ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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This paper addresses the sensitivity analysis of time-dependent computer models. Often, in practice, we partition the inputs into a subset of inputs relevant to the application studied, and a complement subset of nuisance inputs that are not of interest. We propose sensitivity measures for the relevant inputs of such dynamic computer models. The subset of nuisance inputs is used to create replication-type information to help quantify the uncertainty of sensitivity measures (or indices) for the relevant inputs. The method is first demonstrated on an analytical example. Then we use the proposed method in an application about the safety of restraint systems in light tactical vehicles. The method indicates that chest deflection curves are more sensitive to the addition of pretensioners and load limiters than to the type of seatbelt. This article is available in the ASME Digital Collection at .

Managing potential disruptive events at the operating phase of an engineered system therefore improving the system's failure resilience is an importance yet challenging task in engineering design. The resilience of an engineered system can be improved by enhancing the failure restoration capability of the system with appropriate system control strategies. Therefore, control-guided failure restoration is an essential step in engineering design for resilience. Considering different characteristics of disruptive events and their impacts to the performance of a system, effective control strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop generally applicable guiding principles for selecting effective control strategies, thus implementing the control-guided failure restorations. In this paper, a comparison of three commonly used control strategies for dynamic system control is conducted with the focus on the effectiveness of restoring system performance after the system has undergone different major disruptive events. A case study of an electricity transmission system is used to demonstrate the dynamic system modeling and the comparison of three control strategies for disruption management. This article is available in the ASME Digital Collection at .

Momentum-based method and multibody method are often used in accident reconstructions. In this paper, a method that combines momentum-based method and multibody method is presented to analyze a real-world accident with occupant ejection. A multibody model is employed as an effective approach to simulate interaction between vehicle and occupants in the postanalysis of a traffic accident. This model can be used to answer questions that which occupant the driver was, when the accident happened, and the vehicle's speed before occupant ejection. Based on the information of the vehicle turn-over location and vehicle speed at the moment of the accident, some unknown parameters of the vehicle before the accident, such as initial speed, acceleration, and vehicle-ground friction coefficient, can be determined. In order to estimate these unknown parameters, a Kriging surrogate model is first constructed for the vehicle movement simulation. The Markov Chain Monte Carlo (MCMC) simulation is then employed to infer the unknown parameters based on the Kriging surrogate model. The results show that a high degree of consistency has been achieved between the computer simulation model and the accident on-scene information. In addition, the injury of occupants in simulation agrees well with conclusions of autopsy report. This article is available in the ASME Digital Collection at .

The reliability of repairable systems is important for many engineering applications such as warranty forecasting, maintenance strategies, and durability, among others. A generalized renewal process (GRP) approach, considering the effectiveness of repairs, is commonly used, modeling the concepts of minimal repair, perfect repair, and general repair. The effect of the latter is between the effects of a minimal and a perfect repair. The GRP models the sequence of recurrent failure/repair events for repairable systems by solving the so-called g-renewal equation, which has no analytical solution. This paper proposes a data-driven numerical estimation of the expected number of failures (ENF) for the GRP model without solving the complicated g-renewal equation directly. Instead, it uses an empirical relationship among the cumulative intensity function (CIF) of the GRP, ordinary renewal process (ORP), and nonhomogeneous Poisson process (NHPP). The ORP and NHPP are limiting cases of the generalized renewal process. For practical reasons, it is common to observe only a few units of a repairable system population for only a short time. Using the observed data, the proposed approach creates a reliability metamodel of a renewal process, which is then used to predict the expected number of failures, and assess the average effectiveness of each repair. This increases the usefulness of the method for many practical reliability problems where the collection of a large amount of data is not possible or economical. The good accuracy of the proposed approach is demonstrated with three examples using simulated data, and a real-life example of locomotive braking grids using actual data. This article is available in the ASME Digital Collection at .

Uncertainties cannot be ignored in the design process of complex multidisciplinary systems. Robust multidisciplinary design optimization methods (RMDOs) can treat uncertainties as specified probabilistic distributions when enough statistical information is available while they assign intervals for nondeterministic variables since designers may not have enough information to obtain statistical distributions, especially in the early stage of design optimization processes. Both types of uncertainties are very likely to appear simultaneously. In order to obtain solutions to RMDO problems under mixed interval and probabilistic uncertainties, this work proposed a new sequential RMDO approach, mixed SR-MDO. First, the robust optimization (RO) problem in a single discipline under mixed uncertainties is formulated and solved. Then, following the SR-MDO framework from the previous work, MDO problems under mixed uncertainties are solved by handling probabilistic and interval uncertainties sequentially in decomposed subsystem problems. Interval uncertainties are handled by using the worst-case sensitivity analysis, and the influence of probabilistic uncertainties in objectives, constraints, as well as in discipline analysis models is characterized by corresponding mean and variance. The applied SR-MDO framework allows subsystems in its full autonomy RO and sequential RO stages to run independently in parallel. This makes mixed SR-MDO be efficient for independent disciplines to work simultaneously and be more time-saving. Computational complexity of the proposed approach mainly relates to the double-loop optimization process in the worst-case interval uncertainties analysis. Examples are presented to demonstrate the applicability and efficiency of the mixed SR-MDO approach. This article is available in the ASME Digital Collection at .

