Search

A dynamic process model based on a base metal refinery including critical control layers has been developed. The critical control layers include sensors, actuators, regulatory controllers, alarm systems, safety interlocks and supervisory control. With the help of expert knowledge, a fault (abnormal event) library was incorporated into the dynamic model. Fault diagnosis methods are used to detect and identify abnormal process conditions. With the use of the dynamic process model, fault detection and identification methods can be more rigorously and accurately evaluated for hydrometallurgical industry use. An economic performance function is developed from expert knowledge. Once the fault diagnosis is complete, an economic impact analysis based on the fault diagnosis results is completed. A possible economic case for fault diagnosis is concluded from the results.

This paper studies a fully distributed optimal formation flying problem for a multi-satellite system with a chief satellite and some deputy satellites, in order to seek the optimal formation via si...

A new reliability analysis method for tunnels was developed by integrating an adaptive Radial Basis Function (RBF) metamodeling technique with a First-Order Reliability Method (FORM). In each iteration of the adaptive RBF (A-RBF) technique, an explicit metamodel of the performance function was constructed using an augmented RBF, before an alternate FORM was applied. Additional sample points were then added in the neighborhood of the design point in order to improve the accuracy of the metamodel. Three examples were solved and the proposed approach worked well. This work provides an efficient and effective reliability analysis method for practical tunnel engineering problems.

Based on reliability theory, the value of the standard normal distribution integral can be obtained by calculating the probability of the failure domain of the linear performance function. After th...

Accurate determination of the failure probability of oil and gas pipeline is very important in integrity assessment and work conditions of such structure. In this paper, the failure probability of the corroded pipelines which is made by X60 steel grade is evaluated. The burst corroded performance function is developed using the M5 model tree for this complex real engineering failure problem. The structural reliability of a the pressurized gas pipeline containing external corrosion defects has been evaluated using hybrid reliability method combined by the M5 model tree and Monte Carlo simulation. The results indicated that increasing the defects depth are strongly reduced the safety levels of this problem.

According to the study made by United Nation Economic Commission for Africa, Ethiopia stands as one of the worst countries with respect to road safety performance in terms of traffic accident fatalities per 10,000 vehicles (i.e. 95 in 2007/8). Road safety generally depends on humans, vehicles, and highway conditions. These factors influence road safety separately or in combination. One of the basic means to improve road safety is to reduce hazardous conditions of roads. The main objective of this study is to identify and rank hazardous locations and propose appropriate simple and inexpensive countermeasures along Hawassa-Shashemene-Bulbula main two-lane rural road. Accordingly, the road and traffic data were collected from field investigation and Ethiopian Road Authority and accident data were gathered from police stations. Then, the study road equally divided into short sections of 1.5 km and traffic volume and accident frequencies assigned for each road site to predict theoretical frequencies of accident. Empirical Bayes method and Safety Performance Function have been used to estimate an index known as Potential for Safety Improvement (PSI) for each site of the study area to identify and rank road sites. The result showed that out of 43 road segments 22 of them were identified as dangerous road segments. Moreover, based on further criterion established for screening the ranked road sections 8 road segments were found the most dangerous road segments as they have contributed 76% of total PSI values. The degree of haphazardness of a given road segment in the study area has directly associated with the availability of risk indicating road and traffic factors. Finally, it recommends that regulatory body of road safety in the study area should give high priority and immediate response for the improvement of most dangerous road segments.

This paper proposes a sampled-data $H_\infty $ fuzzy observer technique for nonlinear uncertain oscillating systems that are represented by the Takagi–Sugeno fuzzy model. The observers are designed for two cases: a measurable premise variable and an immeasurable premise variable. The error system between the nonlinear uncertain system and the sampled-data observer is constructed. The $H_\infty $ performance function for the oscillating system is defined and is guaranteed in the Lyapunov sense. Sufficient conditions for the $H_\infty $ sampled-data fuzzy observer are given in terms of linear matrix inequalities. Finally, the feasibility of the proposed technique is shown using two simulation examples.

Under the condition of fuzzy inputs, the structure safety level is generally measured by the failure possibility. Differing from the level cut optimization method and the fuzzy simulation method, a novel method for estimating the failure possibility is proposed in this paper by combining the adaptive Kriging surrogate model with the Markov chain simulation. Based on the principle of fuzzy simulation, the fuzzy design point is defined as the failure point with the maximal joint membership function in the fuzzy simulation sample pool, and the problem of estimating the failure possibility can be transformed into that of searching the fuzzy design point. Then the Markov chain simulation which can quickly simulate the samples in the interest region is employed to search the fuzzy design point by a designed transfer criteria. To efficiently recognize the candidate states of the Markov chain, the Kriging surrogate model is adaptively trained in the candidate sample pool generated by the Markov chain states. By replacing the actual performance function with the convergent Kriging model, the efficiency of Markov chain simulation can be improved greatly. The presented examples are used to prove the rationality and high efficiency of the proposed method.

