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Objective/Context: This article surveys the evolution of commercial finance in Ming-Qing China and responds to the debates about the role of finance in the “Great Divergence” between China and Europe. Methodology: Based on new historical materials, especially commercial documents, we study the evolution of financial organizations at different levels as delineated by Fernand Braudel. We also analyze the long-run trends of commercial interest rates in the Ming-Qing eras based on a novel historical database of interest rates in China. Originality: This article explores capital market developments since the 16th century and their relationship with the state —a topic that has been almost entirely neglected in the existing literature. We also find improvements in market integration in China before the late 19th century when Western financial institutions began to enter China, and the prime commercial interest rate in the capital market was much lower than the dominant accounts in the “Great Divergence” debate. Conclusions: While current comparative studies either emphasize the financial stagnation of Ming-Qing China relative to the West or emphasize its special development through non-market mechanisms, e.g., clans, this article shows that the capital market played a similar key role in the evolution of finance in Ming-Qing China as it did in advanced parts of Europe. Thus, we suggest that the difference in financial development between China and the West lies in the organizational structure of the financial sector and the relationship between finance and the state

Abstract Stimulated by the enhancing requirements for system safety as well as process reliability in complex industrial processes, this paper investigates a data‐driven fault detection and fault‐tolerant control scheme for large‐scale systems. To this end, a pair‐wise decomposition approach is first presented based on the inclusion principle, which expands the original large‐scale system into a set of disjointed pair‐wise subsystems. Then, for the pair‐wise subsystems, the observer‐based residual generators and observer‐based state feedback controllers for fault detection and fault‐tolerant control purpose are further developed in a decentralized way. On this basis, the fault‐tolerant controller for the original large‐scale system is obtained by the coordination and contraction of the decentralized observer‐based state feedback controllers. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through the case study on a multi‐area interconnected power system.

Abstract A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

Abstract Distributed Nash equilibrium seeking of aggregative games is investigated and a continuous-time algorithm is proposed. The algorithm is designed by virtue of projected gradient play dynamics and aggregation tracking dynamics, and is applicable to games with constrained strategy sets and weight-balanced communication graphs. The key feature of our method is that the proposed projected dynamics achieves exponential convergence, whereas such convergence results are only obtained for non-projected dynamics in existing works on distributed optimization and equilibrium seeking. Numerical examples illustrate the effectiveness of our methods.

As the ageing population increases, it is essential to manage human resources with residual life prediction, especially for the ageing population. Thus, this study aims to predict residual life using the available information of health statuses, such as the performances in activities of daily living (ADL) and memory. In this study, the principal components of ADL and memory information are extracted for prediction. The relationship between the principal components and residual life is established based on the concept of the proportional residual, which states that the residual life may be proportional to the changes in ADL and memory performance. A recursive model for residual life prediction is formulated and fitted for the Chinese Health and Nutrition Survey data. Finally, a goodness-of-fit test is conducted, and an example case is presented. The results show that the fitted model in this study is more accurate and precise than the original, unfitted model to estimate the residual life of human beings. This study is advantageous over existing studies in two aspects: (1) the model is formulated using a recursive method based on stochastic filtering; (2) the data include both physical status and mental status.

A distributed optimization problem is cooperatively solved by a network of agents, which have significant applications in many science and engineering fields, such as metallurgical engineering. For complex industrial processes with multiple-level characteristics, varying working conditions, and long processes, numerous optimization decision-making micro and macro control problems, such as product quality control, production planning, scheduling, and energy comprehensive deployment, are encountered. The theory and method of distributed optimization are keys to promoting the strategic decision-making of the integration of industrialization and new-generation industrial revolution. Their development enhances the ability to deal with large-scale and complex problems of big data, which have important practical value and economic benefits. In this study, consensus optimization with set constraints in multi-agent networks was explored. A distributed algorithm with a fixed step size was proposed on the basis of a primal-dual gradient scheme. Parameters such as step size affect the convergence of the algorithm. As such, convergence should be analyzed first, and appropriate parameters should be subsequently set in accordance with convergence conditions. Existing works have constructed different Lyapunov functions by exploiting the specific iteration scheme of this algorithm and analyzing convergence. Conversely, a convergence analysis paradigm based on a Lyapunov function was proposed in this study for general fixed step size iteration schemes, which were similar to the analysis method of Lyapunov convergence for general differential equations. A suitable Lyapunov function was constructed for the distributed gradient algorithm, and a parameter setting range was obtained in accordance with the convergence conditions. The proposed method avoids the tedious and complicated analysis of algorithm convergence and parameter assignment. The theory and method presented in this study also provide a framework and systematic demonstration method for other types of distributed algorithms and may be regarded as future directions of distributed optimization.

