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We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by decomposing the input random processes using principal components or wavelets and then using a few simulations to estimate the distributions of the decomposition coefficients. A similar decomposition is also performed on the output random process. A kriging model is then established between the input and output decomposition coefficients and subsequently used to quantify the output random process corresponding to a realization of the input random parameters and random processes. What distinguishes our approach from others in metamodeling is that the system input is not deterministic but random.

A simulation-based, system reliability-based design optimization (RBDO) method is presented that can handle problems with multiple failure regions and correlated random variables. Copulas are used to represent the correlation. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with a trust-region optimization approach and local metamodels covering each trust region. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation per trust region. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. The PRRA method is based on importance sampling. It provides accurate results, if the support of the sampling PDF contains the support of the joint PDF of the input random variables. The sequential, trust-region optimization approach satisfies this requirement.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

In this paper, experimental study and FEA simulation are performed to investigate the effect of three different methods for joining dissimilar metal coupons in terms of their strength and load transferring capacity. The joining techniques considered include adhesive bonding, bolting and hybrid bolting-and-bonding. Elastic-plastic material model with damage consideration is used for each of the joint components. Traction-separation rule and failure criterion is defined for adhesive. Load transfer capacity and the failure mode are assessed for each type of joining. Joint strength is examined in terms of the effects of adhesive property, bolt preload level, and friction coefficient. Results show that load transferred and failure mechanism vary significantly between samples with different joint methods; preload evolution in bolt changes with friction coefficient; hybrid joint generally has advantage over the other two methods, namely, bolting-only and bonding-only.

Automotive systems engineering addresses the system throughout its life cycle, including requirement, specification, design, implementation, verification and validation of systems, modeling, simulation, testing, manufacturing, operation and maintenance. This four-volume set features 49 papers, originally published from 1999 through 2010, that cover the latest research and developments on various aspects of automotive systems engineering. The four-volume set consists of these individual volumes: Automotive Systems Engineering - Overview Automotive Systems Engineering - Requirements and Testing Automotive Systems Engineering - Modeling Automotive Systems Engineering - Approach and Verification

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.

Voltage and current surges are a major concern when it comes to ensuring the functional integrity of electrical and electronic components and modules in an automobile system. This paper presents a computer simulation study for analyzing the effect of high voltage spikes and current load dump on a new Integrated Driver/Receiver (IDR) IC, currently being developed for a J1850 Data Communication Bus in an automobile. It describes the modeling and simulation of the protection structure proposed for the device. The simulation study yields a prediction of current and voltage capability of the protection circuit based on thermal breakdown and transient responses of the circuit. Two levels of modeling, namely, the behavioral level model and the component level model, are used to generate the simulation results. Experimental data will be acquired and used to validate the simulation model when the actual device becomes available.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

An optical method for measuring surface deformation of automotive panels is presented. The technique, which is based a 3-D computer vision technique previously reported by the authors, is full-field, noncontact, simple, fast and can be automated. Theory of the technique together with experimental results are presented in the paper.

This paper presents a novel technique allowing time-dependent displacement of an object to be studied by continuously digitizing the speckle images using a high speed image acquisition system. Instead of generating fringe patterns, the displacement versus time for any point of interest can be studied. Therefore, the technique is equivalent to “many” massless and noncontact displacement sensors, which is particularly useful for vibration measurement.

Monte Carlo simulation is a popular tool for reliability assessment because of its robustness and ease of implementation. A major concern with this method is its computational cost; standard Monte Carlo simulation requires quadrupling the number of replications for halving the standard deviation of the estimated failure probability. Efforts to increase efficiency focus on intelligent sampling procedures and methods for efficient calculation of the performance function of a system. This paper proposes a new method to manage cost that views design as a decision among alternatives with uncertain reliabilities. Information from a simulation has value only if it enables the designer to make a better choice among the alternative options. Consequently, the value of information from the simulation is equal to the gain from using this information to improve the decision. A designer can determine the number of replications that are worth performing by using the method.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. It also affects the scheduling for preventive maintenance. Reliability usually degrades with time increasing therefore, the lifecycle cost due to more frequent failures which result in increased warranty costs, costly repairs and loss of market share. In a lifecycle cost based design, we must account for product quality and preventive maintenance using time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. For a repairable system, preventive maintenance is scheduled to avoid failures, unnecessary production loss and safety violations. This article proposes a methodology to obtain the optimal scheduling for preventive maintenance using time-dependent reliability principles.