The first–order reliability method (FORM) is widely used in structural reliability analysis for its simplicity and efficiency. It can be solved by gradient–based algorithms, on which the nonlinear degree of performance functions may have a great influence. On the other hand, evolutionary algorithms could achieve convergence solutions even for highly nonlinear performance function, usually with expensive computational cost. To overcome their drawbacks, we propose a new reliability analysis method called PMA–IACC to search the most probable failure point (MPP). As the inverse reliability analysis and reliability analysis are reversible each other, the performance measure approach (PMA) of the inverse reliability analysis could be used for the reliability analysis based on the multi–objective optimization theory. To enhance the efficiency and robustness of the inverse reliability, the improved adaptive chaos control (IACC) method is proposed and then it is integrated into the PMA reliability analysis strategy. Five illustrative examples, including three two–dimensional problems and two multi–dimensional problems, demonstrate the outstanding efficiency and robustness of the PMA–IACC over other prevalent approaches.

The paper presents a High order stochastic response surface method (HOSRSM) for reliability and reliability-based sensitivity analyses of transmission mechanism of a shearer in heavy and impulsive environments. Dynamic analysis is affected by several sources of uncertainty, and it is important to quantify their combined effect on performance of the mechanism in order to aid risk assessment, risk mitigation, and decision-making. A dynamic transmission model of the traction unit is established under the condition of low speed and impulsive load. A performance function is proposed to evaluate the system transmission accuracy. The effects of basic variables in HOSRSM are investigated on calculation efficiency and computation cost of system precision reliability. The results indicate that structure parameters of a sun gear are more sensitive than others. The tooth width is a more sensitive geometric parameter than the diameter of the center hole. The targets are proposed to manufacture and inspect the sun gear in traction unit for safety.

We show that the left-monotone martingale coupling is optimal for any given performance function satisfying the martingale version of the Spence–Mirrlees condition, without assuming additional structural conditions on the marginals. We also give a new interpretation of the left monotone coupling in terms of Skorokhod embedding which allows us to give a short proof of uniqueness.

The time-variant system reliability analysis is a significant topic in the field of reliability engineering. Although progress has been made, there remain multiple challenges in the existing methods including the explosive number of possible failure modes, requiring correlation information, and excessive computational efforts. In the present work, an improved moment method with high accuracy, efficiency and robustness is proposed for system reliability analysis of deteriorating structures. First, by introducing the complete system failure process, an equivalent time dependent performance function describing a deteriorating structural system is obtained. Second, a point estimate method based on the adaptive trivariate dimensional decomposition is adopted to calculate the first six moments of the performance function. And third, a saddlepoint approximation involving the first six moments is developed to estimate the system failure probability. Several examples are investigated to verify the accuracy, efficiency and stability of the proposed method.

Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However, the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.

Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on Monte Carlo simulation (MCS) for complex performance functions. M5 model tree (M5Tree) model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the MCS and M5Tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of M5Tree meta-model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based MCS and M5Tree improved the computational efforts for evaluating the performance function in reliability analysis. The M5Tree significantly increased the efficiency of reliability analysis with accurate failure probability.

Consider a system composed of several units. The performance of each unit can be affected by providing a portion of a limited amount of costly resources available. An allocation of resources to a unit results in a unit's response that depends on the level of resources allocated to it and some of its random parameters. In this paper we consider cases where each unit has one or two random parameters. The overall performance of the system is mapped by a function on the vector of responses generated by all the units in the system. Our interest is in identifying the conditions on the response function of the units, the system performance function and the random parameters under which the random system performance as a function of the resource allocation has stochastic arrangement increasing property. This allows one to substantially reduce the number of allocation that needs to be searched to identify an optimal allocation that maximizes the expected utility derived from the system response as a result of the resource allocation.

In this presentation, we propose to use the expertise gained in the cluster community in unbiased optimization to identify materials with optimal properties. Specifically, we present an inverse-design method that automatically identifies atoms or functional groups to be placed at pre-defined positions. Which groups are placed at which positions is identified automatically by optimizing a so called performance function. In the present work, we study, as a simple example, mixed Si-Ge clusters whereby their performance in solar-energy harvesting is optimized. We demonstrate that the proposed method is capable of identifying a larger number of very good candidate structures, although there are cases where not the best (but ‘only’ the 2nd best) structure is identified. It is also demonstrated that it can be difficult to predict the optimal system based on simple chemical and/or physical ideas. Finally, extensions and challenges of the present approach are being briefly discussed.

