Residual Generation Research Articles

A data‐driven fault detection and fault‐tolerant control scheme for large‐scale systems and its application on multi‐area interconnected power systems

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.

Reduced-order observer design for fault diagnosis of Boolean control networks

In this paper, we propose an approach to design reduced-order observer of Boolean control networks (BCNs) for fault diagnosis by applying the semi-tensor product (STP) of matrices. At first, for observability analysis a computationally efficient approach based on the relational coarsest partition problem (RCP) is proposed. Then a necessary and sufficient condition for fault detectability analysis is introduced. After that an approach is introduced to design a reduced-order observer for fault detection. If fault occurrence has been detected, then an approach is proposed to solve fault isolation problem of BCNs. The basic idea is to separate dynamics of the faults into different independent subsystems by using graph theory. For each subsystem a residual generator is constructed based on reduced-order observer by getting rid of indistinguishable states. By theoretical analysis and simulation study it is shown that the approach proposed in the paper for fault isolation requires lower computational effort than the conventional scheme of a bank of full-order observers.

Chemical and Mineralogical Characterization of Montevive Celestine Mineral

The Montevive celestine mineral deposit, set in the Granada Basin in a marine evaporitic uppermost Tortonian–lowermost Messinian sequence, is the largest reserve in Europe of this economically important strontium ore. Currently, the mine has a large amount of tailings resulting from the rejection of a manual dry screening of high-grade celestine mineral. This visual and density screening was carried out in the early days of mining (1954–1973). Concentrating the celestine mineral and increasing the ore recovery rate would reduce mine operation costs and the generation of new tailings, reducing the impact on the environment. In order to define more adequate concentration methods, we have used complementary analytical techniques such as optical (OM) and scanning and transmission electron microscopy (SEM and TEM), energy-dispersive X-rays (EDXs), X-ray fluorescence (XRF), and X-ray diffraction (XRD) to fully characterize the morphology, microstructure, chemistry, and mineralogy of the celestine mineral. The low-grade mineral is made of prismatic celestine crystals that are replacing a matrix of micro sparry calcite. Other minority minerals are strontianite, dolomite, quartz, and clays (kaolinite, paragonite, and illite). There is also a certain amount of iron oxides and hydroxides (mainly magnetite) associated with clays. We showed that the concentration of low-grade celestine mineral can be achieved through a low-cost and eco-friendly method based on grinding and size separation. The coarser fractions (>5 mm) have more celestine (up to 12 percent units higher than the starting unprocessed mineral) due to the selective loss of calcite and minority minerals (quartz, clays, and iron oxides) that are mainly found in the finer fraction (<1 mm). This process can make mine exploitation more sustainable, reducing the generation of residues that negatively impact the environment.

Event-Triggered Fault Detection Filtering of Fuzzy-Model-Based Systems With Prescribed Performance

This article deals with the fault detection filtering problem for a nonlinear dynamic system in the Takagi–Sugeno fuzzy framework. An event-triggered scheme considering network bandwidth utilization rate and fault occurrence probability is utilized, which can conserve communication resources and reduce computational burden. The fault detection fuzzy filter is used as a residual generator, and the proposed event-based fault detection scheme is formulated as a fuzzy filtering problem. Moreover, the generated residual is robust against exogenous disturbances while being sensitive to system faults, and the resulting fault detection system has asymptotic stability with a specified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> error property. The corresponding solvability conditions of the fault detection filter are constructed by converting the nonconvex feasibility problem into a series of optimization problems. Finally, a numerical simulation is conducted to demonstrate the feasibility and validity of the proposed scheme.

MUNICIPAL WASTE IN SLOVAKIA AS A POTENTIAL FOR THE CIRCULAR ECONOMY

If we talk about municipal waste in connection with circular economy, each European produces about 500 kg/year of waste. Less than half of it is 46% recycled, 27% is incinerated and 24% is landfilled. However, if we talk about the Slovak Republic, in 2019, 1 person produced an average of 421 kg/year of waste, and this indicator has a significantly rising character. In the context of the circular economy, an interesting indicator is precisely the rate of waste recycling, which is at the level of 38.5% in Slovakia. A similar analysis of municipal waste generation in selected EU countries was carried out in 2019, with the aim of identifying the potential for residual municipal waste generation, taking into account historical data, EU targets and existing residual waste treatment capacities. The results of this assessment for the Slovak Republic are discussed in article, which indicate a lack of processing capacity for non-recyclable waste.

