Mixed active/passive robust fault detection and isolation using set-theoretic unknown input observers

2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIEEE This paper proposes a robust fault detection and isolation (FDI) approach that combines active and passive robust FDI approaches. Standard active FDI approaches obtain robustness by using the unknown input observer (UIO) to decouple unknown inputs from residuals. Differently, standard passive FDI approaches achieve robustness by using the set theory to bound the effect of uncertain factors (disturbances and noises). In this paper, we combine the UIO-based and the set-based approaches to produce a mixed robust FDI, which can mitigate the disadvantages and exert the advantages of the two robust FDI approaches. In order to emphasize the role of set theory, the UIO design based on the set theory is named as the set-theoretic UIO (SUIO). A quadrotor subsystem is used to illustrate the effectiveness of the proposed FDI approach.Peer ReviewedPostprint (author's final draft

Robust state estimation and fault detection combining unknown input observer and set-membership approach

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper aims to propose a new robust state-estimation and fault-detection method by combining the unknown input observer (UIO) and the set-membership estimator (SME). It is known that both the SUIO and the SME can be used to estimate the states of a system. The former aims to obtain a particular value by actively decoupling the effect of unknown inputs, while the latter can obtain state-estimation sets by prediction and correction based on the set theory. Instead of particular state values, the latter can obtain state-estimation sets guaranteeing to contain system states (i.e., robust state estimation). In this paper, we propose to use the framework of the UIO to actively decouple part of unknown inputs and then further employ the set-membership estimation method to estimate state sets and detect faults. The objective of the proposed method is to reduce the conservatism of robust state-estimation sets by using the UIO to remove the contribution of part of unknown inputs to the sizes of state-estimation sets. At the end of this paper, a numerical example is used to illustrate the effectiveness and advantages of the proposed approach.Accepted versio

Economic Evaluation of Post-Combustion CO2 Capture Integration Technology in Natural Gas Combined Cycle Power Plant

[Introduction] In recent years, natural gas power generation has played an important role in the construction of clean energy system of China. By the end of the "14th Five-year Plan" in 2025, China's gas power installed capacity is expected to hit 150 million kilowatts. Carbon capture，utilization and storage (CCUS) is one of the key paths for gas power to achieve the carbon peaking and carbon neutrality goals. [Method] To this end, an integrated plant combining 600 MW natural gas combined cycle (NGCC) and CO2 post-combustion capture (PCC) were set up as the simulation object. [Result] The simulation study shows that the design captures all CO2 flue gas with 90% efficiency, the CO2 compression and purification rate is 99.5%, the total output of gas power generation decreases by about 16.05%, the auxiliary power ratio increases by 5.55%, and the demand for circulating cooling water increases by about 50.52%. [Conclusion] The economic analysis shows that the static investment cost of the integrated plant is 54.28% higher than that of the single power plant, and the levelized cost of energy (LCOE) increases by 15.96%, which brings great difficulties to the deployment and development of carbon dioxide capture. However, the natural gas price is still the most important factor affecting the operating cost of the power plant

Facile Fabrication of Sandwich Structural Membrane With a Hydrogel Nanofibrous Mat as Inner Layer for Wound Dressing Application

A common problem existing in wound dressing is to integrate the properties of against water erosion while maintaining a high water-uptake capacity. To tackle this issue, we imbedded one layer of hydrogel nanofibrous mat into two hydrophobic nanofibrous mats, thereafter, the sandwich structural membrane (SSM) was obtained. Particularly, SSM is composed of three individual nanofibrous layers which were fabricated through sequential electrospinning technology, including two polyurethane/antibacterial agent layers, and one middle gelatin/rutin layer. The obtained SSM is characterized in terms of morphology, component, mechanical, and functional performance. In addition to the satisfactory antibacterial activity against Staphylococcus aureus and Escherichia coli, and antioxidant property upon scavenging DPPH free radicals, the obtained SSM also shows a desirable thermally regulated water vapor transmission rate. More importantly, such SSM can be mechanically stable and keep its intact morphology without appearance damage while showing a high water-absorption ratio. Therefore, the prepared sandwich structural membrane with hydrogel nanofibrous mat as inner layer can be expected as a novel wound dressing

