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For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effective manner to make local monitoring model remember the features of previous modes. Choosing the principal component analysis (PCA) as a basic monitoring model, we try to resolve this problem. A modified PCA algorithm is built with continual learning ability for monitoring multimode processes, which adopts elastic weight consolidation (EWC) to overcome catastrophic forgetting of PCA for successive modes. It is called PCA–EWC, where the significant features of previous modes are preserved when a PCA model is established for the current mode. The optimal parameters are acquired by difference of convex functions. Moreover, the proposed PCA–EWC is extended to general multimode processes and the procedure is presented. The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm. Potential limitations and relevant solutions are pointed to understand the algorithm further. A numerical case study and a practical industrial system in China are employed to illustrate the effectiveness of the proposed algorithm. • A novel PCA–EWC framework is proposed for monitoring multimode processes. • It builds one model with continual learning ability, without retraining from scratch. • The model is updated continually based on the learned knowledge and current mode data. • It is not required that data from all modes are available in advance. [ABSTRACT FROM AUTHOR]

This paper studies the fault detection problem for a class of discrete-time switched linear systems under dwell-time (DT) constraints, using the parity space-based approach. The DT-dependent Lyapunov function is employed to investigate the asymptotic stability with less conservatism and to solve the constant ${l}_{2}$ -gain performance analysis problem, and its advantage is verified compared to the time-independent Lyapunov function approach. The corresponding switching residual generation is made to carry out the desired fault detection in the framework of parity space-based model. Then, by means of solving a generalized eigenvalue–eigenvector problem, the parity space matrices design is implemented. A quantitative relationship is established between the optimization performance and the choice of the parity space order. Two numerical examples are utilized to demonstrate effectiveness of the developed fault detection approach, including an application to switched RLC circuits. [ABSTRACT FROM AUTHOR]

Background: Cambodia is a Southeast Asian country and has one the highest rates of maternal and child mortality with inadequate use of maternal healthcare services in the region. The present study aimed to analyse the progress made in terms of using maternal healthcare services since 2000.Methods: Two rounds of Demographic and Health Surveys (DHS 2000 and DHS 2014) were used in the study. Sample population consisted 11,961 women aged between 15 and 49 years. The outcome measures were: Timing of first antenatal care (ANC) attendance, adequacy of ANC attendance, place of delivery and postnatal checkup. WHO guidelines were used to set the cut-off/define these measures. Data were analyzed in Stata version 14 using descriptive and multivariate regression analyses.Results: Findings indicated that the overall prevalence of making the first ANC visit in the first trimester was 64.19% [95%CI = 62.22,66.11], and that of having at least four ANC visits was 43.80% [95%CI = 41.89,45.73]. Prevalence of health facility delivery was 48.76% [46.62,50.90] and that of postnatal checkup was 71.14% [95%CI = 69.21,73.01]. Between 2000 and 2014, the percentage of timely and adequate use of ANC increased by respectively 61.8 and 65.3%, while that of health facility delivery and postnatal care increased by respectively 74.5 and 43.9%. Important demographic, socioeconomic and geographic disparities were observed in the utilization of ANC, health facility delivery and postnatal care services. Urban residency, having better educational status, white collar job, access to electronic media showed positive association, whereas higher parity (having > 2 children) and unwanted pregnancy showed negative association with the use of maternal healthcare services. Having at least four ANC visits was associated with significantly increased higher odds of using health facility delivery and postnatal care.Conclusion: There has a been a remarkable increase in the prevalence of women who are using the maternal healthcare services since 2000. The current findings provide important insights regarding the sociodemographic factors associated with the utilization of maternal health services in Cambodia that could contribute to evidence-based health policy making and designing intervention programs. [ABSTRACT FROM AUTHOR]

Multivariable stochastic degradation system (MSDS) is quite common in industries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large‐scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non‐Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi‐sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed‐form distribution of system‐level remaining useful life (RUL) is obtained with a mild two‐layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method. [ABSTRACT FROM AUTHOR]

• In this paper, we propose a weighted PMI based related component analysis (WPMI-RCA) method to search for the low-dimensional latent subspace of process variables that retains the maximal statistical associations with multiple quality variables. • Considering different correlations with different quality variables, a new weighted fusion index is constructed by combining PMI with the weighted fusion of Bayesian inference. Process variables with strong relations to quality can be selected to remove the redundancy and filter the noise on the original measurement space. • By transforming the axes in the measurement space, related components (RCs) with maximum correlation to quality are obtained by related component analysis (RCA) with the supervision of quality. Meaningful information hidden between process and quality data can be effectively captured. And the statistics in two orthogonal subspaces are defined for the quality-related process monitoring. • The simulation results of the numerical example and the benchmark TEP show the superior monitoring performance of the presented method. The detection rates for quality-related faults can be improved, the nuisance detections of quality-unrelated anomalies can be reduced at the same time. Industrial products have become the core of today's highly competitive international society, but quality-related faults happened in practical industrial processes heavily affect product quality. In this paper, we will consider the problem of the detection of quality-related faults. Inspired by part mutual information (PMI), we develop a process monitoring method called weighted PMI based related component analysis (WPMI-RCA). Firstly, combining PMI and Bayesian weighted fusion, process variables strongly related to quality are selected with the supervision of multi-quality indicators. Then, the selected variables are modeled by related component analysis (RCA) and thus orthogonal related components (RCs) containing the main information of quality variations can be obtained. The process data space can be divided into two subspaces and the monitoring statistics are developed for the quality-related fault detection. Finally, the validity of WPMI-RCA is demonstrated by a numerical example and the benchmark Tennessee Eastman process (TEP). The proposed method can improve the detection rates of quality-related faults and significantly reduce the nuisance detections. It may be helpful to improve the management efficiency for practical industrial processes. [ABSTRACT FROM AUTHOR]