This paper presents a reliability analysis method for automated vehicles equipped with adaptive cruise control (ACC) and autonomous emergency braking (AEB) systems to avoid collision with an obstacle in front of the vehicle. The proposed approach consists of two main elements, namely uncertainty modeling of traffic conditions and model-based reliability analysis. In the uncertainty modeling step, a recently developed Gaussian mixture copula (GMC) method is employed to accurately represent the uncertainty in the road traffic conditions using the real-world data, and to capture the complicated correlations between different variables. Based on the uncertainty modeling of traffic conditions, an adaptive Kriging surrogate modeling method with an active learning function is then used to efficiently and accurately evaluate the collision-avoidance reliability of an automated vehicle. The application of the proposed method to the Department of Transportation Safety Pilot Model Deployment database and an in-house built Advanced Driver Assist Systems with ACC and AEB controllers demonstrate the effectiveness of the proposed method in evaluating the collision-avoidance reliability. This article is available in the ASME Digital Collection at .

Variance and sensitivity analysis are challenging tasks when the evaluation of system performances incurs a high-computational cost. To resolve this issue, this paper investigates several multifidelity statistical estimators for the responses of complex systems, especially the mesostructure–structure system manufactured by additive manufacturing. First, this paper reviews an established control variate multifidelity estimator, which leverages the output of an inexpensive, low-fidelity model and the correlation between the high-fidelity model and the low-fidelity model to predict the statistics of the system responses. Second, we investigate several variants of the original estimator and propose a new formulation of the control variate estimator. All these estimators and the associated sensitivity analysis approaches are compared on two engineering examples of mesostructure–structure system analysis. A multifidelity metamodel-based sensitivity analysis approach is also included in the comparative study. The proposed estimator demonstrates its strength in predicting variance when only a limited number of expensive high-fidelity data are available. Finally, the pros and cons of each estimator are discussed, and recommendations are made on the selection of multifidelity estimators for variance and sensitivity analysis. This article is available in the ASME Digital Collection at .

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods. This article is available in the ASME Digital Collection at .

Accurate state-of-charge (SOC) estimation of lithium-ion battery packs is technically challenging because of the cell-to-cell variability due to the manufacturing tolerance. In addition, there is no unanimous definition of the pack SOC since each cell has its own SOC and the pack can be configured in different ways. This study first adopts a suitable pack SOC definition among existing ones, then proposes uncertainty modeling and propagation analysis for pack SOC estimation considering the cell-to-cell variability, and finally conducts the SOC estimation for one serially connected battery pack using the one state hysteresis model with the extended Kalman filter (EKF). The results reveal that pack SOC variability is inevitable due to the cell-to-cell variability and accurate pack SOC estimation is challenging considering both the cell-level SOC accuracy and the pack-level estimation efficiency. This article is available in the ASME Digital Collection at .

System reliability is usually predicted with the assumption that all component states are independent. This assumption may not accurate for systems with outsourced components since their states are strongly dependent and component details may be unknown. The purpose of this study is to develop an accurate system reliability method that can produce complete joint probability density function (PDF) of all the component states, thereby leading to accurate system reliability predictions. The proposed method works for systems whose failures are caused by excessive loading. In addition to the component reliability, system designers also ask for partial safety factors for shared loadings from component suppliers. The information is then sufficient for building a system-level joint PDF. Algorithms are designed for a component supplier to generate partial safety factors. The method enables accurate system reliability predictions without requiring proprietary information from component suppliers. This article is available in the ASME Digital Collection at .

Due to the uncertain and dynamic parameters from design, manufacturing, and working conditions, many engineering structures usually show uncertain and dynamic properties. During the product design and development stages, designers often encounter reliability and robustness measures of dynamic uncertain structures. Time-varying and high nonlinear performance brings a new challenge for the reliability-based robust design optimization. This paper proposes a multi-objective integrated framework for time-dependent reliability-based robust design optimization and the corresponding algorithms. The integrated framework is first established by minimizing the mean value and coefficient of variation of the objective performance at the same time subject to time-dependent probabilistic constraints. The time-dependent probabilistic constraints are then converted into deterministic constraints using the dimension reduction method. The evolutionary multi-objective optimization algorithm is finally employed for the deterministic multi-objective optimization problem. Several examples are investigated to demonstrate the effectiveness of the proposed method. This article is available in the ASME Digital Collection at .

