System State Model Research Articles

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.

Analysis and design of different control strategies of hybrid active power filter based on the state model

A configuration consisting of a combination of a series active power filter (APF) and a parallel passive filter is shown to be an efficient system for non-linear load compensation. For this topology, different compensation strategies are proposed to control the series APF. The most effective strategies determine the APF compensation voltage in proportion to the source current harmonics. The proportionality constant ` k ' in the control algorithm implementation is related to the APF gain and complete system dynamic. In this study, the system state model is obtained for three control strategies of series APF: compensation voltage proportional to source current harmonics, compensation voltage opposite to the load voltage harmonics and a hybrid strategy which combines both previous strategies. The analyses of the resulting models provide the information needed to establish design criteria with respect to harmonic filtering and system stability. The methodology proposed in this study eliminates the arbitrary choice of the proportionality constant. The three strategies were analysed in two different situations: sinusoidal supply voltages and distorted supply voltages. Finally, results of an experimental prototype developed for this purpose allowed the proposed analysis to be verified.

Hybrid active power filter: design criteria

The configuration of a series active power filter (APF) and a parallel passive filter (PPF) has proven to be an efficient system for nonlinear load compensation. For this topology, different compensation strategies have been proposed to control the series APF. The most effective strategy determines the APF refrerence voltage as a proportion of the source current harmonics. The proportionality constant in the control algorithm implementation is related to the APF gain and the system dynamics. In this paper, the system state model has been obtained for three control strategies of series APF: voltage proportional to source current harmonics, voltage opposite to the load voltage harmonics and a hybrid strategy which combines both previous. The resulting model analyses provide the information needed to establish design criteria for each strategy, both in terms of harmonic filtering and the system stability. The three strategies were compared in two different situations: sinusoidal supply voltages and distorted supply voltages. Finally, results of an experimental prototype developed for this purpose allowed the proposed analysis to be verified.

State-Based Approach to System Testing

State and transition coverage during system testing is crucial to ensuring the reliability of software systems. However, the state diagrams of complete systems turn out to be so large and complex that they are seldom constructed by the developers. A state diagram of a system on the other hand is the primary artifact based on which test cases for achieving state and transition coverage could be designed. We therefore synthesize state diagrams of systems from other available UML models. Based on the synthesized system state models, we automatically generate test cases for transition path coverage. As there appears to be no published work on state-based system testing, we compare our work with traditional testing techniques. From our experiments, we find that our approach could uncover several faults that were not detected by traditional approaches.

Stiefel manifold particle filtering

In order to solve the problems of particle degeneration and lackness of diversity of particle filter, a new particle filter based on Stiefel manifold (SMPF) is proposed in this paper. In the SMPF the system model is based on Stiefel manifold, Langevin distribution is used as a prior density, the matrix normal distribution serves a as likelihood function, and particle is sampled on the manifold distribution. First, manifold is embedded in Euclidean space, then the mean of particles is calculated in Euclidean space and its result is projected back to embedded manifold. So the influence on variance of particle weight caused by statistic characteristics of noise is removed, and a kind of universal selecting scheme of important probability density function is acquired which is hardly restrained to system state model. The simulation results based on univariate nonstationary growth model nonlinear system indicate that the SMPF works much better than scentless particle filter in real-time performance, robustness, filtering precision and filtering efficiency.

Pervasive Model Adaptation: The Integration of Planning and Information Gathering in Dynamic Production Systems

Model-based planning often presumes a static system model, while in a practice physical system may evolve or drift over time. This paper proposes the idea of pervasive model adaptation in a production system, where the model is dynamically updated using observation of production output. The core idea is the interplay between model adaptation and production planning. We seek plans which simultaneously serve the goals of achieving high productivity for production, and information gathering for model adaptation. We use a modular printing example to illustrate issues such as formulation of the information criterion and search strategy for informative plans. The idea of pervasive adaptation can be further extended to improve long term productivity in production systems.