Unmanned Ground Vehicle Platooning Under Cyber Attacks: A Human-Robot Interaction Framework

In this paper, we propose a human-robot interaction (HRI) framework to reduce the vulnerability of an unmanned ground vehicle (UGV) platoon under cyber attacks. An observer-based autonomous resilient control strategy is first designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. Rigorous proof of the safety conditions of the platoon under V2V cyber attacks is derived. Next, to facilitate human supervision in emergencies and uncertainties, a decision-making aid system is developed, which consists of an anomaly reporting system (ARS), a trust-based information management system (TIMS), and a graphical user interface (GUI). ARS is designed to detect the abnormality of the neighbor UGVs based on the model-based residual generation and analysis. TIMS is developed to rule out information with low trustworthiness, which is fulfilled by a Bayesian-based information pre-processor followed by an enhanced median filter with trustworthiness regulation. It is rigorously proved that TIMS can recover the tempered information under the mild cyber attack scenario autonomously. To deal with unexpected severe vehicle-to-cloud (V2C) cyber attacks, human observations can also be leveraged to bolster the resilience of the TIMS. Representative simulation and humans-in-the-loop experiment (fulfilled by the designed GUI) demonstrate that the proposed framework can effectively guide human operators when working with UGV platoons under cyber attacks. On average, compared to a multi-screen surveillance benchmark approach, the proposed framework can achieve 78.4% decrements of platoon vulnerability and 39.0% decrements in human workload.

An Integrated Design Scheme for SKR-Based Data-Driven Dynamic Fault Detection Systems

In this article, an integrated design diagram for a stable kernel representation (SKR)-based data-driven fault detection (FD) system and performance criteria is proposed for stochastic dynamic systems in the probabilistic sense. A new distributionally robust FD system is developed using input and output data in the absence of a system model and perfect probability distributions for noises and random faults. To be specific, an SKR-based data-driven primary residual generator is first constructed. By introducing the so-called mean-covariance based ambiguity sets, families of probability distributions of the primary residual in fault-free and the concerned multiple faulty cases are characterized. The FD system design is then formulated as a distributionally robust optimization problem in the sense of minimizing the missed detection rate (MDR) with a predefined upper bound of false alarm rate (FAR). With the aid of worst-case conditional value-at-risk, a matrix-valued distribution independent solution to the targeting FD problem is derived without posing specific distribution assumptions. The developed FD system is, thus, robust against the distributional uncertainties of noises and random faults. Simultaneously, a tighter upper bound of MDR for an identical FAR criterion is achieved in comparison with the vector-valued distributionally robust FD method. An experimental study on a laboratory setup of a three-tank system shows the applicability of the proposed method.

Mutually activated residual linear modeling GAN for pose-guided person image generation

Translating a pose of a given person to another desired pose is popular in computer vision applications. However, previous works usually directly utilized pose information to guide appearance information for the generation without deep consideration of the interaction between these two kinds of information. Moreover, the global long-range relation that exists in both kinds of data has not been well modeled due to the physical design of convolutional filters. In this paper, a novel Mutually Activated Residual Linear Modeling Generative Adversarial Network (MARLM-GAN) is proposed to address these two challenges. The MARLM-GAN consists of T cascaded MARLM modules for learning the latent transformation progressively from both appearance and pose codes. In each MARLM module, there are two mutually-activated residual linear modeling blocks for both appearance and pose pathways. In addition, an information update strategy is also developed, which makes the latent appearance and pose representations benefit each other interactively. Our experiments on two challenging datasets demonstrate that the proposed MARLM-GAN can achieve competitive results in terms of objective evaluation metrics and subjective visual realness compared with recent state-of-the-art methods.

Fault detection in uncertain systems via proportional integral observer

This article focuses on the design of a proportional integral observer (PIO) for fault detection in uncertain systems. For multi-output linear systems with actuator fault and disturbances, an approach using two PIOs is designed to estimate both the system states and disturbances simultaneously when the actuator fault is free. Then a residual generator based on the output function is used to identify whether the fault is occurring. For a class of nonlinear systems with constant disturbances, a full-order PIO is designed to estimate the disturbances. Similarly, the residual generator is used to identify whether the fault is occurring. Numerical examples are given to show the efficiency of the proposed approach.