In the past decades, with the rapid development of modern technologies, a special kind of fault, the intermittent fault (IF) has become a serious threat to system securities. Although fruitful research results have been obtained for fault diagnosis, most of them are concerned with permanent faults. Furthermore, up to now, there has been no overview on the diagnosis techniques for IFs in dynamic systems. In fact, IFs are common in a variety of fields, whereas their research is far from mature and many open issues still remain to be solved. Therefore, in this overview, our main objective is to attract direct attentions from more researchers and engineers into the fascinating area of IF diagnosis. In this paper, the concept, research background, challenges and research motivations of IFs are introduced. Then, current research into diagnostics of IFs is reviewed, along with discussions on their strengths, limitations, and future directions. [ABSTRACT FROM AUTHOR]

For some practical industrial systems or components, such as blast furnaces and Li-ion batteries, there are two important factors to model the degradation processes. One is the long-range dependence, which can reflect the non-Markovian nature of the degradation processes. The other factor is the existence of multiple modes, because the operating conditions and external environments inevitably change during the whole lifetime of these systems. In this paper, we first propose a fractional Brownian motion (FBM) based degradation model with long-range dependence and multiple modes, and then consider the prediction of remaining useful life. To identify the multiple modes in the degradation process, we propose a two-step method, including change-points detection and linear segments clustering. In each degradation mode, the degradation rate is assumed to be normally distributed. The means and variances of these distributions can be obtained by the maximum likelihood estimation. To describe the switching between different modes, the continuous-time Markov chain is applied, and its transition rate matrix can be estimated by the historical switching time. An approximation of the first passage time with a predefined threshold can be obtained by a weak convergence theorem and a time-space transformation. A numerical simulation and a practical case of a blast furnace wall are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A subspace identification scheme based on PCA incorporating prior information is proposed. • The prior information is incorporated through the constrained least squares • The proposed scheme improved the accuracy and stability of the identified models. Subspace identification is a very useful tool for estimating a state-space model for a dynamic system. However, most of the subspace identification methods (SIMs) can only provide consistent estimations when the quality of the data is good. This problem can be solved by integrating prior information about a system into an identification procedure. In this paper, we propose a new approach for closed-loop SIMs based on principal component analysis (PCA) utilizing prior information. After performing the PCA procedure, we use the constrained least squares (CLS) approach with an equality constraint to incorporate prior information into the impulse response. The simulation results reveal that the proposed methods are more accurate and stable in model identification. [ABSTRACT FROM AUTHOR]

• A new multimode process monitoring method CIBL-RTCSA is proposed. • CIBL is proposed for mode identification, with higher accuracy than traditional classification methods. • A multiple RTCSA model with a window-switching scheme is developed for diagnosing the incipient sensor fault in multimode processes. • A numerical example and simulation on a CSTH both demonstrate the effectiveness of the proposed method. This paper considers the problem of detecting and isolating incipient sensor fault in multimode processes. A data-driven multimode process monitoring method called conditionally independent Bayesian learning based recursive transformed component statistical analysis (CIBL-RTCSA) is presented. Considering the strong assumption of conditional independence in naïve Bayes, orthogonal transformation is applied to measured variables to improve the extent of conditional independence in different operating modes. The Bayes-based mode identification is adopted for transformed data, and a multiple RTCSA model with a window-switching scheme is developed for monitoring multimode processes. With the orthogonal transformation, the accuracy of mode identification can be effectively improved compared with naïve Bayes. In addition, the fault detection and isolation performance of the proposed method outperforms traditional monitoring methods. The effectiveness of the proposed method is demonstrated by a numerical example and the simulation on a continuous stirred tank heater process. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method. Highlights • New time-constrained sparse representation method is put forward. • Novel robust characteristic dimensionality reduction method is proposed. • Online fault detection algorithm is developed and fault detectability is analyzed. • Experimental studies on TEP and EMU braking system validate the proposed methods. [ABSTRACT FROM AUTHOR]