Progress and challenges in the automated construction of Markov state models for full protein systems

Markov state models (MSMs) are a powerful tool for modeling both the thermodynamics and kinetics of molecular systems. In addition, they provide a rigorous means to combine information from multiple sources into a single model and to direct future simulations/experiments to minimize uncertainties in the model. However, constructing MSMs is challenging because doing so requires decomposing the extremely high dimensional and rugged free energy landscape of a molecular system into long-lived states, also called metastable states. Thus, their application has generally required significant chemical intuition and hand-tuning. To address this limitation we have developed a toolkit for automating the construction of MSMs called MSMBUILDER (available at https://simtk.org/home/msmbuilder). In this work we demonstrate the application of MSMBUILDER to the villin headpiece (HP-35 NleNle), one of the smallest and fastest folding proteins. We show that the resulting MSM captures both the thermodynamics and kinetics of the original molecular dynamics of the system. As a first step toward experimental validation of our methodology we show that our model provides accurate structure prediction and that the longest timescale events correspond to folding.

MPC of Switching in a Boost Converter Using a Hybrid State Model With a Sliding Mode Observer

In this paper, a model of a DC-DC (boost) converter is first expressed as a hybrid/switched/variable-structure system state model for the purpose of applying recently developed hybrid optimal control theory to control switching in a boost converter. Switching control is achieved by forming the embedded form of the hybrid state model, which enables the derivation of a control that solves for the switching function that minimizes a user-defined performance index. This approach eliminates the need to form average-value models and provides flexibility to balance competing objectives through appropriate weighting of individual terms in the performance index. Since, in practical situations, both the source voltage and the load resistance vary with time in unknown and unmeasurable ways, we introduce a sliding mode observer based on an enlarged state model which allows implicit estimation of the unknown variables. The combined optimal switching control and sliding mode observer are applied to a boost converter in which several nonidealities and losses are represented. The results of time-domain simulation and hardware experiments are used to validate and compare the response of the hybrid optimal control-sliding mode observer to that of a traditional current-mode control strategy.

Automatic generation of test specifications for coverage of system state transitions

Adequate system testing of present day application programs requires satisfactory coverage of system states and transitions. This can be achieved by using a system state model. However, the system state models are rarely constructed by system developers, as these are large and complex. The only state models that are constructed by the developers are those of individual objects. However test case generation for state-based system testing based on traversal of statecharts of individual objects appears to be infeasible, since system test cases would have to be specified in the form of scenario sequences rather than transitions on individual object statecharts. In this paper, we propose a novel approach to coverage of elementary transition paths of an automatically synthesized system state model. Our technique for coverage of elementary transition paths would also ensure coverage of all states and transitions of the system model.

Control of series active power filter by state feedback

The active power filters in series connection or series APF, are static compensation systems based on an electronic converter PWM (Pulse Width Modulation). The systematic use of series APFs in the power systems allows the elimination of the harmonics caused by specific loads named voltage source harmonic loads. To obtain this compensation objective, several strategies have been proposed up now. Nevertheless, each strategy development has been based on the equivalent electrical circuit and on the results obtained in simulation and/or experimental tests. Generally, the control structure formal analysis is not carried out. In this work, the whole system state model has been obtained. Besides, the system behavior has been analyzed from the state equations for each compensation strategy. As a consequence, the analysis has allowed the establishment of design rules respect to the resultant control strategy. Finally, a practical case is presented, whose results illustrate the proposed method.

System state coverage through automatic test case generation

Achieving reasonable coverage of system states during testing is a non-trivial problem, since the number of system states for practical systems is usually very large. To further complicate the problem, system developers often do not construct a system state model. On the other hand, many modern systems are extremely complex and state-based, making it necessary to ensure satisfactory state coverage during system testing. In this paper, we propose a method to automatically generate system test cases to achieve coverage of system states based on Unified Modeling Language (UML) models constructed during a normal development process. We use UML use case, sequence and statechart models to generate a set of sequences of scenarios that can achieve adequate coverage of system states.

