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In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.

Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.

Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

Assessing the dynamic performance of multilayer plates subjected to impulsive loading is of interest for identifying configurations that either absorb energy or transmit the energy in the transverse directions, thereby mitigating the through-thickness energy propagation. A reduced-order modeling approach is presented in this paper for rapidly evaluating the structural dynamic performance of various multilayer plate designs. The new approach is based on the reverberation matrix method (RMM) with the theory of generalized rays for fast analysis of the structural dynamic characteristics of multilayer plates. In the RMM model, the waves radiated from the dynamic load are reflected and refracted at each interface between layers, and the waves within each layer are transmitted with a phase lag. These two phenomena are represented by the global scattering matrix and the global phase matrix, respectively.

The Powertrain Analysis and Computational Environment (PACE) is a forward-looking powertrain simulation tool that is ready for a High-Performance Computing (HPC) environment. The code, written in C++, is one actor in a comprehensive ground vehicle co-simulation architecture being developed by the CREATE-GV program. PACE provides an advanced behavioral modeling capability for the powertrain subsystem of a conventional or hybrid-electric vehicle that exploits the idea of reusable vehicle modeling that underpins the Autonomie modeling environment developed by the Argonne National Laboratory. PACE permits the user to define a powertrain in Autonomie, which requires a single desktop license for MATLAB/Simulink, and port it to a cluster computer where PACE runs with an open-source BSD-3 license so that it can be distributed to as many nodes as needed.

Using the total probability theorem, we propose a method to calculate the failure rate of a linear vibratory system with random parameters excited by stationary Gaussian processes. The response of such a system is non-stationary because of the randomness of the input parameters. A space-filling design, such as optimal symmetric Latin hypercube sampling or maximin, is first used to sample the input parameter space. For each design point, the output process is stationary and Gaussian. We present two approaches to calculate the corresponding conditional probability of failure. A Kriging metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure and the failure rate of the dynamic system. The proposed method is demonstrated using a vibratory system.

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.

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.

Real-time driving simulations are an important tool for verifying vehicle and vehicle component designs with a driver in the loop. They not only provide a cost effective solution but also an ability to verify designs in a safe and controlled operating environment. A real-time driving experiment has been developed for Stryker to compare the ride and handling performance of a baseline passive suspension to that of a Magneto-Rheological (MR) semi-active damper suspension. The Tank Automotive Research Development and Engineering Center (TARDEC) has integrated this new suspension into a real time vehicle dynamics model of the Stryker using the MR suspension model developed by the Original Equipment Manufacturer (OEM). Using this real-time model and the TARDEC Ride Motion Simulator (RMS), TARDEC associates, along with associates from the Stryker Program Management office and the suspension OEM were able to drive and compare the passive and MR Stryker in a virtual 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.

This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The integrated stochastic framework combines the computational physics-based predictions with experimental testing information for assessing vehicle reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size computational mechanics models and high-dimensional stochastic spaces, a HPC simulation-based approach to the reliability problem was implemented.

This paper outlines the process by which a current production commercial diesel engine was modified for high performance military use. Salient among the needs for military use are a compact package and high power output. The compact engine package was addressed by the selection of a base engine of relatively small displacement and slim inline configuration. The air system, fuel pump, and other ancillary components were re-packaged close to the engine. A high engine power output was achieved by turbocharging at the pressure ratios achievable by current production turbochargers, increased engine speed, and upgrades in the design of engine components. Close attention was paid to thermal and mechanical loading of all components of the engine. Most components in the engine were changed to accommodate these loadings and packaging needs.