In this paper, a robust tracking control scheme is proposed for wheeled mobile robots with skidding, slipping, and input disturbance. Considering the existing skidding and slipping, a desired disturbance-observer-based virtual velocity control law is first designed. Then, the robust tracking control scheme is developed by considering the prescribed tracking performance requirement and using the disturbance observer. In the tracking control scheme design, the prescribed performance function method is employed to guarantee the desired tracking performance. To handle the skidding, slipping, and input disturbance, the disturbance observer is developed in the control scheme design. Experiment results demonstrate the effectiveness of the proposed tracking control scheme for wheeled mobile robots with skidding, slipping, and input disturbance.

Probability of failure sensitivity analysis is a subject of major interest in uncertainty based optimization. However, the computational effort required to obtain accurate results is frequently very high. In this work we present a novel strategy for probability of failure sensitivity analysis, that is based on polynomial expansions of both the performance function and its derivatives. Sensitivity analysis is then made using the obtained polynomial expansions together with standard expressions. Since the simulation step requires only evaluation of closed form polynomials, very large samples can be used to obtain accurate results with small computational effort. The proposed approach is expected to be efficient when polynomial expansion methods are suitable. Four numerical examples are presented in order to show the effectiveness of the proposed approach.

This paper proposes a new terminal homing guidance law with line-of-sight angle constraint for a missile to intercept a noncooperative maneuvering target by integrating prescribed performance control with sliding mode control. The newly proposed guidance law is derived analytically with a prescribed performance controller in combination with the continuous nonsingular terminal sliding mode manifold and the disturbance observer. The prescribed performance controller is designed with a new prescribed performance function to avoid undesired abrupt change in the derivative of prescribed performance variable. In addition to its robustness and preciseness, this new homing guidance law is able to attenuate a largely abrupt change in guidance commands due to driving the sliding mode variable whose initial derivative is close to zero to a desired residual set at the beginning of the terminal homing phase. Furthermore, the time-varying control gain of the new guidance law is analytically determined by terminal tolerance. Theoretical analysis and numerical simulations are conducted to demonstrate the effectiveness of this new guidance law.

To obtain concrete results, we will consider a case of dynamical systems with input/output mechanism.

This paper presents robust design optimization (RDO) of a reinforced concrete (RC) frame subjected to stochastic blast-induced ground motion (BIGM). As a full simulation approach requires extensive computational time to solve such problem, the polynomial response surface method (RSM) has been applied in the present study to alleviate the associated computational burden. The least squares method (LSM) generally adopted in the conventional RSM is often reported to be a source of error in optimization. Hence, a moving least squares method (MLSM)-based adaptive RSM is explored in the RDO. Random blast load is modelled by spectral representation method. The record-to-record variation of BIGM time history is captured by applying dual RSM. The optimization problem has been developed as a cost-minimization problem subjected to the displacement constraints. The RDO is formulated by simultaneously optimizing the expected value and the variation of the performance function by using weighted sum method. The robustness in the constraint is ensured by limiting the probability of failure of limit state function. The results show that the proposed MLSM-based RDO strategy yields more accurate solutions than the conventional LSM-based RSM taking full simulation approach as reference. At the same time, the present procedure yields Pareto-front in significantly lesser computational time than the full-simulation-based RDO approach.

In order to solve the problem of multi-sensor multi-objective configuration, a management algorithm based on Simulated Annealing Genetic Algorithm (SA-GA) is proposed. Firstly, Analytic Hierarchy Process (AHP) is used to quantify the sensor’s spatial information perception ability. Secondly, target’s priority is calculated. Finally, SA-GA is used to allocate sensor resources. It can be seen from the simulation results that this algorithm achieves the maximum performance function in solving the multi-objective distribution. And it has certain engineering significance.

BACKGROUND The purpose of this study was to examine the use of observational learning among athletes when they observed themselves (self-model) or their opponents (opponent-model). Furthermore, there was an emphasis on determining the relationships between age, experience, and the functions (i.e., skill, strategy, performance) of observational learning according to the type of model observed. METHODS A total of 158 athletes (male=70, female=88) who competed in different team (N.=83) and individual (N.=75) sports participated in the study. They ranged in age from 17 years to 32 years. Their total years of sport participation ranged from one year to 18 years. RESULTS Athletes reported significantly greater use of all three functions of observational learning for the self-model than for the opponent-model. Bivariate correlations revealed years of experience was significantly and positively associated with the skill, self, and performance functions of observational learning for the self-model. In contrast, age was significantly and positively related with the performance function of observational learning for the opponent-model. CONCLUSIONS The finding provides the first empirical evidence to suggest that model type influences the use of observational learning in athletes. A positive relationship also exists between experience and self-observational learning as well as age and the opponent-performance function of observational learning. The results contribute to the observational learning literature and provide support to the Applied Model for the Use of Observation.

