Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.

Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

Remaining useful life prediction for a nonlinear multi-degradation system with public noise

To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization(EM) algorithm. Next, with known thresholds,the RUL of each degradation can be predicted by using the first hitting time(FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.

Three-Dimensional Structure of Optimal Nonlinear Excitation for Decadal Variability of the Thermohaline Circulation

The decadal variability of the North Atlantic thermohaline circulation(THC) is investigated within a three-dimensional ocean circulation model using the conditional nonlinear optimal perturbation method. The results show that the optimal initial perturbations of temperature and salinity exciting the strongest decadal THC variations have similar structures: the perturbations are mainly in the northwestern basin at a depth ranging from 1500 to 3000 m. These temperature and salinity perturbations act as the optimal precursors for future modifications of the THC, highlighting the importance of observations in the northwestern basin to monitor the variations of temperature and salinity at depth. The decadal THC variation in the nonlinear model initialized by the optimal salinity perturbations is much stronger than that caused by the optimal temperature perturbations, indicating that salinity variations might play a relatively important role in exciting the decadal THC variability. Moreover, the decadal THC variations in the tangent linear and nonlinear models show remarkably different characteristics, suggesting the importance of nonlinear processes in the decadal variability of the THC.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Higher-Order Statistics and Nonlinear Processes in Space Plasmas

Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-particle interaction in space plasmas. The signals considered here are medium scale electron density irregularities and ELF/ULF electrostatic turbulence. Nonlinearities are mainly observed in the ELF range. They are independently pointed out in time series associated with fluctuations in electronic density and in time series associated with the measurement of one electric field component. Peaks in cross-bicorrelation function and in mutual information clearly show that, in well delimited frequency bands, the wave-particle interactions are nonlinear above a certain level of fluctuations. The way the energy is transferred within the frequencies of density fluctuations is indicated by a bi-spectra analysis.

New Algorithm Model for Processing GeneralizedDynamic Nonlinear Data Derived from Deformation Monitoring Network

The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.

Effect of Nonlinear Dynamic Process on Formation and Breakdown of Blocking

With the L-P approximate method(variation of parameter method), a barotropic channel model in β-plane is used to study the effect of nonlinear interaction between two waves with different scales on the formation of blocking. The approximate analytical solution, which can describe the process of the blocking formation, maintenance and breakdown, has been obtained by using the method of aproximate expansion. The importance of nonlinear interaction between two waves with different scales is stressed in the solution. The result suggests that the nonlinear interaction is the main dynamic process of the blocking formation. Some required conditions of blocking formation are also discussed.

On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares

An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

A universal robust limit theorem for nonlinear Lévy processes under sublinear expectation

This article establishes a universal robust limit theorem under a sublinear expectation framework.Under moment and consistency conditions,we show that,forα∈(1,2),the i.i.d.sequence{(1/√∑_(i=1)^(n)X_(i),1/n∑_(i=1)^(n)X_(i)Y_(i),1/α√n∑_(i=1)^(n)X_(i))}_(n=1)^(∞)converges in distribution to L_(1),where L_(t=(ε_(t),η_(t),ζ_(t))),t∈[0,1],is a multidimensional nonlinear Lévy process with an uncertainty■set as a set of Lévy triplets.This nonlinear Lévy process is characterized by a fully nonlinear and possibly degenerate partial integro-differential equation(PIDE){δ_(t)u(t,x,y,z)-sup_(F_(μ),q,Q)∈■{∫_(R^(d)δλu(t,x,y,z)(dλ)with.To construct the limit process,we develop a novel weak convergence approach based on the notions of tightness and weak compactness on a sublinear expectation space.We further prove a new type of Lévy-Khintchine representation formula to characterize.As a byproduct,we also provide a probabilistic approach to prove the existence of the above fully nonlinear degenerate PIDE.

Infrared upconversion imaging in nonlinear metasurfaces

Infrared imaging is a crucial technique in a multitude of applications,including night vision,autonomous vehicle navigation,optical tomography,and food quality control.Conventional infrared imaging technologies,however,require the use of materials such as narrow bandgap semiconductors,which are sensitive to thermal noise and often require cryogenic cooling.We demonstrate a compact all-optical alternative to perform infrared imaging in a metasurface composed of GaAs semiconductor nanoantennas,using a nonlinear wave-mixing process.We experimentally show the upconversion of short-wave infrared wavelengths via the coherent parametric process of sum-frequency generation.In this process,an infrared image of a target is mixed inside the metasurface with a strong pump beam,translating the image from the infrared to the visible in a nanoscale ultrathin imaging device.Our results open up new opportunities for the development of compact infrared imaging devices with applications in infrared vision and life sciences.

Nonlinear Branching Processes with Immigration

The nonlinear branching process with immigration is constructed as the pathwise unique solution of a stochastic integral equation driven by Poisson random measures. Some criteria for the regularity, recurrence, ergodicity and strong ergodicity of the process are then established.

Sensor Fault Tolerant Generic Model Control for Nonlinear Systems

A modified Strong Tracking Filter (STF) is used to develop a new approach to sensor fault tolerant control. Generic Model Control (GMC) is used to control the nonlinear process while the process runs normally because of its robust control performance. If a fault occurs in the sensor, a sensor bias vector is then introduced to the output equation of the process model. The sensor bias vector is estimated on line during every control period using the STF. The estimated sensor bias vector is used to develop a fault detection mechanism to supervise the sensors. When a sensor fault occurs, the conventional GMC is switched to a fault tolerant control scheme, which is, in essence, a state estimation and output prediction based GMC. The laboratory experimental results on a three tank system demonstrate the effectiveness of the proposed Sensor Fault Tolerant Generic Model Control (SFTGMC) approach.