A self-calibrating phase-shifting technique using a Michelson Interferometer is presented to measure phase distribution more accurately in Digital Speckle Pattern Interferometry (DSPI). DSPI is a well-established technique for the determination of whole field deformation via quantitatively measuring the phase distribution of speckle interferograms that use the phase shifting technique. In the phase shifting technique, the phase distribution in a speckle interferogram is quantitatively determined by recording multiple intensity images (usually four images) in which a constant phase shift, e.g. 90 degrees, is introduced between each consecutive image. A precise phase determination is greatly dependent on the accuracy of the phase shift introduced. The popular methods to minimize the error resulting from inaccurate phase shift use various algorithms and need to record five or eight images (rather than four images).

As the use of electronic devices in automobiles increases, the reliability of such devices is becoming increasingly important. One possible failure is due to leakage resulted from imperfect hermetical seal in mircochips and microelectronic packages. This paper presents an optical technique referred to as shearography for rapid evaluation of hermetics seals. The proposed process of leaking testing is very fast and practical.

This paper presents an optical method for evaluating residual stresses. The approach is based on measuring the deformation due to the relief of stress produced by a blind-hole drilling technique. The deformation is rapidly measured by shearography. Unlike the strain gage method, this method does not require mounting strain gages /transducers. A rapid process for detection of residual stress using a micro-indentation technique is also present. The method is practical for employment in a production/field environment.

Shearography is an optical technique developed for full-field measurement of surface deformation. It has since been accepted by industry as a practical nondestructive testing technique for evaluating the structural integrity of components and structures. Qualitatively, shearography reveals flaws from flaw-induced, anomalous deformation in the component under inspection, and quantitatively, shearography assesses the detected flaws through back-calculation from the anomalous deformation. This paper demonstrates that, with the use of multiple-frequency acoustical excitation together with the time-integrated shearographic recording technique, rapid and automated assessment of the integrity of adhesive-bonded composite structures can be realized in the actual plant environment.

Reaching a system level reliability target is an inverse problem. Component level reliabilities are determined for a required system level reliability. Because this inverse problem does not have a unique solution, one approach is to tradeoff system reliability with cost and to allow the designer to select a design with a target system reliability, using his/her preferences. In this case, the component reliabilities are readily available from the calculation of the reliability-cost tradeoff. To arrive at the set of solutions to be traded off, one encounters two problems. First, the system reliability calculation is based on repeated system simulations where each system state, indicating which components work and which have failed, is tested to determine if it causes system failure, and second, the task of eliciting and encoding the decision maker's preferences is extremely difficult because of uncertainty in modeling the decision maker's preferences.

In this article we present an approach to identify the system topology using simulation for reliability calculations. The system topology provides how all components in a system are functionally connected. Most reliability engineering literature assumes that either the system topology is known and therefore all failure modes can be deduced or if the system topology is not known we are only interested in identifying the dominant failure modes. The authors contend that we should try to extract as much information about the system topology from failure or success information of a system as possible. This will not only identify the dominant failure modes but will also provide an understanding of how the components are functionally connected, allowing for more complicated analyses, if needed. We use an evolutionary approach where system topologies are generated at random and then tested against failure or success data. The topologies evolve based on how consistent they are with test data.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. This paper proposes a methodology to calculate the cumulative probability of failure (probability of first passage or upcrossing) of a dynamic system, driven by an ergodic input random process.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. Monte-Carlo simulations are accurate but computationally expensive.