Time-variant Components Research Articles

A new approach for reliability analysis with time-variant performance characteristics

Reliability represents safety level in industry practice and may variant due to time-variant operation condition and components deterioration throughout a product life-cycle. Thus, the capability to perform time-variant reliability analysis is of vital importance in practical engineering applications. This paper presents a new approach, referred to as nested extreme response surface (NERS), that can efficiently tackle time dependency issue in time-variant reliability analysis and enable to solve such problem by easily integrating with advanced time-independent tools. The key of the NERS approach is to build a nested response surface of time corresponding to the extreme value of the limit state function by employing Kriging model. To obtain the data for the Kriging model, the efficient global optimization technique is integrated with the NERS to extract the extreme time responses of the limit state function for any given system input. An adaptive response prediction and model maturation mechanism is developed based on mean square error (MSE) to concurrently improve the accuracy and computational efficiency of the proposed approach. With the nested response surface of time, the time-variant reliability analysis can be converted into the time-independent reliability analysis and existing advanced reliability analysis methods can be used. Three case studies are used to demonstrate the efficiency and accuracy of NERS approach.

A Nested Extreme Response Surface Approach for Time-Dependent Reliability-Based Design Optimization

A primary concern in practical engineering design is ensuring high system reliability throughout a product's lifecycle, which is subject to time-variant operating conditions and component deteriorations. Thus, the capability of dealing with time-dependent probabilistic constraints in reliability-based design optimization (RBDO) is of vital importance in practical engineering design applications. This paper presents a nested extreme response surface (NERS) approach to efficiently carry out time-dependent reliability analysis and determine the optimal designs. This approach employs the kriging model to build a nested response surface of time corresponding to the extreme value of the limit state function. The efficient global optimization (EGO) technique is integrated with the NERS approach to extract the extreme time responses of the limit state function for any given system design. An adaptive response prediction and model maturation (ARPMM) mechanism is developed based on the mean square error (MSE) to concurrently improve the accuracy and computational efficiency of the proposed approach. With the nested response surface of time, the time-dependent reliability analysis can be converted into the time-independent reliability analysis, and existing advanced reliability analysis and design methods can be used. The NERS approach is compared with existing time-dependent reliability analysis approaches and integrated with RBDO for engineered system design with time-dependent probabilistic constraints. Two case studies are used to demonstrate the efficacy of the proposed NERS approach.

A Comprehensive Approach for Thermal Error Model Optimization for ANN-Based Real-Time Error Compensation in CNC Machine Tools

Thermally induced errors play a critical role in the control of machining accuracy. They can account for as much as 70% of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design phase, errors compensation appears to be the most economical solution. Accurate and efficient modeling of the thermally induced errors is an essential part of the error compensation process. This paper presents a comprehensive approach for thermal error modeling optimization. The proposed optimization method is based on multiple temperature measurements, Taguchi’s orthogonal arrays, various statistical tools and artificial neural networks to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The proposed approach can be effectively and advantageously used for real-time thermal error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed optimization method and show that the resultant model can accurately predict the time-variant thermal error components under various operating conditions.

Comment on “A random kinetic energy model for rock avalanches: Eight case studies” by T. Preuth et al.

Comment on “A random kinetic energy model for rock avalanches: Eight case studies” by T. Preuth et al.

Groundwater balance estimation in karst by using a conceptual rainfall–runoff model

