Adaptive Identification Research Articles

Fault Isolation of a Class of Uncertain Nonlinear Parabolic PDE Systems

This paper proposes a novel fault isolation (FI) scheme for distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with nonlinear uncertain dynamics. A key feature of the proposed FI scheme is its capability of dealing with the effects of system uncertainties for accurate FI. Specifically, an approximate ordinary differential equation (ODE) system is first derived to capture the dominant dynamics of the original PDE system. An adaptive dynamics identification approach using radial basis function neural network is then proposed based on this ODE system, to achieve locally-accurate identification of the uncertain system dynamics under faulty modes. A bank of FI estimators with associated adaptive thresholds are finally designed for real-time FI decision making. Rigorous analysis on the fault isolatability is provided. Simulation study on a representative transport-reaction process is conducted to demonstrate the effectiveness of the proposed approach.

A Novel Empirical Variational Mode Decomposition for Early Fault Feature Extraction

Early fault features of large-scale and low-speed mechanical equipment with heavy duty are weak and exhibit strong non-stationary characteristics. The adaptive extraction and identification of highly relevant important features from such signals has attracted significant attention. In this study, a novel empirical variational mode decomposition and exact Teager energy operator are proposed to explore valuable information. To highlight the fault impact signal representation, we use the exact energy operator to enhance the weak-impact components in the early fault signal. The proposed binary mechanism effectively distinguishes irrelevant features based on the adaptive decomposition parameter construction strategy. Therefore, interference features are easily removed from similar mixed signals, and the independent mode features are determined. The experimental results of the simulation and collected data are compared with those obtained with existing signal decomposition methods, and the superiority of the proposed method, owing to its better modal distinction and less time consumption, is verified.

Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this article, we propose a particle center AdaBoost (PCAD) method and achieve the multiparameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the AdaBoost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded two-dimensional image by PCAD, we can distinguish landmines from other targets.

Model reference adaptive system-based precise online inertia identification of induction motor with error utilisation technique

Time varying parameters can be estimated using either adaptive control or robust control. Robust control can be used only when the control law is fixed and varying parameters are bounded. Adaptive control is concerned with control law changing itself. Model reference adaptive system (MRAS) is an adaptive control method that can be used to identify initially unknown parameters. Variation in load and machine vibrations affect the control system performance of any motor drive system when it is in operating condition. Estimation of moment of inertia becomes essential to improve the dynamic response characteristics of any high-performance asynchronous machine (ASM) drive system. A novel method using model reference adaptive system (MRAS) with error utilisation technique (EUT) for precise identification of online varying inertia is presented in this article. The proposed method uses an interesting relation between the estimation error and actual inertia, to compensate for the error and provide high precision online inertia identification.

Glasdi: Parametric Physics-Informed Greedy Latent Space Dynamics Identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. An interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on-the-fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, an efficient k-nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 66 to 4,417x speed-up with 1 to 5% relative errors.

ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ РАБОТОСПОСОБНОСТИ МЕТОДИКИ АДАПТИВНОЙ ИДЕНТИФИКАЦИИ ЭЛЕКТРИЧЕСКИХ ПАРАМЕТРОВ АСИНХРОННОЙ МАШИНЫ С РАЗОМКНУТОЙ ОБМОТКОЙ РОТОРА В УСТАНОВИВШЕМСЯ РЕЖИМЕ НА ОСНОВЕ БАЛАНСА МОЩНОСТЕЙ