For data-driven remaining useful life (RUL) prediction, an appropriate degradation model is critically important to achieve accurate prediction. The degradation processes in some practical systems are not only related to the age but also related to the current degradation state, and the degradation processes may be non-Markovian processes. However, most existing stochastic process-based degradation models only depend on the age, and simply assume that the increments are independent. In this paper, an age- and state-dependent degradation model with long-range dependence is developed, which is more general than most of the existing models based on either Brownian motions (BMs) or fractional Brownian motions (FBMs). The Radon-Nikodym derivative is utilized to obtain a likelihood ratio function of unknown parameters, and the estimates are obtained by maximizing the likelihood ratio function. A weak convergence theorem is introduced to approximate the FBM by a BM with a time-varying coefficient. A time-space transformation is further utilized to obtain an approximate explicit solution of the RUL. At last, numerical simulations and two real case studies of blast furnace walls and ball bearings are adopted to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

For quality-related process monitoring, the current approaches, such as partial least square (PLS) and its variants, often misjudge quality-unrelated anomalies as quality-related anomalies, which brings confusion to quality control personnel. Hence it is important to achieve high detection rates while ensuring the quality-related/-unrelated anomalies can be exactly detected and distinguished. In this article, dynamic related component analysis (DRCA) in full space is proposed to exactly distinguish quality-related/-unrelated anomalies while decreasing the rate of misjudging quality-unrelated anomalies as quality-related anomalies in dynamic processes. The augmented sample covariance matrices with the time lag shift technique are used to conduct eigenvalue decomposition and data transformation. Dynamic related components (DRCs) containing quality-related dynamic information and dynamic unrelated components (DUCs) containing quality-unrelated dynamic information are obtained, and their statistics are used for process monitoring. Both orthogonal quality-related subspaces and orthogonal quality-unrelated subspaces are modeled. Case studies on Tennessee Eastman Process (TEP) and a practical thermal power plant demonstrate the anomaly detection performance of DRCA and its ability to determine whether anomalies affect quality correctly. • The proposed method can effectively improve the performance of anomaly detection. • Compared with other methods, DRCA is more sensitive to dynamic changes of processes. • DRCA can accurately judge whether an abnormality affects quality. • Simulations indicate superior performance of DRCA, compared with comparative methods. [ABSTRACT FROM AUTHOR]

Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al., are strongly dependent on continuous variables because most of them inevitably involve Euclidean or Mahalanobis distance. With industrial processes becoming more and more complex and integrated, binary variables also appear in monitoring variables besides continuous variables, which makes process monitoring more challenging. The aforementioned traditional approaches are incompetent to mine the information of binary variables, so that the useful information contained in them is usually discarded during the data preprocessing. To solve the problem, this paper focuses on the issue of hybrid variable monitoring (HVM) and proposes a novel unsupervised framework of process monitoring with hybrid variables including continuous and binary variables. HVM is addressed in the probabilistic framework, which can effectively exploit the process information implicit in both continuous and binary variables at the same time. In HVM, the statistics and the monitoring strategy suitable for hybrid variables with only healthy state data are defined and the physical explanation behind the framework is elaborated. In addition, the estimation of parameters required in HVM is derived in detail and the detectable condition of the proposed method is analyzed. Finally, the superiority of HVM is fully demonstrated first on a numerical simulation and then on an actual case of a thermal power plant. [ABSTRACT FROM AUTHOR]

In diesem Beitrag werden Verfahren zur Berechnungen von Trägern mit starrem oder elastischem Verbund sowie ohne Verbundwirkung behandelt. Die Anwendung von geschlossenen Lösungen für die exakte Berechnung des elastischen Verbundes ist unentbehrlich. Einige geschlossene Lösungen zur Bestimmung der Normalkraft und der Durchbiegung werden angegeben. Anwendungsmöglichkeiten des hier eingeführten Verbundfaktors und die dahinter stehenden Federmodelle werden gezeigt. Schließlich wird eine Näherungsmethode in Verwendung eines erweiterten Federmodells und eines Verbundfaktors entwickelt. Die Ergebnisse, die nach beiden Rechenmethoden in Beispielen berechnet wurden, zeigen eine gute Übereinstimmung. Contribution to the calculation of composite beams with elastic composite action. Methods of Calculations according to the exact equations for rigid and elastic composite action as well as without composite action are discussed. The application of closed solutions is essential for the calculation of the elastic composite action. Some closed solutions for the normal force and the deflection are given. Applications of the introduced composite factor and the spring models behind it are shown. Finally, an approximation method is developed from an extended spring model and the composite factor. The results of model calculations, which were carried out by both methods, show a good agreement. [ABSTRACT FROM AUTHOR]