The proton exchange membrane fuel cell (PEMFC) is a promising power source, but the short lifespan and high maintenance cost restrict its development and widespread application. Performance degradation prediction is an effective technique to extend the lifespan and reduce the maintenance cost of PEMFC. This paper proposed a novel hybrid method for the performance degradation prediction of PEMFC. Firstly, considering the randomness of PEMFC degradation, a Wiener process model is established to describe the degradation of the aging factor. Secondly, the unscented Kalman filter algorithm is used to estimate the degradation state of the aging factor from monitoring voltage. Then, in order to predict the degradation state of PEMFC, the transformer structure is used to capture the data characteristics and fluctuations of the aging factor. To quantify the uncertainty of the predicted results, we also add the Monte Carlo dropout technology to the transformer to obtain the confidence interval of the predicted result. Finally, the effectiveness and superiority of the proposed method are verified on the experimental datasets.

The stability and convergence analysis of a multivariable stochastic self-tuning system (STC) is very difficult because of its highly nonlinear structure. In this paper, based on the virtual equivalent system method, the structural nonlinear or nonlinear dominated multivariable self-tuning system is transformed into a structural linear or linear dominated system, thus simplifying the stability and convergence analysis of multivariable STC systems. For the control process of a multivariable stochastic STC system, parameter estimation is required, and there may be three cases of parameter estimation convergence, convergence to the actual value and divergence. For these three cases, this paper provides four theorems and two corollaries. Given the theorems and corollaries, it can be directly concluded that the convergence of parameter estimation is a sufficient condition for the stability and convergence of stochastic STC systems but not a necessary condition, and the four theorems and two corollaries proposed in this paper are independent of specific controller design strategies and specific parameter estimation algorithms. The virtual equivalent system theory proposed in this paper does not need specific control strategies, parameters and estimation algorithms but only needs the nature of the system itself, which can judge the stability and convergence of the self-tuning system and relax the dependence of the system stability convergence criterion on the system structure information. The virtual equivalent system method proposed in this paper is proved to be effective when the parameter estimation may have convergence, convergence to the actual value and divergence.

Mechanical properties are important indexes to evaluate the quality of hot rolling strips. It is a research hotspot in the field of hot rolling that realizing timely and accurate soft sensing of mechanical properties. Traditional soft sensing methods have poor performance in the application of strong nonlinearity and multiple working conditions. Moreover, the utilization rate of data is relatively low, which limit the improvement of prediction accuracy. To solve the problems above, a just-in-time learning (JITL) based multi-block weighted semisupervised Gaussian mixture regression (JMWSSGMR) soft sensor is proposed in the paper. There are two stages in the soft sensor: off-line variable blocking and on-line local modeling. In the off-line phase, process variables are divided into different sub-blocks by partial least square (PLS) according to distinct principal component directions. In each sub-block, original variables with high contribution rate are retained. In the on-line phase, optimized Mahalanobis distance is constructed to select the most similar historical samples to the query sample. Next, various real-time semisupervised sub-models are built to estimate the output of the query sample. Finally, predicted values of sub-models are fused and ultimate prediction of mechanical properties is obtained. Case studies are carried out on a numerical example and a hot rolling process. The feasibility and effectiveness of proposed soft sensor are verified by the predicted results.

Customized production, quality variation of raw materials and other factors make industrial processes work in multiple operating modes. In general, complex industrial processes have strong nonlinearity under each operating mode. In this paper, a Gaussian mixture variational autoencoder (GMVAE) model, which combines with Gaussian mixture and VAE, is proposed to monitor nonlinear processes with multiple operating modes. Due to the Gaussian mixture distribution limitation in latent variable space, GMVAE can not only automatically extract features of the nonlinear system, but also make these features follow Gaussian mixture distribution. Based on Gaussian mixture distribution in latent variable space and the reconstruction error, two probability monitoring indexes are constructed, whose control limits can be determined by χ2 distribution. TE benchmark data and real hot strip mill process (HSMP) data have been used to verify the effectiveness of the proposed method.

In this paper, the problem of fault-tolerant control of actuators for multi-joint robots is studied. Aiming at the jitter problem in the design of fault-tolerant control law for conventional sliding mode controllers (SMC), a controller design method based on fractional-order sliding mode (FSMC) theory is proposed. At first, the mathematical model of the multi-joint robot is established and the fractional-order sliding mode surface is constructed according to the mathematical model. Then, the robust control law is designed based on the Lyapunov function. Finally, the experiments are carried out. Compared with the conventional sliding mode control, the experimental results show that the multi-joint robot is more stable under the control of fractional-order sliding mode, and it can achieve almost no jitter while tracking the reference. The steady-state error for joint1 and joint2 could reach 0.073 radians under the control of SMC, while it is 0.015 radians under the control of FSMC. The steady-state error data indicate that the fluctuation amplitude under FSMC is five times smaller than SMC for the end part of the multi-joint robot under actuator gain faults. The regulation time for joint1 and joint2 is about 0.11 s under the control of SMC, and it is around 0.04 s for FSMC. The regulation time is reduced to one of three or four. These data show the effectiveness of the FSMC proposed in this paper.

This paper is concerned with theoretical and practices approach for overall quality-related fault detection and identification in process industries. Fault detection and fault tracing can help engineers to take corrective actions and recover the process operations. A novel diagnostic method is proposed based on stacked automatic encoder-canonical correlation analysis (SAE-CCA) and least absolute shrinkage selection operator (Lasso). First, a quality monitoring scheme based on SAE-CCA is proposed, which establishes a relationship model among quality and characteristic variables to detect faults. Then, Lasso is used for locating the root causes, according to the process state and fault information. Finally, the experiments are conducted with typical industry process data, i.e., a hot strip mill process (HSMP), in order to demonstrate the effectiveness of the whole diagnosis method.