Synthesis of system state models

Many modern systems are state-based. For such systems, a system state model is important not only for understanding the behavior of the system, but also for test case design, test coverage analysis, maintenance, etc. However, developers rarely construct the system state model for practical systems because it is usually too complex and cumbersome to construct. On the other hand, developers normally construct the state models of individual classes. We propose a novel method to automatically synthesize the state model of a system by analyzing the different sequences of scenarios and determining whether these lead to any state changes of the individual objects.

Bayesian analysis of some static system models

This study deals with the Bayesian analysis of the reliability characteristics of some static system models. Recognizing the fact that overestimation (underestimation) of reliability function or mean time to system failure (MTSF), hazard rate is more serious than that resulting due to its underestimation (overestimation), an asymmetric loss function, called the linear exponential (LINEX), has been used in the analysis. The results are also given for the squared error loss function (SELF).

Indistinguishable states IIThe imperfect model scenario

Given a perfect model of a chaotic system and a set of noisy observations of arbitrary duration, it is not possible to determine the state of this system precisely, rather one must consider a set of states which are indistinguishable from one another given the observations [K. Judd, L.A. Smith, Indistinguishable states I, Physica D 151 (2001)]. Yet the perfect model scenario is a fiction; in practice all models are imperfect. How do the results from the perfect model scenario change under imperfect models? It is shown to be essential to take even small model imperfections into account: failure to do so can systematically degrade state estimation or prediction of nonlinear systems. With an imperfect model, the system state space and model state space are rarely (if ever) equivalent, and so one must consider a projection of the system state into the model state space. Furthermore, it is almost certain that no trajectory of the model is consistent with an infinite series of observations, thus there is no consistent way to estimate the projection of system state using trajectories. There are pseudo-orbits, however, that are consistent with observations and these can be used to estimate the projection of the system state. Using pseudo-orbits one finds that, as in the perfect model scenario, there is a set of states that are indistinguishable from the projection of the system state. Estimation of this set of indistinguishable states and the probability density on these states is discussed. The main conclusions are (i) that there is no state of the model that can be identified with the state of the system; and (ii) that great care must be taken when using an imperfect model to forecast the system, because the initialization of the model state from observations can provide a poor analogue for the system. The forecast may not shadow the future behaviour of the system for very long, even if one were able to obtain a noise-free projection of the system state. The ultimate aims of probability forecasts should be re-examined in light of these results.

Indistinguishable states II: The imperfect model scenario

Given a perfect model of a chaotic system and a set of noisy observations of arbitrary duration, it is not possible to determine the state of this system precisely, rather one must consider a set of states which are indistinguishable from one another given the observations [K. Judd, L.A. Smith, Indistinguishable states I, Physica D 151 (2001)]. Yet the perfect model scenario is a fiction; in practice all models are imperfect. How do the results from the perfect model scenario change under imperfect models? It is shown to be essential to take even small model imperfections into account: failure to do so can systematically degrade state estimation or prediction of nonlinear systems. With an imperfect model, the system state space and model state space are rarely (if ever) equivalent, and so one must consider a projection of the system state into the model state space. Furthermore, it is almost certain that no trajectory of the model is consistent with an infinite series of observations, thus there is no consistent way to estimate the projection of system state using trajectories. There are pseudo-orbits, however, that are consistent with observations and these can be used to estimate the projection of the system state. Using pseudo-orbits one finds that, as in the perfect model scenario, there is a set of states that are indistinguishable from the projection of the system state. Estimation of this set of indistinguishable states and the probability density on these states is discussed. The main conclusions are (i) that there is no state of the model that can be identified with the state of the system; and (ii) that great care must be taken when using an imperfect model to forecast the system, because the initialization of the model state from observations can provide a poor analogue for the system. The forecast may not shadow the future behaviour of the system for very long, even if one were able to obtain a noise-free projection of the system state. The ultimate aims of probability forecasts should be re-examined in light of these results.

Electromagnetic field and force analyses of a noncontacting conveyance system for steel mill application

Based on the design concepts of linear induction motors, an electromagnetic device has been constructed to investigate its feasibility for noncontacting steel-plate conveyance system applications. The system is intended to provide the desired lift, propulsive, and guidance forces to the steel plate simultaneously. Three-dimensional (3-D) field distributions and force characteristics have been investigated through a system state model and finite element analysis. Satisfactory results in the system's intended 3-D directions are provided to verify the suitability of the design objective.