Reliability estimation based on degradation model is a feasible and low-cost alternative used to estimate reliability for highly reliable systems when the failure-time data are rare. Based on reliability estimation by degradation modeling, preventive maintenance work orders need to be timely triggered to minimize unscheduled downtime. In Trans. Autom. Sci. Eng., vol. 9, no. 1, pp. 209–212, Jan. 2012, Sun et al., an approach to dynamically extract maintenance threshold is presented for maintenance scheduling, in which the reliability threshold for maintenance is determined by maximizing the expected availability and the reliability estimation is achieved by a modified two-stage degradation modeling approach. Although this approach is novel and useful, its reliability estimation is an asymptotic solution in long time scale. In this paper, we generalize the above result by considering a general degradation path model and provide the exact and explicit formulation for reliability estimation. Additionally, a maximum-likelihood estimation method for parameters in the presented model is proposed based on the historical degradation observations. Finally, an example is provided for illustration. [ABSTRACT FROM AUTHOR]

About 120,000-200,000 Chinese laborers worked for Allied troops during WWI. 80% of them were illiterate and they encountered many language barriers and difficulties. Some members of the North American YMCA came to Britain and France to help them, especially by teaching them to read. James Yen and his colleagues initiated a new movement to teach the laborers. They compiled a new basic Chinese characters book and a new paper called Chinese Laborers Weekly, both of which were very useful in evangelizing and civilizing the laborers in order to positively affect the construction of a new nation at home. This also blazed a new trail for removing illiteracy in modern China. [ABSTRACT FROM AUTHOR]

Highlights: [•] Noise reduction for CRAMPS by utilizing narrowband filters. [•] Exploiting analog filter delay to sample data in the next detection window. [•] Enabling CRAMPS proton experiments on spectrometers without wideline hardware. [Copyright &y& Elsevier]

This paper presents a novel prespecified finite-time and overshoot-restrained fault-tolerant tracking control scheme to compensate the intermittent faults in linear uncertain systems. Starting from the system tracking error dynamics model, we design a speed excitation function to improve the response speed of the control. A finite-time performance index function is established in the zero-sum game framework for the fault-free H ∞ optimal control policy. Based on Lyapunov stability theory, a fault compensation oriented fault estimation and diagnosis method is proposed to ensure the uniformly ultimately bounded stability of tracking errors. It is noteworthy that the proposed speeding fault-tolerant control can accelerate the trajectory tracking, narrow the upper bound of tracking errors and restrain the overshoot. Numerical and practical experiments fully illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract: Mixed surfactant solution, containing cetyltrimethyl ammonium bromide (CTAB) and sodium dodecylbenzyl sulfonate (SDBS), is used to prepare polyaniline (PANI) with soft 1D structure and high electrical conductivity. The mixed surfactants in the reaction play double roles of soft templates and dispersion reagents. The influence of CTAB–SDBS ratio on the morphology and conductive property of PANI is investigated. Through varying the proportion of surfactants, uniformly branched nanofibers with higher aspect ratio and good dispersion are obtained, which possess the highest conductivity (0.102S·cm−1). Moreover, FT-IR spectra are measured to explain the change of structure and conductivity under assistance of mixed surfactants. As a result, the mixed surfactants have significant effect on the electron density of whole structure as well as the PANI molecular orientation. [Copyright &y& Elsevier]

Abstract: A new tracking filtering algorithm for a class of multivariate dynamic stochastic systems is presented. The system is expressed by a set of time-varying discrete systems with non-Gaussian stochastic input and nonlinear output. A new concept, such as hybrid characteristic function, is introduced to describe the stochastic nature of the dynamic conditional estimation errors, where the key idea is to ensure the distribution of the conditional estimation error to follow a target distribution. For this purpose, the relationships between the hybrid characteristic functions of the multivariate stochastic input and the outputs, and the properties of the hybrid characteristic function, are established. A new performance index of the tracking filter is then constructed based on the form of the hybrid characteristic function of the conditional estimation error. An analytical solution, which guarantees the filter gain matrix to be an optimal one, is then obtained. A simulation case study is included to show the effectiveness of the proposed algorithm, and encouraging results have been obtained. [Copyright &y& Elsevier]

Abstract: Arrearage phenomenon commonly exists in actuators. With arrearages, the actuator cannot achieve the target suggested by the controller, so the control performance degrades. Deadzones and stuck faults can be considered to be two special classes of arrearages, which are studied widely; however, more general arrearage phenomenon cannot be described and treated by these forms. In this paper, a general formulation is introduced for this phenomenon, which is referred as actuator arrearage faults. Based on this formulation, an improved generalized predictive control scheme is proposed for a class of single-input single-output linear systems. With arrearage faults, the system becomes a stochastic process. Hence, a sufficient condition, which guarantees the output tracking error to be uniformly ultimately bounded in the mean-square sense, is presented in this paper. Simulations are given to illustrate the feasibility and effectiveness of the proposed scheme. [Copyright &y& Elsevier]