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Towards a Convex Design Framework for Online Active Fault Diagnosis of LPV Systems

International audienceThis paper focuses on the design of on-line optimal input sequence for robust active fault diagnosis (AFD) of discrete-time linear parameter varying (LPV) systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of off-line input sequence, the proposed approach focuses on on-line (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the conservatism of robust AFD has the potential to be further reduced. The criterion on the design of optimal input is characterized based on a non-convex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. Aside this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method

Optimal robust fault detection of discrete‐time LPV systems with measurement error‐affected scheduling variables combining ZKF and pQP

International audienceOptimal robust state estimation (SE) and fault detection (FD) methods of discrete-time linear parameter varying (LPV) systems with measurement error-affected scheduling variables are proposed under the boundedness assumption of system uncertainties. By using the weighted Frobenius norm of the generator matrix of SE zonotope to characterize the set size, the optimal observer gain can be computed by a Zonotopic Kalman Filter (ZKF) procedure for the purpose of observation. Meanwhile, by minimizing the influence of system uncertainties while maximizing that of faults on SE to enhance the sensitivity of FD, the optimal FD criterion is characterized based on an on-line fractional programming problem, which can be equivalently transformed into a parametric quadratic programming (pQP) problem. The pQP problem can be efficiently solved by searching the root of its nonlinear characteristic equation using secant method. In general, as long as sensors with sufficiently high precision are equipped to measure the scheduling variables, the bounds of measurement errors of scheduling variables can be less conservative than those direct bounds of scheduling variables, which can reduce the conservatism of FD or SE in this way. At the end of this paper, a case study based on a practical circuit model is used to illustrate the effectiveness of the proposed method. Copyrigh

Combining set-theoretic UIO and invariant sets for optimal guaranteed robust fault detection and isolation

This paper proposes an optimal guaranteed robust fault detection and isolation (FDI) method combing the set-theoretic unknown input observer (SUIO) and the robust positively invariant (RPI) sets for linear time-invariant (LTI) systems. The optimality of the proposed FDI method is achieved under a two-layer framework. The first layer allows to design a single optimal FDI-oriented SUIO with RPI set separation-based guaranteed FDI conditions to reduce the FDI conservatism of single observer. The second layer consists in designing an optimal observer configuration for a bank of optimal SUIOs to reduce the FDI conservatism of all the SUIOs together. Finally, the effectiveness of the proposed guaranteed robust FDI method is illustrated by using a four-tank systemPeer ReviewedPostprint (updated version

A unified description for polarization-transfer mechanisms in magnetic resonance in static solids: Cross polarization and DNP

International audiencePolarization transfers are crucial building blocks in magnetic resonance experiments, i.e., they can be used to polarize insensitive nuclei and correlate nuclear spins in multidimensional nuclear magnetic resonance (NMR) spectroscopy. The polarization can be transferred either across different nuclear spin species or from electron spins to the relatively low-polarized nuclear spins. The former route occurring in solid-state NMR can be performed via cross polarization (CP), while the latter route is known as dynamic nuclear polarization (DNP). Despite having different operating conditions, we opinionate that both mechanisms are theoretically similar processes in ideal conditions, i.e., the electron is merely another spin-1/2 particle with a much higher gyromagnetic ratio. Here, we show that the CP and DNP processes can be described using a unified theory based on average Hamiltonian theory combined with fictitious operators. The intuitive and unified approach has allowed new insights into the cross-effect DNP mechanism, leading to better design of DNP polarizing agents and extending the applications beyond just hyperpolarization. We explore the possibility of exploiting theoretically predicted DNP transients for electron–nucleus distance measurements—such as routine dipolar-recoupling experiments in solid-state NMR