A conceptual rainfall–runoff model is proposed for the estimation of groundwater balance components including the influences of time-variant catchment boundaries and intercatchment groundwater flows. Karst underground is considered as a lumped system that contains: (1) soil cover, (2) epikarst zone and (3) vadose and phreatic zones. The soil cover receives an average rainfall in catchment as the input into the system. The epikarst zone is composed of a production store and a routing store. The production store contributes to the loss of water in process of evapotranspiration. The routing store contributes to the retention of percolated water and the lateral distribution of groundwater recharge between internal runoff and diffuse infiltration. The vadose and phreatic zones accumulate groundwater in fissures, fractures and vertical shafts, and produce the vadose seepage, vadose flow and shaft flow components of the karst spring discharge. The parameter estimation and calculation procedure assemble the moisture balance and the groundwater-balance approaches. The rainfall–runoff model is divided in two sub-models. The sub-model based on the moisture balance of soil cover and epikarst production store calculates effective rainfalls. The sub-model based on the groundwater balance of vadose and phreatic zone calculates groundwater recharges. The difference between the effective rainfalls and the groundwater recharges represents the contribution of epikarst zone and non-conservative and time-variant components to the groundwater balance. The proposed methodology is applied to the Jadro Spring located near the city of Split in Croatia. The calculated groundwater balance shows that the Jadro Spring aquifer contains a significant storage capacity in the vadose and phreatic zones. During the year, the aquifer may accumulate up to 140 millions m3. The variability of calculated catchment area is explained with the time-variant catchment boundary dependent on groundwater levels and the intercatchment groundwater flows from neighboring catchments. The average catchment area of 396 km2 is estimated by using the average monthly effective rainfalls and the average monthly groundwater recharges.

Online identification and nonlinear control of the electrically stimulated quadriceps muscle

A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth—active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure.

Repetitive Peripheral Magnetic Stimulation (RPMS) for Rehabilitation of Central Paresis – Clinical Experimental Investigations and Technical Approach

Concept of Repetitive Peripheral Magnetic Stimulation (RPMS) is based on the activation of a reorganization process in the CNS (neuromodulation) by induction of a proprioceptive inflow to the CNS, which corresponds physiologically to the lost inflow during active movements. For the induction of this inflow, we use RPMS which depolarizes thick myelinated nerve fibers of the terminal sensorimotor branches. In contrast to fES, the magnetic field penetrates to deeper regions of the muscle, whereas the current caused by the electrical field will take the way of lowest resistance, thus being fairly limited spatially on the surface. Various components of an improvement due to RPMS are clinically and experimentally investigated: 1) Spasticity independent of the level of origin can always be suppressed by RPMS. In a clinical experimental investigation with spastic paresis of finger and hand extensors a dramatic decrease of spasticity together with an increase of voluntary movement could be demonstrated. 2) In a PET study of eight patients it was shown that, due to RPMS, areas of the fronto-parietal circuits which are involved in goal-directed controlled movements, are activated. 3) In disturbed goal-directed motor performances such as reaching and grasping, the regularity of the performed trajectory could be improved. 4) In patients with local tactile extinction (neglect), RPMS reduces the recognition error of different local tactile stimuli. Also the spatial cognition (position sense) can be improved by means of RPMS. We may assume that these various and long-lasting effects on the spinal and cortical levels are based on neuromodulation which is caused by muscle mechanoreceptor afferents. The development of a non-linear adaptive closed-loop control to induce coordinated finger movements by RPMS is the overall target in order to increase the therapeutic outcome. For this purpose our physiological findings from RPMS form the basis to realize the dominant and time-variant components (i.e., recruitment, spasticity, muscle fatigue and rehabilitation progress). These components have to be identified and compensated for by closed-loop control.

Online identification of the electrically stimulated quadriceps muscle group

In this paper, a new approach for estimating a nonlinear model of the electrically stimulated quadriceps muscle group under non-isometric conditions is investigated. In order to identify the muscle dynamics (stimulation pulse width-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shankquadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM). and passive joint properties form the time-invariant part. The actuator. i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time non linear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously.

Office Capitalization Rates: Real Estate and Capital Market Influences

Focusing on the office capitalization rate, the central objective of this article is to shed light on two sets of issues that have not yet been fully researched. The first involves the importance of local-fixed and time-variant components of the office capitalization rate, and the significance of persistence in its time trends. The second centers on the relative importance of local office markets and the national capital market in shaping the various components of the office capitalization rate. Extensive econometric analysis of capitalization-rate series employed to address these issues highlights the existence of both local fixed and time-variant effects, while indicating differential persistence of time trends across markets. Moreover, such analysis uncovers the pivotal role of specific local office-market traits and the lesser role of national capital-market features in determining office capitalization-rate variations. Such findings on the role of local influences may provide a richer foundation for asset valuation and investment practices.