Abstract: Multi-phase batch process is common in industry, such as injection molding process, fermentation and sequencing batch reactor; however, it is still an open problem to control and analyze this kind of processes. Motivated by injection molding processes, the multi-phase batch process in each cycle is formulated as a switched system with internally forced switching instant. Controlling multi-phase batch processes can be decomposed into two subtasks: detecting the dynamics-switching-time; designing the control law for each phase with considering switching effect. In this paper, it is assumed that the dynamics-switching-time can be obtained in real-time and only the second subtask is studied. To exploit the repetitive nature of batch processes, iterative learning control scheme is used in batch direction. To deal with constraints, updating law is designed by using model predictive control scheme. An online iterative learning model predictive control (ILMPC) law is first proposed with a quadratic programming problem to be solved online. To reduce computation burden, an offline ILMPC is also proposed and compared. Applications on injection molding processes show that the proposed algorithms can control multi-phase batch processes well. [Copyright &y& Elsevier]

Abstract: High-sensitivity proton detected experiments in solid-state NMR have been recently demonstrated in proton diluted proteins as well as fully protonated samples under fast magic-angle spinning. One key element for performing successful proton detection is effective solvent suppression achieved by pulsed field gradients (PFG) and/or saturation pulses. Here we report a high-performance solvent suppression method that attenuates multiple solvent signals simultaneously by more than a factor of 10,000, achieved by an optimized combination of homospoil gradients and supercycled saturation pulses. This method, which we call Multiple Intense Solvent Suppression Intended for Sensitive Spectroscopic Investigation of Protonated Proteins, Instantly (MISSISSIPPI), can be applied without a PFG probe. It opens up new opportunities for two-dimensional heteronuclear correlation spectroscopy of hydrated proteins at natural abundance as well as high-sensitivity and multi-dimensional experimental investigation of protein–solvent interactions. [Copyright &y& Elsevier]

Abstract: An iterative learning reliable control (ILRC) scheme is developed in this paper for batch processes with unknown disturbances and sensor faults. The batch process is transformed into and treated as a two-dimensional Fornasini–Marchesini (2D-FM) model. Under the proposed control law, the closed-loop system with unknown disturbances and sensor faults not only converges along both the time and the cycle directions, but also satisfies certain performance. For performance comparison, a traditional reliable control (TRC) law based on dynamic output feedback is also developed by considering the batch process in each cycle as a continuous process. Conditions for the existence of ILRC scheme are given as biaffine and linear matrix inequalities. Algorithms are given to solve these matrix inequalities and to optimize performance indices. Applications to injection packing pressure control show that the proposed scheme can achieve the design objectives well, with performance improvement along both time and cycle directions, and also has good robustness to uncertain initialization and measurement disturbances. [Copyright &y& Elsevier]

Remarkable progress in solid-state NMR has enabled complete structure determination of uniformly labeled proteins in the size range of 5–10 kDa. Expanding these applications to larger or mass-limited systems requires further improvements in spectral sensitivity, for which inverse detection of 13C and 15N signals with ¹H is one promising approach. Proton detection has previously been demonstrated to offer sensitivity benefits in the limit of sparse protonation or with ∼30 kHz magic-angle spinning (MAS). Here we focus on experimental schemes for proteins with ∼100% protonation. Full protonation simplifies sample preparation and permits more complete chemical shift information to be obtained from a single sample. We demonstrate experimental schemes using the fully protonated, uniformly 13C,15N-labeled protein GB1 at 40 kHz MAS rate with 1.6-mm rotors. At 500 MHz proton frequency, 1-ppm proton line widths were observed (500 ± 150 Hz), and the sensitivity was enhanced by 3 and 4 times, respectively, versus direct 130 and 15N detection. The enhanced sensitivity enabled a family of 3D experiments for spectral assignment to be performed in a time-efficient manner with less than a micromole of protein. CANH, CONH, and NOAH 3D spectra provided sufficient resolution and sensitivity to make full backbone and partial side-chain proton assignments. At 750 MHz proton frequency and 40 kHz MAS rate, proton line widths improve further in an absolute sense (360 ± 115 Hz). Sensitivity and resolution increase in a better than linear manner with increasing magnetic field, resulting in 14 times greater sensitivity for 1H detection relative to that of 15N detection. [ABSTRACT FROM AUTHOR]

Abstract: This article concerns with the regulation and fault-tolerant control of non-minimum phase nonlinear processes with mismatched uncertainties. A variable structure controller, switching between a first-order sliding mode control and a second-order sliding mode control, is proposed to regulate the output and to stabilize the unstable zero dynamics with mismatched uncertainties. Once detected, the fault is estimated on-line by an approximator of radial basis function network; the control law is reconfigured to compensate the fault with closed-loop system asymptotically stable. The application of the proposed algorithm to a non-minimum phase continuously stirred tank reactor (CSTR) is illustrated in the presence of matched and mismatched uncertainties and component fault. [Copyright &y& Elsevier]

Abstract: By use of the idea of fault detection and identification, this Letter proposes a new scheme to resolve the problem of chaotic private communication. From the point of view of fault detection and identification the scalar message signal hidden in the chaotic systems can be regarded as the component fault signal, thereby it can be detected and recovered using the model-based methods of fault detection and identification. The famous Duffing oscillator is used to illustrate and verify the effectiveness of this scheme. [Copyright &y& Elsevier]