Operator-based Nonlinear Temperature Control Experiment for Microreactor Group Actuated by Peltier Devices

In this paper, operator-based nonlinear water temperature control for a group of three connected microreactors actuated by Peltier devices is proposed. To control the water temperature of tube in the microreactor, the temperature change of aluminum effects is considered. Therefore, the temperature change of aluminum becomes the part of an input of the tube. First, nonlinear thermal models of aluminum plates and tubes that structure the microreactor are obtained. Then, an operator based nonlinear water temperature control system for the microreactor is designed. Finally, the effectiveness of the proposed models and methods is confirmed by simulation and experimental results.

Importance-Weighted Transfer Learning for Fault Classification under Covariate Shift

In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.

Sensor Network Design for Nonlinear Processes

This paper presents a method to design a cost-optimal nonredundant sensor network to observe all variables in a general nonlinear process. A mixed integer linear programming model was used to minimize the cost with data classification to check the observability of all unmeasured variables. This work is a starting point for designing sensor networks for general nonlinear processes based on various criteria, such as reliability and accuracy.

The roles of heterogeneous chemical processes in the formation of an air pollution complex and gray haze

Urban and regional air pollutions are characterized by high concentrations of secondary pollutants such as photo-oxidants （mainly ozone） and fine particulate matter, which are formed through chemical reactions of the primary pollutants emitted from various sources. The accumulation of these pollutants under stagnant meteorological conditions results in the formation of gray haze, reducing visibility and causing major impacts on human health and climate. In an air pollution complex, the co- existence of high concentrations of primary and secondary gaseous and particulate pollutants provides a large amount of reac- tants for heterogeneous reactions on the surface of fine particles; these reactions change the oxidizing capacity of the atmos- phere, as well as chemical compositions along with the physicochemical and optical properties of particulate matter, thereby accelerating formation of the air pollution complex and gray haze. Using in situ technologies, such as diffuse reflectance infra- red Fourier-transform spectroscopy and single-particle Raman spectroscopy, we systematically investigated the reaction kinet- ics and mechanisms of gaseous pollutants （i.e., NO2, SO2, 03, and formaldehyde） on the surfaces of the major components of atmospheric particles such as CaCO3, kaolinite, montmorillonite, NaC1, sea salt, A1203, and Tit2. We found that the main re- action products were sulfate, nitrate, or formate, which can change the hygroscopicity and light extinction parameters of those particles significantly. By analyzing the reaction kinetics of these heterogeneous reactions, we identified synergetic mechanisms of the three ternary reaction systems, ,i.e., NOE-particles-H2O, SO2-particles-O3, and organics/SO2-particles-UV illumination. These synergetic mechanisms can provide experimental and theoretical bases for understanding the feedback mechanisms and nonlinear processes in the formation of an air pollution complex and gray haze.

Optical vortex copier and regenerator in the Fourier domain

The generation and manipulation of optical vortices are of fundamental importance in a variety of promising applications. Here, we develop a nonlinear optical paradigm to implement self- and cross-convolution of optical vortex arrays, demonstrating the features of a vortex copier and regenerator. We use a phase-only spatial light modulator to prepare the 1064 nm invisible fundamental light to carry special optical vortex arrays and use a potassium titanyl phosphate crystal to perform type Ⅱ second-harmonic generation in the Fourier domain to achieve 532 nm visible structured vortices. Based on pure cross-convolution, we succeed in copying arbitrary-order single vortices as well as their superposition states onto a prearranged array of fundamental Gaussian spots. Also, based on the simultaneous effect of self- and cross-convolutions, we can expand the initial vortex lattices to regenerate more vortices carrying various higher topological charges. Our presented method of realizing imaging, an optical vortex copier and regenerator could find direct applications in optical mani optical communication, and quantum information processing with structured vortex pulation, optical arrays.

Two-color multiphoton in vivo imaging with a femtosecond diamond Raman laser

Two-color multiphoton microscopy through wavelength mixing of synchronized lasers has been shown to increase the spectral window of excitable fluorophores without the need for wavelength tuning.However,most currently available dual output laser sources rely on the costly and complicated optical parametric generation approach.In this report,we detail a relatively simple and low cost diamond Raman laser pumped by a ytterbium fiber amplifier emitting at 1055 nm,which generates a first Stokes emission centered at 1240 nm with a pulse width of 100 fs.The two excitation wavelengths of 1055 and 1240 nm,along with the effective two-color excitation wavelength of 1140 nm,provide an almost complete coverage of fluorophores excitable within the range of 1000–1300 nm.When compared with 1055 nm excitation,two-color excitation at 1140 nm offers a 90%increase in signal for many far-red emitting fluorescent proteins(for example,tdKatushka2).We demonstrate multicolor imaging of tdKatushka2 and Hoechst 33342 via simultaneous two-color two-photon,and two-color three-photon microscopy in engineered 3D multicellular spheroids.We further discuss potential benefits and applications for two-color three-photon excitation.In addition,we show that this laser system is capable of in vivo imaging in mouse cortex to nearly 1 mm in depth with two-color excitation.