Relevance For the operation of a closed-loop sensorless electric drive as part of an electrical complex, it is necessary to ensure adequate restoration of the vector of state variables of an induction motor using a customizable mathematical model in the control system. The accuracy of restoring the state variable vector depends on two factors. The first factor is the mathematical description of the substitution circuit of an asynchronous motor by a system of differential equations with certain assumptions that reliably reflects the physical processes occurring in an asynchronous motor. Used in the electric drive control system, T- and L-shaped equivaent circuits of an asynchronous motor, described by a system of differential equations in a customizable mathematical model, are universal and allow simulating the behavior of an asynchronous motor in all operating modes: «start», «load surge», «load shedding», «reverse», «stop», which is a sufficient condition for their use without modifications within the framework of this article. The second factor — the coefficients in the system of differential equations with some error correspond to the electrical parameters of the equivalent circuit of an asynchronous motor. In turn, the electrical parameters of the equivalent circuit of an asynchronous motor are unique for each motor and cannot be used in a system of differential equations without their refinement, i.e. no identification process. There are several types of identification of the electrical parameters of an induction motor: by the experience of idling and short circuit, by catalog data, by induction motor attenuation with a locked rotor of an induction motor, and others. The described methods for identifying parameters in most cases require the decommissioning of an asynchronous motor and conducting separate experimental studies, after which the electrical parameters in the equivalent circuit are considered constant and unchanged during operation. It is known that when an asynchronous motor is heated during operation, the active resistances of the stator and rotor circuits change. The inductance of the main magnetization circuit, stator and rotor circuits vary depending on the shape of the supply voltage and the operating modes of the asynchronous motor. Changing these parameters means that the restoration of the state variable vector will occur with a larger error. Given the above, the development of a methodology for adaptive identification of the electrical parameters of the equivalent circuit of an asynchronous motor during operation, without decommissioning and shutting down the entire electrical complex, is an urgent scientific and practically significant task. Aim of research Development and experimental study of a method for determining the electrical parameters of the stator winding in steady-state operation modes without decommissioning and stopping the electrical complex. Research methods Systems of nonlinear equations, iterative procedures, Newton's method, numerical methods, state space method, optimization methods. Results A method of adaptive identification of the electrical parameters of the stator winding replacement circuit in steady-state operation based on the power balance has developed and experimentally tested, which allows for accurate estimation of the parameters of the stator winding replacement circuit during induction motor operation. The developed technique in the future, with the expansion of the mathematical apparatus, will allow determining the parameters of both the stator and rotor windings of the induction motor when the electric drive is operating as part of an electrical complex. The practical application of open woundrotor induction motor is not possible, therefore this article is of a research nature and is intended only for experimental confirmation of the operation of the technique.

Adaptive Identification of Linear Systems with a Mix of Static and Time-Varying Parameters

Adaptive Identification of Linear Systems with a Mix of Static and Time-Varying Parameters

Adaptive Identification and Application of Flow Mapping and Inverse Flow Mapping for Electrohydraulic Valves

Good estimation of flow mapping (FM) and inverse flow mapping (IFM) for electrohydraulic valves are important in automation of fluid power system. The purpose of this paper is to propose adaptive identification methods based on LSM, BPNN, RBFNN, GRNN, LSSVM and RLSM to estimate the uncertain structure and parameters in flow mapping and inverse flow mapping for electrohydraulic valves. In order to reduce the complexity and improve the identification performance, model structures derived from new algorithm are introduced. The above identification methods are applied to map the flow characteristic of an electrohydraulic valve. With the help of novel simulation architecture via OPC UA, the accuracy and efficiency of these algorithms could be verified. Some issues like invertibility of flow mapping are discussed. At last, places and suggestions to apply these methods are made.

An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront

In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level Leq,1′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of Leq,1′ falling prevalently within the range ±0.5 dB. The experimental profile of Leq,1′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations.

Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record

Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both short- and long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.

OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes

With the increasing process complexities, data-driven fault prognosis has emerged as a promising fault management tool that predicts and manages abnormal events well in advance. In this paper, we develop a fault prognosis framework named ‘OASIS-P’ by integrating operable adaptive sparse identification of systems (OASIS), which is a data-driven adaptive modeling technique, with a risk-based process monitoring approach and contribution plots. Firstly, OASIS is employed with the risk assessment procedure for the prediction of impending faults. As the OASIS model is adaptive, it copes with the initial fault symptoms and forecasts the future behavior of the process under faulty conditions reasonably well, thereby providing an early fault prediction. Next, the fault isolation step is immediately initiated using contribution plots to identify the faulty variables. Unlike in fault diagnosis, the problem of ambiguity in interpreting contribution results due to fault propagation is not an issue in fault prognosis, if the fault isolation step is implemented at an early stage of the fault before it affects the other variables. Hence, the contribution plots together with OASIS can proactively monitor the process in real-time. As a case study, we demonstrate OASIS-P for fault prognosis of a reactor–separator system.