Abstract: In this Letter, the problem of the fault-tolerant control (FTC) of chaotic systems is investigated. A time-delayed integrity controller is proposed to ensure the stabilization of unstable periodic orbits (UPOs) in the case of sensor failures. If some parameters are chosen suitably, this controller can effectively stabilize UPOs for possible sensor failures. The famous Lorenz system is illustrated to show the effectiveness of this controller. [Copyright &y& Elsevier]

Abstract: We present a systematic study of proton linewidths in rigid solids as a function of sample spinning frequency and proton density, with the latter controlled by the ratio of protonated and perdeuterated model compounds. We find that the linewidth correlates more closely with the overall proton density (ρ H) than the size of local clusters of 1H spins. At relatively high magic-angle spinning (MAS) rates, the linewidth depends linearly upon the inverse MAS rate. In the limit of infinite spinning rate and/or zero proton concentration, the linewidth extrapolates to a non-zero value, owing to contributions from scalar couplings, chemical shift dispersion, and B 0 field inhomogeneity. The slope of this line depends on the overall concentration of unexchangeable protons in the sample and the spinning rate. At up to 30% protonation levels (∼2 1H/100Å3), proton detection experiments are demonstrated to have a substantial (2- to 3-fold) sensitivity gain over corresponding 13C-detected experiments. Within this range, the absolute sensitivity increases with protonation level; the optimal compromise between sensitivity and resolution is in the range of 20–30% protonation. We illustrate the use of dilute protons for polarization transfer to and from low-γ spins within 5Å, and to be utilized as both magnetization source and detection spins. The intermediate protonation regime enhances relaxation properties, which we expect will enable new types of 1H correlation pulse sequences to be implemented with improved resolution and sensitivity. [Copyright &y& Elsevier]

Abstract: Combining the repetitive learning strategy and the optimality principle, this Letter proposes a time-delayed method to control chaotic maps. This method can effectively stabilize unstable periodic orbits within chaotic attractors in the sense of least mean square. Numerical simulations of some chaotic maps verify the effectiveness of this method. [Copyright &y& Elsevier]

Magic-angle spinning solid-state NMR (SSNMR) studies of the β1 immunoglobulin binding domain of protein G (GB1) are presented. Chemical shift correlation spectra at 11.7 T (500 MHz ¹H frequency) were employed to identify signals specific to each amino acid residue type and to establish backbone connectivities. High sensitivity and resolution facilitated the detection and assignment of every 15N and 13C site, including the N-terminal (M1) 15NH3, the C-terminal (E56) 13C', and side-chain resonances from residues exhibiting fast-limit conformational exchange near room temperature. The assigned spectra lend novel insight into the structure and dynamics of microcrystalline GB1. Secondary isotropic chemical shifts report on conformation, enabling a detailed comparison of the microcrystalline state with the conformation of single crystals and the protein in solution; the consistency of backbone conformation in these three preparations is the best among proteins studied so far. Signal intensities and line widths vary as a function of amino acid position and temperature. High-resolution spectra are observed near room temperature (280 K) and at <180 K, whereas resolution and sensitivity greatly degrade substantially near 210 K; the magnitude of this effect is greatest among the side chains of residues at the intermolecular interface of the microcrystal lattice, which we attribute to intermediate-rate translational diffusion of solvent molecules near the glass transition. These features of GB1 will enable its use as an excellent model protein not only for SSNMR methods development but also for fundamental studies of protein thermodynamics in the solid state. [ABSTRACT FROM AUTHOR]

Abstract: This Letter deals with the synchronization of a class of uncertain chaotic systems in the drive-response framework. A robust adaptive observer based response system is designed to synchronize a given chaotic system with unknown parameters and external disturbances. Lyapunov stability ensures the global synchronization between the drive and response systems even if Lipschitz constants on function matrices and bounds on uncertainties are unknown. Numerical simulation of Genesio–Tesi system verifies the effectiveness of this scheme. [Copyright &y& Elsevier]

Abstract: In recent years, robust fault diagnosis of nonlinear systems has received much more attention due to the universal existence of nonlinearities and model uncertainties in practice. By introducing a new adaptive law and sliding mode observers with boundary layer control into Polycarpou''s online approximator, we propose a fast and robust fault diagnosis strategy for a class of nonlinear systems in this article. The robustness and stability are proved theoretically by the Lyapunov method and the detectability conditions as well as the upper bound of detection time are given, which demonstrate that the detection time of our strategy is much shorter than that of Polycarpou''s approach. Simulation results on the three-tank system “DTS200” show the effectiveness and fastness of the proposed strategy. [Copyright &y& Elsevier]