In actual production practice, the occurrence probability of multiple faults is much higher than that of a single fault. Since the composition of multiple faults is uncertain, it is difficult to establish a single model for multifault diagnosis. In this paper, a new hierarchical framework is proposed for solving multifault detection and isolation problems. First, an adaptive dynamic kernel independent component analysis method is proposed for time-varying and unknown multifault detection. After that, a sparse local exponential discriminant analysis method is developed for the multimodal multifault isolation problem. Finally, the Tennessee Eastman process is used to validate the performance of the proposed methods, and the experimental results show that the proposed methods can efficiently detect and isolate multiple faults.

This paper is devoted to investigating the observer-based fault detection (FD) filters for nonlinear distributed processes described by hyperbolic partial differential equations (PDEs). To this end, the PDE systems are first approximated by the Takagi-Sugeno fuzzy models with spatiotemporal uncertainties. Then, the fuzzy FD filter is developed for the hyperbolic PDE systems to guarantee that the residual signal is robust against process inputs including disturbances. The dynamic threshold is designed to ensure the real-time detection of potential faults. It is worth mentioning that the distributed weighting factors are used to weigh the residual signal such that the overall fault detectability can be optimized.

In this paper, an integrated framework for fault diagnosis and control is designed based on residual decoupling technology, which has fully considered the balance between the robustness of control system and the sensitivity of fault diagnosis system. To this end, the decoupling technology is first used for separation of fault and disturbance. Then, robust controller and fault-tolerant controller are designed for rejecting the system disturbance and guaranteeing the system performance, respectively. Finally, the application to three-tank system is given to demonstrate the performance and effectiveness of the proposed methods.

Distributed parameter systems (DPS) widely exist in the large-scale industrial production industry. Techniques developed for DPS can further demonstrate the complexity of the industrial process, such as the hot-rolled strip laminar cooling (HSLC) process. Due to the infinite dimensional of states variables and manipulated variables, it is a challenging work to model and monitor for DPS in practice. In this paper, a data-driven approach for process modeling and quality monitoring of DPS is obtained. A second-order partial differential equation (PDE) is transformed into finite-dimensional model of ordinary differential equation (ODE) with finite element method (FEM) and Galerkin method. Then, this model is described by state space with time-space separation. To realize the proposed scheme by the data-driven approach, we use the industrial process data to estimate the parameters in the model and basic functions by recursive least squares method. Based on this model, a kernel representation of DPS for residual generation is obtained in the statistical framework. $T^{2}$ statistic is employed to evaluate the residual and the threshold is determined by the use of noncentral $\chi ^{2}$ -distribution. Finally, the effectiveness of the proposed scheme is demonstrated by conducting a simulation on the production process of HSLC.

The explosion of different fault detection (FD) statistics in multivariate statistics-based FD approaches has meant that the practitioner is faced with the unenviable job of determining which to use in a given circumstance. Moreover, compared to extensive investigations on additive faults, the performance of commonly used FD statistics for detecting multiplicative faults has not been holistically evaluated. Therefore, this paper seeks to investigate the different statistics that can be applied to detect multiplicative faults in order to provide users and practitioners in the FD field with guidance to select an appropriate method. The considered statistics are broadly classified into three groups: traditional methods (e.g. T2-statistic) and their extensions; the Wishart distribution-based methods; and those methods created in the information and communication fields to describe the measurement variance and covariance (e.g. Kullback-Leibler divergence). These three groups are compared by considering the required probability distributions, interconnections, and detection performance for multiplicative faults. Using simulated data from numerical examples and the Tennessee Eastman benchmark process, the theoretical results are validated, and the applicability of multivariate statistics-based FD methods incorporating all considered statistics for detecting multiplicative faults is examined at the end of this paper.

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.

In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient is proposed to analyze the correlation of variables in causality graph quantitatively. To achieve accurate fault detection results, the proposed CI is monitored by probability principal component analysis. Moreover, the concept of weighted average value is introduced to identify fault propagation path based on reconstruction-based contribution and causality graph after detecting a fault. Finally, the new proposed scheme would be practiced with real industrial HSMP data, where the individual steps as well as the complete framework were extensively tested.

A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMM-based approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.

Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods.

An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor.

Aiming at damping the inter-area oscillations of power systems, the present study proposes a wide-area decentralized coordinated control framework, where the upper-level controller is designed to coordinate the lower-level multiple FACTS devices. Based on the polytopic differential inclusion method, the derived controller adopts a decentralized structure and it is guaranteed to be robust to meet the demand of operation under multiple operating conditions. Since time delay of wide area signal transmission is inevitable, in what follows, the quantum evolution algorithm (QEA) method is introduced to find an optimal solution of the time-delay coordinated controller. In this regard, the stability of the system with a prescribed time delay is guaranteed and the system damping ratio is increased. Effectiveness and applicability of the proposed controller design methods have been demonstrated through numerical simulations.