Dynamics of an Unbalanced Shaft Interacting with a Limited Power Supply

Rotation of an elastically mounted unbalanced shaft may be in general affected by its lateral vibration, due to a “vibrational torque”. This interaction is nonlinear, and can be neglected only in case of an unlimited power supply. Whenever the available power of the drive is comparable with power consumption due to vibration, various nonlinear phenomena may be observed, the most well-known of these being the so called Sommerfeld effect – slowing down or complete capture of the shaft at resonance. The corresponding steady-state motions and their stability can be studied by asymptotic methods, as applied to the governing nonlinear set of two second-order equations. However, study of transient motions in general requires numerical solution. This numerical solution is obtained here, and extensive parametric studies are performed of the Sommerfeld effect. In particular, the influence is evaluated of damping ratio and slope of the torque-speed curve of the drive on a passage/capture threshold. The results of numerical simulation, as well as experiments with a physical model, also demonstrate the effect of smooth passage through resonance with a limited power supply, based on using a “switch” of suspension stiffness from a certain artificially increased value to the design one. A brief description is presented also of time-variant components of the resonant amplitude and rotational frequency responses in the case of capture, as observed both in numerical simulation studies and in experiments. Whilst these components are small compared with the corresponding constant ones, i.e. steady-state vibration amplitude and rotational frequency, the above observations indicate the possibility for periodic or chaotic nonstationarity in the system's response.

Evozierte Kohärenz des EEG: II. Kommunikation der Hirnareale und Verarbeitungstiefe

The in part one [42] introduced method of evoked coherences was used in search for independent signs of the levels of processing effects on memory performance. 9 subjects in a visual pilot study and 16 subjects in the acoustic main experiments were presented 30 words each in a perceptive, semantic and metacognitive orientation condition. A 16-channel-EEG was recorded and stimulus-related time-variant spectral components (δ, ϑ, α, β) were calculated for analysis of evoked coherence. In order to differentiate between task processing and general functional state of the brain in the main study one series of experimental conditions was carried out in the state of deep relaxation (trance). The evoked coherences were tested with multi-factorial ANOVA and contrasted by Sche/fe-test. As expected, the variation in the state of the consciousness was related with strong but diffuse changes in power and coherence spectra. In contrast to this, differences in level of processing were accompanied by specific focal patterns of evoked coherence with nearly no changes in power. Especially in the lower frequency bands there appeared differentiating coherences between posterior regions and single points of the frontal and left-temporal regions of the cortex. The data also supported the interpretation that „deeper processing” is characterized by a functional de-coupling of activity in progressively more anteriory located regions and within lower frequency band, particularly 5.

Thermal error modelling for real-time error compensation

A modelling strategy for the prediction of both the scalar and the position-dependent thermal error components is presented. Two types of empirical modelling method based on the multiple regression analysis (MRA) and the artificial neural network (ANN) have been proposed for the real-time prediction of thermal errors with multiple temperature measurements. Both approaches have a systematic and computerised algorithm to search automatically for the nonlinear and interaction terms between different temperature variables. The experimental results on a machining centre show that both the MRA and the ANN can accurately predict the time-variant thermal error components under different spindle speeds and temperature fields. The accuracy of a horizontal machining centre can be improved through experiment by a factor of ten and the errors of a cut aluminium workpiece owing to thermal distortion have been reduced from 92.4 µm to 7.2 µm in the lateral direction. The depth difference due to the spindle thermal growth has been reduced from 196 µm to 8 µm.

Introduction of time variance to linear conceptual catchment models

Most linear conceptual models relating rainfall and runoff are time invariant and are based on two simple components, the linear channel and the linear reservoir. Time variant versions of these two components are defined, and expressions are obtained for their impulse responses. The convolution integral is used both to study different combinations of the two basic time variant components and to obtain the response of any time variant linear system to a given input. The theoretical background of more advanced models is discussed and it is suggested that time variance may be introduced into any linear model founded on these two conceptual components.