Solid solutions of (1−x)Pb(Mg1/3Nb2/3)O3xPb(Sc1/2Nb1/2)O3 (PMN/PSN) have been investigated using high-resolution 93Nb 3-quantum magic-angle spinning nuclear magnetic resonance experiments (3QMAS NMR). In previous MAS NMR investigations, the local B-cation ordering in these relaxor ferroelectric solid solutions was quantitatively determined. However, in conventional one-dimensional MAS spectra the effects of chemical shifts and quadrupole interaction are convoluted; this, in addition to the insufficient resolution, precludes reliable extraction of the values of isotropic chemical shift and quadrupole coupling product. In the current 3QMAS investigation, 93Nb spectra are presented for concentrations x=0, 0.1, 0.2, 0.6, 0.7, and 0.9 at high magnetic field (19.6 T) and fast sample spinning speed (35.7 kHz). Seven narrow peaks and two broad components are observed. The unique high-resolution of the two-dimensional 3QMAS spectra enables unambiguous and consistent assignments of spectral intensities to the specific 28 nearest B-site neighbor (nBn) configurations, (NMg,NSc,NNb) where each number ranges from 0 to 6 and their sum is 6. It is now possible to isolate the isotropic chemical shift and quadrupole coupling product and separately determine their values for most of the 28 nBn configurations. The isotropic chemical shift depends linearly on the number of Mg2+ cations in the configuration; δisoCS=(13.7±0.1)NMg−970±0.4 ppm, regardless of the ratio NSc/NNb. For the seven Nb5+-deficient configurations (NMg, 6-NMg, 0) and the pure niobium configuration (0, 0, 6), the quadrupole coupling products (and hence the electric field gradients) are small (PQ∼6–12 MHz) and for the remaining configurations containing small, ferroelectric active Nb5+ ions, the quadrupole coupling products are significantly larger (PQ∼40 MHz), indicating larger electric field gradients. [Copyright &y& Elsevier]

Robust fault tolerant control for a class of time-delay linear systems with parameter uncertainties is studied, and a time delay related state feedback control is proposed. On the basis of Lyapunov method, we prove that the proposed control law has integrity against sensor and/or actuator failures if the correspondent sufficient condition can be satisfied. A heuristic algorithm is also provided to facilitate the realization of the fault tolerant control. Finally, a simulation example is presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, a novel active fault‐tolerant control (AFTC) design method is developed by using the switching linear parameter varying (LPV) controller with inexact fault‐effect parameters. The LPV controller is not only considered as a gain‐scheduled controller varying with the operating points, but also an AFTC controller varying with the fault‐effect parameters, which can be obtained via the fault estimator in real‐time. The inexact measurement of fault‐effect parameters is considered for the first time in this article. To cover large parameters variation, a class of switched LPV fault‐tolerant controller is designed to work in the multiple partitioned parameters subregions. The dynamic output feedback controller model is constructed to perform the switched LPV fault‐tolerant control task under the mode‐dependent average dwell time constraint. By using the multiple parameter‐dependent Lyapunov function approach, sufficient conditions with less conservatism are obtained, such that the corresponding closed‐loop systems are globally uniformly exponentially stable and satisfy an upper bound of weighted l2‐gain performance indexes. Finally, effectiveness and applicability of the developed approaches are validated via an active magnetic bearing system. [ABSTRACT FROM AUTHOR]

The current data-driven anomaly detection approaches for practical industrial processes are strongly based on continues variables, represented by PCA, PLS and their variants. However, there exist a large amount of two-valued variables in large-scale processes, whose values are stored as 0 or 1. For example, the No.1 generator set of Zhoushan power plant, China, has 17381 variables, in which 8822 are two-valued variables. Therefore, an interesting problem naturally arises: how to combine these abundant two-valued information to enhance the performance of anomaly detection? This paper considers this problem and develops a novel mixed hidden naive Bayesian model (MHNBM) for anomaly detection, which can make the best usage of continuous and two-valued variables simultaneously. This kind of model is different from the traditional detection methods, in which the two-valued variables are totally eliminated in the data preprocessing. MHNBM is developed within the probability framework, and can efficiently enhance the detection performance through combining the two-valued variables. The effectiveness of the proposed MHNBM is validated through the simulation data and the actual data of Zhoushan power plant, China. • A novel data-driven process monitoring model called mixed hidden native Bayesian model (MHNBM) is proposed for anomaly detection. • In the proposed model, both continuous and two-valued variables are utilized for detection. • The variable selection is performed for both continuous and two-valued variables. • Through the simulation data and the actual data of a power plant, the effectiveness of the proposed MHNBM is validated. [ABSTRACT FROM AUTHOR]

Reliability of the high-speed train air brake system is very critical to ensure a safe and comfortable operation environment for passengers. In our recently published work, a new monitoring strategy based on the concept of inter-variable variance (IVV) was proposed. It was illustrated by theoretical analysis and extensive experiments that this strategy is effective for several kinds of faults, and superior to the KNORR logic which is adopted by most high-speed trains in practice. However, the aforementioned strategy based on conventional IVV still suffers from two drawbacks. More specifically, due to the specific property of IVV, this strategy is unable to detect certain faults even if the fault is very serious; in addition, the detectability of conventional IVV for incipient faults with relatively small magnitudes still needs to improve. In this paper, a fault detection method incorporating the idea of four stages partition and involving a new combined statistic is presented to perform fault detection for high-speed train air brake systems. Through theoretical reasoning, it is pointed out that the proposed method can overcome drawbacks of the conventional IVV method to a large extent. To demonstrate the effectiveness and merits of the proposed fault detection method, in comparison with existing methods, experimental studies are carried out on a practical high-speed train air brake test bench. • A novel fault detection index is proposed to monitor the air brake system. • The index can detect some faults that cannot be detected by existing methods. • The index is more sensitive to several kinds of incipient faults. • Practical experiments are carried out to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