A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise

In this paper, an adaptive identification scheme is proposed for nonlinear multi-input multi-output systems with colored noise based on a novel parameter update law. With the help of the hierarchical principle, the identification model is decomposed into three sub-models in which the computational burden is reduced. For each sub-model, the identification algorithm is proposed to estimate the sub-model parameters. In the process of the identification algorithm design, considering the system information corrupted by the noise, an adaptive filter gain is exploited to extract helpful identification data, in which a filter is designed using the system data instead of the independent design. Based on several auxiliary filtered variables, the estimation error data are obtained, and a new parameter adaptive law with a variable learning gain is proposed according to the estimation error data. Compared with the classic parameter update law, the parameter estimation update is derived based on the estimation error information instead of other error information, such as prediction error information. Under the persistent excitation condition, all the estimated parameters converge to the true parameters. an example is used and two experiments are conducted to test the outstanding identification performance of the proposed algorithm in terms of convergence rate and identification accuracy.

Parameter Identification From Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm.

Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is, therefore, fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation, and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics and address the challenges of limited measurement data and wide parameter ranges from the literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

Adaptive parameter identification of Bouc-wen hysteresis model for a vibration system using magnetorheological elastomer

The Bouc–wen hysteresis model has been widely used to design systems using magnetorheological (MR) materials. However, the model parameters cannot be perfectly determined due to the magnetic-field dependency and unmeasurable states. In this study, a novel adaptive parameter identification method is proposed for a model consisting of three components: an estimated model, a hysteresis observer, and adaptive algorithms. While the estimated model refers to the experimental data and adjusts the parameters directly to their actual values, the hysteresis observer is constructed to portray the unmeasurable hysteretic force that is based on the observer dynamic and misalignment. The proposed adaptive algorithms updated the parameters to actual values based on stability and adaptation theory. The Bouc–wen model, whose parameters were determined by the proposed method, captured the MR elastomer (MRE) characteristics well at different frequencies, amplitudes, and magnetic field strengths. The method was further studied to determine the parameters of a nonlinear vibration structure equipped with an MRE-based isolator. Asymptotic stability in response tracking and robustness in dynamic excitation were demonstrated using this theory. The method was characterized by fast convergence and simple calculations that facilitate the design of vibration control systems with uncertain parameters and hysteretic state. A comparison between the simulation and experimental results proved the effectiveness of the proposed method.

Phenotypic and Genetic Comparison of a Plant-Internalized and an Animal-Isolated Salmonella Choleraesuis Strain.

Contamination of fresh produce with human pathogens poses an important risk for consumers, especially after raw consumption. Moreover, if microorganisms are internalized, no removal by means of further hygienic measures would be possible. Human pathogenic bacteria identified in these food items are mostly of human or animal origin and an adaptation to this new niche and particularly for internalization would be presumed. This study compares a plant-internalized and an animal-borne Salmonella enterica subsp. enterica serovar Choleraesuis aiming at the identification of adaptation of the plant-internalized strain to its original environment. For this purpose, a phenotypical characterization by means of growth curves under conditions resembling the indigenous environment from the plant-internalized strain and further analyses using Pulsed-field gel electrophoresis and Matrix-assisted laser desorption ionization time of flight spectrometry were assessed. Furthermore, comparative genomic analyses by means of single nucleotide polymorphisms and identification of present/absent genes were performed. Although some phenotypical and genetic differences could be found, no signs of a specific adaptation for colonization and internalization in plants could be clearly identified. This could suggest that any Salmonella strain could directly settle in this niche without any evolutionary process being necessary. Further comparative analysis including internalized strains would be necessary to assess this question. However, these kinds of strains are not easily available.

Adaptive identification of lowpass filter cutoff frequency for online vessel model tuning

Tuning of vessel models in real-time based on vessel measurements and weather information is of great interest in order to increase the safety and efficiency of marine operations. Vessel motion signals usually contain high-frequency noise. For an unbiased model tuning algorithm, it is essential to filter the noisy signals in order to identify the power of the wave-induced first-order vessel response. Lowpass filters with high accuracy should therefore be applied. However, it is a challenge to design such a filter since the optimal cutoff frequency can vary with sea states, vessel dimensions, vessel conditions, etc. This paper proposes a novel algorithm to adaptively search for the optimal cutoff frequency for a lowpass filter with high accuracy. The algorithm is fundamentally based on the facts that the vessel naturally acts as a lowpass filter and the energy from the high-frequency components, e.g., signal noise, is significantly smaller than that from the wave-induced vessel response. The algorithm is validated by 500 numerically simulated vessel motion signals with quite high noise levels and also by analysis of several on-site full-scale vessel motion signals. The improvements to the tuning results for the vessel parameters are demonstrated.