Experimental results for multichannel feedforward ANC with noninvasive system identification

This paper presents experimental validation of a class of algorithms designed to enable active noise control (ANC) to function in environments when transfer functions change significantly over time. The experimental results presented are for broadband, local quieting in a diffuse field using a multichannel ANC system. The reverberant enclosure is an ordinary room, measuring approximately 1.4 x 2.4 x 2.4 m3 and containing a seated occupant, with six microphones defining the quiet zone near the occupant's ears. The control system uses a single reference signal and two error channels to drive four secondary sources. Using an ideal reference sensor, reduction in sound pressure level is obtained at the quiet-zone microphones averaged over the frequency range 50 to 1000 Hz with an occupant seated in the room. Two main results are presented: first for an adaptive cancelling algorithm that uses static system models, and second for the same algorithm joined with a noninvasive real-time system identification algorithm. In the first case better than 23 dB of performance is obtained if the occupant remains still through calibration and testing. In the second case, approximately 18 dB is obtained at the error microphones regardless of the motion of the occupant.

Interspecies scaling of the bioaccumulation of lipophilic xenobiotics in fish: An example using trifluralin

AbstractA poorly understood factor that may influence differences in the accumulation of a xenobiotic among fishes is interspecies differences in physiology. We have extensively studied the uptake, distribution, and excretion kinetics of the lipophilic herbicide trifluralin (TF) in fish, using a static water exposure system and compartmental toxicokinetic models. We obtained quantitative estimates of physiologically based toxicokinetic parameters such as uptake clearance, apparent volume of distribution, and elimination clearance due to xenobiotic metabolism, in rainbow trout, channel catfish, and bluegill sunfish at two acclimation temperatures. In these and other species (largemouth bass, gizzard shad, fathead minnows, and lake sturgeon), oxygen consumption rate, total lipid content, plasma protein binding, and in vitro biotransformation rates from liver homogenates were determined and examined for their capacities to predict toxicokinetic parameter values. The uptake clearance of TF was predictable based on the oxygen consumption rate, and in vitro TF biotransformation rate was a useful predictor of the in vivo metabolic clearance of TF. Lipid content, however, did not predict the apparent volume of distribution of TF. Values of uptake and metabolism clearance were predicted in largemouth bass, gizzard shad, fathead minnows, and lake sturgeon, using the oxygen consumption and the in vitro TF biotransformation rates. These predicted parameters were then used to successfully simulate the toxicokinetics of TF in these species.

Causal reasoning in econometric models

Propagation of change based on causal ordering is a central element of causal reasoning in economic models. While causal reasoning has most often been applied in qualitative models, we demonstrate a technique for causal reasoning that offers explanations of structure and behaviour in quantitative, econometric contexts. Given a matching of equations with endogenous variables, causal reasoning can be applied to both static and dynamic system models. By propagating the disturbance of one or more exogenous variables, impact or static multipliers of the model can be derived along with a causal explanation. Dynamic analysis is achieved by propagation of lagged endogenous variables carried from the previous time periods. Two versions of Keynesian macro-econometric models, Klein's Model I, and the Klein-Goldberger Model are used as examples.

On state models of linear time-variant discrete-time systems

A decoupled full-dimension form is introduced for state models. In this form, the dimension of the state vector is time-invariant, which is equal to the dimension of the state-space at each time instant. Therefore, the dimension of the state vector is minimized from the time-variant point of view and all the state variables are independent. Moreover, the state vector in a state model of the decoupled full-dimension form is partitioned into two decoupled subvectors, each of which alone forms a subsystem. One of the subsystems is an FIR system whose state vector is dependent only on inputs at (finitely) past instants. In addition, this subsystem is completely reachable. The other subsystem is an IIR system whose state-space is equal to the force-free solution set of the state vector of the subsystem. It is proved that every state model in the conventional form can be mapped by a linear one-to-one map into an equivalent state model in the decoupled full-dimension form. In comparison with a state model in the...