• A novel algorithm based on KLD is proposed to select the most informative variables in multimode processes. • The one-step Viterbi algorithm is developed for real-time mode identification with high accuracy. • A new detection index MWNLLP is developed to capture the dependency of samples and further improve the detection performance. • Simulations on a numerical example and TEP both demonstrate the effectiveness of the proposed method. • Specifically, MWNLLP can effectively detect the fault 3 in TEP with a detection rate over 95%. Multimode process monitoring has attracted much attention in academia and industry in past decades. Generally, all the measured variables are involved to monitor a process. However, irrelevant variables may degrade the monitoring performance due to over-fitting, and increase the online computational complexity excessively. In order to monitor the faults possibly affecting the operational safety and product quality, it is important to select appropriate monitored variables that are closely related to these faults. This paper explores the problem of multimode process monitoring with variable selection. A novel algorithm based on the Kullback-Leibler divergence is proposed for variable selection in multimode processes, which effectively selects the most informative variables about the concerned faults. Then, the one-step Viterbi algorithm with low computational complexity is developed to implement online mode identification, which utilizes both spatial characteristics and temporal correlations to identify operating modes accurately. By introducing the moving window technique, a new detection index, i.e. moving window-negative log likelihood probability (MWNLLP), is proposed to capture the dependency of samples and further improve the detection performance for the concerned faults. A numerical example and the Tennessee Eastman process (TEP) are adopted to demonstrate the effectiveness of the proposed method. Specifically, MWNLLP can effectively detect the fault 3 in TEP with a detection rate over 95%. [ABSTRACT FROM AUTHOR]

This paper presents a new type of composite slim floor beam, determined by combining the results of an experimental study and theoretical analysis of the ultimate flexural strength of slim floor beams. The shear connectors play a significant role in the mechanical properties of this type of composite slim floor beam, because the precast concrete slab is laid on the bottom flange of the steel section and because the upper portion of the steel beam is encased in the cast-in-place concrete slab. To investigate the ultimate flexural strength, three specimens, which included headed studs, transverse steel bar shear connectors and no shear connectors, were tested. Additionally, a detailed numerical analysis was performed to verify the experimental results, which indicated that a higher-strength steel beam and thicker concrete slab can effectively enhance the stiffness and flexural capacity of the composite slim floor beam. Based on plastic mechanics and limit analysis theory, a calculation method was derived to estimate the ultimate flexural strength of a composite slim floor beam, and a comparison between the calculation and experimental results shows that the theoretical results exhibit good agreement with the experimental results, and the proposed analysis method can be used in future studies to gain a better understanding of the ultimate flexural strength of composite slim floor beams. [ABSTRACT FROM AUTHOR]

Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the problem of fault estimation for a class of continuous‐time switched affine systems with actuator faults. In most of the existing results of switched affine systems based on the min‐switching approach, the candidate Lyapunov function is usually common, such that it is conservative to derive the practical stability conditions. To overcome this restriction, a class of switched Lyapunov function is introduced to increase more degrees of freedom of the designed min‐switching strategy and to ensure the stability of error systems. At the same time, the proposed dwell‐time dependent switching strategy can avoid the frequent switchings commonly existing in the min‐switching mechanism. Then, a new intermediate estimator is designed to estimate the state and fault simultaneously, and the intermediate estimator based fault estimation is performed under the proposed mixed switching law. Moreover, the gains of fault estimator are explicitly calculated via resolving the conditions of linear matrix inequalities. Finally, two examples including a flyback DC‐DC power converter are illustrated to verify the effectiveness and advantage of proposed fault estimation approach. [ABSTRACT FROM AUTHOR]

In this paper, the robust distributed filtering problem is investigated for a class of wireless sensor networks with stochastic parameter and topology uncertainties. The local filters collect information not only from itself but also from its neighbouring sensor nodes according to the network topology. A novel robust distributed filter is proposed which takes stochastic parameter and topology uncertainties into full consideration but does not depend on any specific structure of uncertainties. By proper designing of the filter gain, the trace of estimation error covariance is minimised at each time step, where the new stochastic analysis and augmentation transformation techniques are developed to handle the challenges resulting from multiplicative stochastic uncertainties and sparse topology. The corresponding recursive robust distributed filtering algorithm is also presented for real-time online applications. Finally, a simulation study is carried out to illustrate the effectiveness and applicability of our proposed method. [ABSTRACT FROM AUTHOR]