Dilute-and-shoot versus clean-up approaches: A comprehensive evaluation for the determination of mycotoxins in nuts by UHPLC-MS/MS

Mycotoxins are toxic metabolites naturally occurring in foodstuffs stored in warm, humid conditions. Ochratoxin A, aflatoxins B1, B2, G1, and G2 are known for their adverse health effects. Thus, EU permitted levels are set as low as 2 μg kg−1 for nuts. This study examined solid-liquid extraction (SLE) to isolate mycotoxins from walnuts, followed by UHPLC-MS/MS analysis. However, the strong matrix effect obtained is the main limitation of analyzing mycotoxins in fatty matrices, like nuts. To overcome this problem, 6 clean-ups intended for fatty matrices (PRiME HLB, EMR-Lipid, AFFINIMIP cartridges, Z-sep+, C18, and PSA) were tested in terms of matrix effect, cleanliness of the extract, and co-extractives removal. Nevertheless, none of these clean-ups effectively reduced the strong matrix effects encountered by the SLE. So, the addition of more steps would not be profitable in terms of time, cost, and residues generation. As an alternative, the streamlined, cheap, simple, and fast “dilute-and-shoot” approach was tested. This work shows that by applying 1:100 dilution of SLE extract, obtained matrix effect was soft or negligible. Finally, a 1:100 dilute-and-shoot method was proposed and validated according to EU regulations. LOQs fulfilled EU requirements and satisfactory values were obtained for recovery and precision.

Distributed Intermittent Fault Detection for Linear Stochastic Systems Over Sensor Network.

In this article, the problem of intermittent fault (IF) detection is investigated for linear stochastic systems over sensor networks, where the appearing and disappearing times, and magnitude of IF are all nondeterministic. By utilizing the moving-horizon estimator, a novel residual generator is designed to realize the distributed detection of IFs in sensor networks. Different from the traditional moving horizon estimation algorithms, weight matrices of the quadratic cost function in this article are regulated by an unreliability index of the prior estimate to suppress the smearing effect of IFs. In virtue of the matrix transformation method and statistical theory, estimator parameters are obtained and the detectability of a single IF is analyzed by using the residual. In order to avoid the collisions of detection results from different residuals, the global detectability condition is given for all IFs. A cooperative decision-making strategy is proposed such that the only detection result can be guaranteed, which includes the appearing and disappearing times of IFs, and the nodes suffering from IFs. Finally, an illustrative example is provided to show the feasibility and effectiveness of the derived results.

Methodology for Mapping the Residual Stress Field in Serviced Rails Using LCR Waves.

Non-destructive stress measurement by ultrasonic testing is based on calculating the acoustoelastic modulus obtained from the relationship between material stress and sound wave velocity. A critically refracted longitudinal (LCR) wave, which is a bulk longitudinal wave penetrating below and parallel to the surface below an effective depth, is most suitable for ultrasonic stress measurement tests because it exhibits a relatively large change in travel time in response to a change in stress. In particular, the residual stress distribution through the thickness of the subject can be calculated if transducers of different frequencies are applied because of the characteristic of propagation to different depths of penetration depending on the frequency. The main purpose of this study was to visualize the internal or residual stress distribution through the thickness of rails using LCR waves. To this end, LCR probes with different center frequencies were designed and manufactured, and the residual stress values ​​of an unused railroad rail and two used railroad rails operated under different conditions were calculated. This was done using the ultrasonic signals received from each probe, of which the distributions were mapped. Through these mapping results, different residual stress values could be calculated according to the depth. The differences in residual stress generation and distribution according to the conditions surrounding the contact between train wheels and rails, and their characteristics, were visualized and analyzed. As a result, it could be concluded that the non-destructive evaluation technique using LCR waves could detect differences in the residual stress of a rail, and thus can be used to measure the residual stress of the rail accurately.

Adsorption studies of the hybrid material obtained from the functionalization of silica with alfa and gamma cyclodextrins

The generation of residues containing dyes by industrial sectors has been mobilizing scientists to develop methodologies capable of treating water containing these contaminants. Adsorption is an option to remove these molecules from the aqueous medium and, for this study, the composites between silica and cyclodextrins alpha (α-CDSI) and gamma (γ-CDSI) were used to capture methylene blue. Adsorption was spontaneous for both composites (ΔG < 0) and characterized as exothermic and of a physical nature, with ΔH of −17.68 and −12.13 kJ mol−1 for α-CDSI and γ-CDSI, respectively. Adsorption took place over a wide pH range, with an efficiency of approximately 96%, reaching equilibrium at 5 minutes. The adsorption kinetics was described by the pseudo-second-order model (R2 > 0.999) and the adsorption isotherms showed that the process must occur mainly by dye complexation in the cyclodextrins cavities. The qm values obtained were 210.8 and 205.2 mg g−1 for α-CDSI and γ-CDSI, respectively; the Sips and Temkin models were the ones that best fit the experimental data. The deposition and interactions of the dye with the adsorbent surface were confirmed by the analysis of the IR spectra. Desorption studies showed that the material maintained its adsorption capacity of around 90% until the fourth adsorption/desorption cycle. Thus, the materials produced showed to efficiently remove methylene blue and that composite reuse is a viable process for application in dye removal.

ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System

Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been developed to reduce signal processing complexity. This DL framework uses conditional generative adversarial networks (cGANs) and various convolutional neural networks, namely reverse residual network (reverse ResNet), squeeze-and-excitation ResNet (SE ResNet), ResUNet++, and reverse SE ResNet, as the generator model of cGAN for extracting the features from the quantized received signals. The simulation results of this paper show that the trained residual block-based generator model of cGAN has better channel generation performance than the standard generator model in terms of mean square error.

Application of non-thermal plasma as an alternative for purification of bacterial cellulose membranes

During the production of cellulose a purification process is applied, which is essential for its safe application in certain contexts, such as skin replacement, as it removes metabolites and bacterial cell debris. Currently, the most frequently adopted methodology is the immersion of the membranes in NaOH solution and this generates chemical residues. Also, the lack of consensus on the ideal methodology has led to discrepancies between results obtained by different authors. In the study reported herein, the action of non-thermal plasma (NTP) technology when applied in the bacterial cellulose (BC) purification process was investigated. The BC was synthesized using Gluconacetobacter hansenii. It was removed from the culture medium and immediately exposed to the NTP reactor in sterile distilled water for different treatment periods (10–30 min). The production capacity was determined and the BC obtained was subjected to physical-chemical characterization, morphological analysis, live/dead fluorescent microscopy and surface characterization via contact angle measurements. The results showed that after 15 min of treatment with NTP, no new membranes appeared when the BC samples were placed back in the culture medium. Micrographs of the membranes subjected to 10 min of plasma treatment showed bacteria or bacterial remnants (in both inner and outer areas) while after 15 min a significant decrease in bacterial cells was observed, mainly in the inner area. This demonstrates that the plasma can be used for the superficial and internal reduction of colonies present in BC membranes, to levels of almost complete elimination. The results reported herein demonstrate the potential of NTP application in the bacterial cellulose purification process, optimizing the production time and avoiding the generation of aggressive chemical residues.

Modelling residual stress and residual work hardening induced by surface mechanical attrition treatment

Shot peening induced residual stress is strongly related to residual work hardening which relies on both the process and the target material. In this work, residual stress and coupled residual work hardening are studied in a surface mechanical attrition treatment (SMAT) on a cylindrical structure by combining discrete element method (DEM) and finite element method (FEM). Shot, ultrasonic parameters, chamber structure, sonotrode surface, and cylindrical structure of the target are finely modeled in DEM simulation. On the basis of the DEM results, FEM simulation of the SMAT process is performed on a representative area considering the factual impact parameters and elastic-plastic behavior of the target material, and mechanism of residual stress generation is analyzed. It shows that isotropic hardening and kinematic hardening of the target material play a different role in residual stress generation. The obtained maximum compressive residual stress is mainly determined by residual yield stress, while the residual kinematic hardening is nearly unchanged when the coverage is increased. Further analyses indicate that the mechanisms of residual stress generation should be very different for the Johnson-Cook model and the model that includes kinematic hardening, even though these two kinds of models are reported to be able to well predict the shot peening induced residual stress. The gradient of the residual work hardening can be used for further evaluating mechanical properties of the SMATed cylindrical structure.

Constructing growth evolution laws of arteries via reinforcement learning

Growth evolution laws, which mathematically describe how living tissues change their shape and properties in response to external stimuli, are required for modeling arterial growth. Traditionally, specific forms of growth laws are devised by domain experts. Since in vivo animal studies usually provide limited experimental data, generalization and inference are often employed to prescribe the functional form of growth laws. In this work, we employed the finite growth theory and developed a reinforcement learning (RL) approach to construct growth evolution laws by formulating the arterial growth problem under the framework of the Markov decision process (MDP). To maintain homeostatic stress levels in an optimal manner, RL agents were employed to determine stress-modulated anisotropic growth evolution at each time step. We illustrate the capabilities of the RL-based growth laws in two representative applications: 1) predicting homogenous growth of a thin-walled artery in response to hypertensive blood pressure, and 2) generating residual stress with heterogeneous growth in a thick-walled bi-layer aorta via distributed growth policies. Experimental data, where available, were used to compare expert-prescribed and RL-based growth laws. Our results demonstrated the capabilities of RL to effectively control the growth processes in response to hypertension, and the predictions are in good agreement with experimental observations. In particular, the RL growth laws captured the reduction of in vivo axial stretch without using experimental data for training. Moreover, the distributed RL growth policies achieved residual stress generation in a collaborative manner, which may pave the way for implementation in a finite element setting. This study sheds light on a new avenue to uncover growth evolution laws via RL, perhaps reducing the need for large experimental datasets and expert intelligence during growth law construction.