The reliability-based sensitivity analysis requires to recursively evaluate a multivariate structural model for many failure probability levels. This is in general a computationally intensive task due to irregular integrations used to define the structural failure probability. In this regard, the performance function is first approximated by using the multiplicative dimensional reduction method in this paper, and an approximation for the reliability-based sensitivity index is derived based on the principle of maximum entropy and the fractional moment. Three examples in the literature are presented to examine the performance of this entropy-based approach against the brute-force Monte-Carlo simulation method. Results have shown that the multiplicative dimensional reduction based entropy approach is rather efficient and able to provide reliability estimation results for the reliability-based sensitivity analysis of a multivariate structural model.

To address the problem of control surface redundancy for compound high-speed rotorcraft in multi-mode conversion process, a hybrid multi-objective control allocation strategy based on adaptive particle swarm optimization (PSO) algorithm is proposed. First, the control allocation problem is converted to a hybrid multi-objective optimization problem to solve the control redundancy, and a hybrid multi-objective optimization performance function is designed to satisfy the multiple requirements of flight mission. Second, a preference matrix is designed to determine the weight coefficients of the optimization performance function. The preference matrix can simplify the complexity of hybrid multi-objective optimization problem. Finally, an adaptive PSO algorithm is designed to solve the hybrid multi-objective control allocation dynamically. The simulation verifies the feasibility and effectiveness of the control allocation strategy, which eliminates the need of extra controllers in the mode conversion, reduces the difficulty of flight control system design and improves the system security.

Aiming at the longitudinal motion model of the air-breathing hypersonic vehicles (AHVs) with parameter uncertainties, a new prescribed performance-based active disturbance rejection control (PP-ADRC) method was proposed. First, the AHV model was divided into a velocity subsystem and altitude system. To guarantee the reliability of the control law, the design process was based on the nonaffine form of the AHV model. Unlike the traditional prescribed performance control (PPC), which requires accurate initial tracking errors, by designing a new performance function that does not depend on the initial tracking error and can ensure the small overshoot convergence of the tracking error, the error convergence process can meet the desired dynamic and steady-state performance. Moreover, the designed controller combined with an active disturbance rejection control (ADRC) and extended state observer (ESO) further enhanced the disturbance rejection capability and robustness of the method. To avoid the differential expansion problem and effectively filter out the effects of input noise in the differential signals, a new tracking differentiator was proposed. Finally, the effectiveness of the proposed method was verified by comparative simulations.

This paper, on the one hand, aims to identify significant crash risk factors at unsignalized three-leg intersections connecting rural two-lane two-way roads and minor roads with a STOP control on the approaches (3ST) and, on the other, to make adjustments to the Highway Safety Manual (HSM) procedure, fine-tuning its Safety Performance Function (SPF) based on observation of the local context. Over an 8-year period of study, a total of 240 crashes on 35 3ST intersections were observed, with no geometric-infrastructure adjustments or changes in the Annual Average Daily Traffic (AADT) and surrounding context noted at the intersections investigated. To obtain reliable results, the study period was divided into two groups: (a) 5 years to calibrate a new SPF, and (b) the remaining 3, not included in the first dataset, were used to validate the results. A negative binomial regression model was adopted to calibrate the new SPF. It was found that the AADT on the major and minor roads, the intersection skew angle, the co-occurrence of left and right-turn lanes on the major roads, and lighting seriously affect the crash scenario.

The structural failure of a tunnel is a process that evolves from local damage to overall destruction. The system reliability analysis method can be useful for analyzing the evolutionary law of local structural failure. In many complex stress environments, the structural performance function may be very complicated or even impossible to solve. This paper establishes a response surface function to represent the implicit tunnel performance function. The reliability of the shear capacity of a tunnel is considered. The critical parts and critical failure paths of the tunnel system are determined using the β-unzipping method. Failure evolution is analyzed to obtain the failure process of the tunnel system. Different failure modes are shown under different cases. Based on the partial failure probability and impact on the tunnel system, the risk levels of the critical parts at each stage of the failure history are evaluated. Therefore, the tunnel failure tree model obtained by combining the response surface method and β-unzipping method plays an important role in tunnel reliability evolution, and can evaluate tunnel safety comprehensively.