Research of adaptive nonlinear model by integrated irrigation of water and soils in greenhouse for environment and ecological balance

In view of the high control cost and water consumption of the current commercial greenhouse automatic irrigation system, this paper presents an adaptive nonlinear model identification control algorithm based on the integrated irrigation of water, fertilizer and medicine in the greenhouse, and establishes an adaptive nonlinear model identification control model based on the integrated irrigation of water, fertilizer and medicine in the greenhouse: the adaptive blood equalizer theory is analyzed Based on the known theory, the LMS adaptive equalizer is simulated. The simulation results show that the denoising effect of the adaptive equalizer based on LMS algorithm is very significant, and the algorithm has small amount of calculation and is easy to be implemented in engineering. It has strong applicability. Under the premise of achieving effective greenhouse irrigation effect, the control system reduces the water consumption by 30%, and reduces the opening time of the irrigation system and the control cost.

Applying an Adaptive Signal Identification Method to Improve Vessel Echo Detection and Tracking for SeaSonde HF Radar

To enhance remote sensing for maritime safety and security, various sensors need to be integrated into a centralized maritime surveillance system (MSS). High-frequency (HF) radar systems are a type of mainstream technology widely used in international marine remote sensing and have great potential to detect distant sea surface targets due to their over-the-horizon (OTH) capability. However, effectively recognizing targets in spectra with intrinsic strong disturbance echoes and random environmental noise is still challenging. To avoid the above problem, this paper proposes an adaptive signal identification method to detect target signals based on a rapid and flexible threshold. By integrating a watershed segmentation algorithm, the subsequent direction result can be used to automatically compute the direction of arrival (DOA) of the targets. To assist in the orientation of the object, forward intersections are integrated with the technique. Hence, the proposed technique can effectively recognize vessel echoes with automatic identification system (AIS) verification. Experiments have demonstrated the promising feasibility of the proposed method’s performance.

The relation between psychological need satisfaction in acute psychiatric hospital and recovery among emerging adults: A self-determination theory approach.

Mental illness and addiction are prevalent during emerging adulthood and are associated with poorer functioning and quality of life. Research supports early intervention for enhancing recovery, though emerging adults frequently disengage from services, reducing effectiveness of early care. Research on self-determination theory shows healthcare climates that support psychological needs for autonomy, competence, and relatedness foster sustained engagement in treatment and health-promoting behaviors. The present study hypothesized that more need satisfying (and less frustrating) psychiatric hospital experiences would relate to various indicators of recovery among emerging adults. One-hundred four emerging adults from an acute psychiatric inpatient unit completed an adapted questionnaire measuring need satisfaction in the hospital and surveys of treatment attitudes, illness identity, and wellness during the 3rd week of their hospital stay. Six-month postdischarge service utilization data were obtained through electronic health databases. A more need satisfying hospital experience was associated with greater autonomous motivation for treatment, more positive attitudes toward medications and services, more adaptive illness identification, and greater well-being. Greater need frustration was related to less autonomous and more controlled motivation for treatment, less positive attitudes toward medications and services, less adaptive illness identification, and lower well-being. While neither need satisfaction nor frustration related to subsequent visits to the emergency department or rehospitalization, a need frustrating hospital experience related to fewer attended community appointments during the 6 months following discharge. Hospital experiences that satisfy (and do not frustrate) the psychological needs of emerging adults may promote recovery and treatment engagement. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event occurring in near future based on the current symptoms observed in a process. Such a proactive approach helps in taking an appropriate action beforehand so as to mitigate the impact a fault can have on a process. Recently, data-driven modeling techniques have been widely used due to an increased accessibility to process data. Though the offline trained models are successful in modeling complex dynamics, they have limited ability in capturing the dynamic process behavior, especially under abnormal conditions. To address this issue, we utilize an adaptive modeling technique called operable adaptive sparse identification of systems (OASIS) that can cope with any dynamical changes. Based on the forecasted process behavior using OASIS, we perform risk-assessment to predict faults and assess risk. In the proposed method, risk is used as a criteria to monitor and manage process operation.