Investigation of the optimal culture time for warmed bovine ovarian tissues before transplantation†.

Ovarian tissue cryopreservation by vitrification is an effective technique, but there are still many unresolved issues related to the procedure. The aim of this study was to investigate the optimal culture time of postwarmed ovarian tissues and their viability before ovarian tissue transplantation. The bovine ovarian tissues were used to evaluate the effect of postwarming culture periods (0, 0.25, 0.5, 1, 2, 5, and 24h) in the levels of residual cryoprotectant, LDH release, ROS generation, gene and protein abundance, and follicle viability and its mitochondrial membrane potential. Residual cryoprotectant concentration decreased significantly after 1h of culture. The warmed ovarian tissues that underwent between 0 and 2h of culture time showed similar LDH and ROS levels compared with fresh nonfrozen tissues. The anti-Mullerian hormone transcript abundance did not differ in any of the groups. No increase in the relative transcript abundance and protein level of Caspase 3 and Cleaved-Caspase 3, respectively, in the first 2h of culture after warming. On the other hand, an increased protein level of double stranded DNA breaks (gamma-H2AX) was observed in postwarmed tissues disregarding the length of culture time, and a temporary reduction in pan-AKT was detected in postwarming tissues between 0 and 0.25h of culture time. Prolonged culture time lowered the percentage of viable follicles in warmed tissues, but it did not seem to affect the follicular mitochondrial membrane potential. In conclusion, 1-2h of culture time would be optimal for vitrified-warmed tissues before transplantation.

A Residual-Generator-Based Plug-and-Play Control Scheme Toward Enhancing Power Quality in AC Microgrids

In this article, an active power quality control scheme centered on an observer-based residual generator is proposed for power quality improvement in ac microgrids. The active power quality controller is realized by a plug-and-play (PnP) configuration and consists of three parts: the control of the power electronic converter, the flexible structure, and the optimization algorithm. By means of the observer-based realization of the Youla parameterization of a stabilizing controller, the active power quality controller with the PnP flexible structure is designed. The proposed active controller has the advantage of improving power quality without changing the structure or parameters of the original controller. The optimization of the controller is implemented by tuning the Youla parameterization system in the PnP flexible structure. Here, a gradient descent optimizing algorithm is applied to estimate the optimal tuning/control parameters online. Finally, extensive hardware-in-the-loop experiments and simulations are given to demonstrate the effectiveness of the proposed power quality controller.

Evaluation of Phase Transformation Strain and Its Influence on Residual Stress Generation in Laser Welded Ti–6Al–4V Alloy

Abstract The assessment of welding-induced residual stress is always of significant interest due to its adverse effect on the structural stability and mechanical performance of the welded structure. Incorporation of intricate phenomena, in particular, thermal gradient, phase development, volumetric dilation, and phase transformation strain during FE modelling, not only acts as a reliable method for residual stress calculation but also serves as a directive to reduce tensile residual stress in a weldment which is sensitive to the selection of materials. In order to investigate the same for continuous and pulse laser-welded Ti–6Al–4V alloy, the sequentially coupled thermal–metallurgical–mechanical models are established. The internal state-dependent variables (SDVs) are implemented to capture the growth of phase evolved during diffusionless β → α′ transformation using Koistinen–Marburger (K–M) theory in the cooling cycle. The role played by a phase transformation induced strain on the generation of residual stress is systematically investigated. The volumetric dilation and associated phase fraction form the basis for the estimation of phase transformation strain in the present study. The accomplishment of highest martensitic fraction (∼95%) produced a phase transformation strain of 7.95 × 10−3 in pulse mode of operation. As a result, reversal of residual stress from tensile to compressive is perceived for pulse laser-welded specimen. Similarly, a sign of enriched martensitic transformation is noticed that puts the weld surface into a compression state and mitigates the overall tensile residual stress. The assumption of diffusional phase transformation during heating cycle and non-diffusional transformation during cooling phase in laser welding is more appropriate to predict the residual stress using thermal–metallurgical–mechanical model.