This paper establishes a competing failure analysis model for complex mechanical system under component failure and performance failure considering degradation. Traditionally, mechanical system is composed by a number of components. Meanwhile, mechanical system has the ability to accomplish its specific performance. Therefore, mechanism may fail because of two kinds of failure modes, the component failure due to degradation (such as component wear) and the performance failure (system couldn't complete performance). The two failure modes compete with each other because as soon as one mode occurs the system just fails. The component will degrade with time as system operates as well as the system performance. In this paper, Brownian motion (BM) with nonlinear drift is used to model the degradation of components based on which component failure is analyzed. The function of performance measurement is built by surrogate and performance failure is analyzed by it at different working circulation. Farlie–Gumbel–Morgenstern (FGM) copula is introduced to describe the dependence. The system reliability is analyzed by FGM copula as well as competing failure probability for each failure mode. Finally, a numerical example and an engineering case study are used to illustrate the proposed model. This article is available in the ASME Digital Collection at .

This work uses a new method of determining a parameterization, resampling, and dimension search of an uncertainty model that can be used for efficient engineering models in control design. An algorithm using the Cayley–Menger determinant as a measure of the dimension test geometry (volume/area/length) of the parametric data points is presented to search for a reduced number of dimensions that can be used to represent the parameters of a model that captures the uncertainty in a dynamic system (uncertainty model). A genetic algorithm (GA) is utilized to solve the nonconvex problem of finding the coefficients of a parameterization of the uncertainty model. A resampling approach for the uncertainty model is also presented. The methods presented here are demonstrated on an electrohydraulic valve control system problem. This demonstration includes consideration of the dimensional search, data resampling, and parameterizing of an uncertainty class determined from test data for 30 replications of an electrohydraulic flow control valve which were experimentally modeled in the lab. The suggested resampling method and the parameterization of the uncertainty are used to analyze the robust stability of a control system for the class of valves using both frequency domain h-infinity methods and analysis of closed-loop poles for the resampled uncertainty model. This article is available in the ASME Digital Collection at .

Predicting of hazards associated with the damages of fuel storage tanks in petroleum refineries is critical in maintaining safety of the facilities. This study assesses the hazard of the refinery gasoline tank damage by augmented mathematical and semi-experimental methods. This research evaluates complete content release of the reservoir tank scenario in a given time, as the worst case. Environment conditions are evaluated as affecting factors on the progression of the scenario. Comparison between the results of numerical simulations for pool fire as well as vapor cloud explosion with the results of simulation based on empirical-mathematical models indicates noticeable differences between the results in the vicinity of the boundary conditions; however, with increasing the distance from the center of the accident point, this difference decreases markedly. This article is available in the ASME Digital Collection at .

Drilling is one of the costliest and risky activities in oil and gas industry due to complexity of interactions with downhole formation. Under such conditions, the uncertainty of drillstring behavior increases, and hence, it becomes difficult to predict the causes, occurrences, and types of failures. Lateral and torsional vibrations often cause failure of bottom hole assembly (BHA), drillstring failure, drill bit, and wall borehole damages. In this work, a model is presented to determine the impact of lateral and torsional vibrations on a drillstring during the drilling operation. The model aims to mimic real drillstring behavior inside a wellbore with regards to its dynamic movements due to multiple real situations such as eccentricity of collars, drill pipe sections, and stick-slip phenomena occurring due to the interaction of the bit and the drillstring with the well formation. The work aims to develop a basis for determining critical operating speeds and design parameters to provide safe drilling procedures and reduce drillstring fatigue failure. Lagrangian approach is used in this study to attain drillstring lateral and torsional vibration coupling equations. The nonlinear equations are solved numerically to obtain the response of the system. In this work, we also present a brief description of an in-house constructed experimental setup. The setup has the capability to imitate the downhole lateral and torsional vibration modes. Parameters from the experimental investigations are incorporated for validation of the mathematical models and for prediction of the drillstring fatigue life. Such investigations are essential for oil/gas industries as they provide solutions as well as recommendations about operating speed, lateral and torsional amplitudes measurements and corrections, and the conditions for avoiding occurrence of natural frequency(ies) of the system. This article is available in the ASME Digital Collection at .

The uncertain free vibration analysis of engineering structures with the consideration of nonstochastic spatially dependent uncertain parameters is investigated. A recently proposed concept of interval field is implemented to model the intrinsic spatial dependency of the uncertain-but-bounded system parameters. By employing the appropriate discretization scheme, evaluations of natural frequencies for engineering structures involving interval fields can be executed within the framework of the finite element method. Furthermore, a robust, yet efficient, computational strategy is proposed such that the extreme bounds of natural frequencies of the structure involving interval fields can be rigorously captured by performing two independent eigen-analyses. Within the proposed computational analysis framework, the traditional interval arithmetic is not employed so that the undesirable effect of the interval overestimation can be completely eliminated. Consequently, both sharpness and physical feasibility of the results can be guaranteed to a certain extent for any discretized interval field. The plausibility of the adopted interval field model, as well as the feasibility of the proposed computational scheme, is clearly demonstrated by investigating both academic-sized and practically motivated engineering structures. This article is available in the ASME Digital Collection at .