mode of operation to a faulty mode governed by a different set of differential equations. A soft fault causes a continuous change over time of certain system structure parameters, which results in unknown additive disturbances to the set(s) of differential equations governing the system dynamics. In this setup, the dynamic behavior of an electrical energy system (with possible natural switching) can be described by a switched state-space model where each mode is driven by possibly known and unknown inputs. The problem of detection and isolation of hard faults is equivalent to uniquely recovering the switching signal associated with uncontrolled transitions caused by hard faults. The problem of detection and isolation of soft faults is equivalent to recovering the unknown additive disturbance caused by the fault. Uniquely recovering both switching signal and unknown inputs is the concern of the (left) invertibility problem in switched systems, and we are able to adopt theoretical results on that problem, developed earlier, to the present FDI setting. The application of the proposed framework to fault detection and isolation in switching electrical networks is illustrated with several examples. Index Terms-Electrical energy systems, fault detection and isolation (FDI), invertibility, switched linear systems, switch-singular pairs