Sensor fault detection and isolation using multiple robust filters for linear systems with time-varying parameter uncertainty and error variance constraints

In this paper, a robust sensor fault detection and isolation (FDI) strategy is proposed by means of the multiple model (MM)-based scheme. The proposed approach is composed of robust Kalman filters (RKF) with error variance constraints that are designed for a linear discrete-time system with parameter uncertainties affecting all the system matrices. The robust filter parameters are designed by solving two algebraic Riccati equations expressed in linear matrix inequality feasibility conditions. The goal of this multiobjective problem is to design a robust filter which is not affected by system perturbations and satisfies the performance requirements including an asymptotically stable filtering process as well as individually bounded estimation error variances with predefined values. The proposed multiple RKFs are used in the MM-based strategy to detect and isolate sensor bias faults having different severities. Finally, an illustrative numerical example is given to demonstrate the robustness and the estimation accuracy levels of our proposed FDI scheme as compared with a standard linear Kalman filter-based FDI method. 2014 IEEE.Qatar National Research FundScopu

Multiple-model based sensor fault diagnosis using hybrid kalman filter approach for nonlinear gas turbine engines

In this paper, an efficient sensor fault detection and isolation (FDI) strategy is proposed based on multiple-model (MM) approach. The scheme is composed of hybrid kalman filters (HKF) by integrating a nonlinear gas turbine engine model that represents the operational engine model with a number of piecewise linear (PWL) models to estimate sensor outputs. The proposed FDI scheme is capable of detecting and isolating permanent sensor bias faults during the entire operational regime of the engine by interpolating the PWL models using a Bayesian approach. Another important aspect of our proposed FDI strategy is its effectiveness within the engine life cycle by periodically updating the model to the degraded health parameters, that one estimated by means of an off-line trend monitoring system that is based on post flight data. The simulation results demonstrate the effectiveness of our proposed online sensor fault diagnosis scheme as well as the robustness of our technique with respect to the engine health parameters degradations. 2013 AACC American Automatic Control Council.Qatar National Research FundScopu

Cyber – physiochemical interfaces

Living things rely on various physical, chemical, and biological interfaces, e.g., somatosensation, olfactory/gustatory perception, and nervous system response. They help organisms to perceive the world, adapt to their surroundings, and maintain internal and external balance. Interfacial information exchanges are complicated but efficient, delicate but precise, and multimodal but unisonous, which has driven researchers to study the science of such interfaces and develop techniques with potential applications in health monitoring, smart robotics, future wearable devices, and cyber physical/human systems. To understand better the issues in these interfaces, a cyber–physiochemical interface (CPI) that is capable of extracting biophysical and biochemical signals, and closely relating them to electronic, communication, and computing technology, to provide the core for aforementioned applications, is proposed. The scientific and technical progress in CPI is summarized, and the challenges to and strategies for building stable interfaces, including materials, sensor development, system integration, and data processing techniques are discussed. It is hoped that this will result in an unprecedented multi‐disciplinary network of scientific collaboration in CPI to explore much uncharted territory for progress, providing technical inspiration—to the development of the next‐generation personal healthcare technology, smart sports‐technology, adaptive prosthetics and augmentation of human capability, etc.NRF (Natl Research Foundation, S’pore)ASTAR (Agency for Sci., Tech. and Research, S’pore)MOE (Min. of Education, S’pore)Accepted versio