Browse Topic: Statistical analysis
ABSTRACT This paper addresses some aspects of an on-going multiyear research project of GP Technologies for US Army TARDEC. The focus of the research project has been the enhancement of the overall vehicle reliability prediction process. This paper describes briefly few selected aspects of the new integrated reliability prediction approach. The integrated approach uses both computational mechanics predictions and experimental test databases for assessing vehicle system 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
ABSTRACT Implementing Prognostic and Predictive Maintenance (PPMx) for the U.S. Army’s ground vehicle fleet requires the design and integration of on-platform predictive analytics. To support the design process, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) and Applied Research Laboratory (ARL) Penn State researchers are developing a systematic approach that uses reliability modeling in a guiding role. The key steps of the process are building the initial reliability model from available data (e.g., system diagrams and physical layouts), augmenting with information on observed states and failure modes via subject matter experts, and then conducting trades on additional sensors and algorithms to determine a suitable predictive analytics capability. In this paper we provide an example of this process as applied to an Army ground vehicle, first focusing on a simplified sub-problem to demonstrate the technique, then providing statistics on the large scale process. Citation: M
ABSTRACT In this paper, we discuss a neuroimaging experiment that employed a mission-based scenario (MBS) design, a new approach for designing experiments in simulated environments for human subjects [1]. This approach aims to enhance the realism of the Soldier-task-environment interaction by eliminating many of the tightly-scripted elements of a typical laboratory experiment; however, the absence of these elements introduces several challenges for both the experimental design and statistical analysis of the experimental data. Here, we describe an MBS experiment using a simulated, closed-hatch crewstation environment. For each experimental session, two Soldiers participated as a Commander-Driver team to perform six simulated low-threat security patrol missions. We discuss challenges faced while designing and implementing the experiment before addressing analysis approaches appropriate for this type of experimentation. We conclude by highlighting three example transition pathways from
ABSTRACT Predictive analysis of vehicle electrical systems is achievable by combining condition based maintenance (CBM) techniques and testing for statistical significance (TSS). When paired together, these two fundamentally sound sciences quantify the state of health (SOH) for batteries, alternators, starters, and electrical systems. The use of a communication protocol such as SAE J1939 allows for scheduling maintenance based on condition and not a traditional time schedule
ABSTRACT In this context, a damage model is a mathematical algorithm that is used to predict if and when in a given loading history a structure will fail by ductile fracture. Increments in a damage parameter are related to strain increments and state of stress. The damage model would operate as part of a numerical simulation, or separately on an output file. A scale effect in ductile fracture is widely recognized from test data, where a large structure tends to fail at lower strain than a smaller structure that is geometrically similar and of the same material. Most damage models are not scale sensitive, and when they are calibrated to data from small laboratory specimens, they will tend to over-predict the performance (i.e., energy absorbing capability) of a larger structure. Another factor is scatter in test results even when specimens are made with care to be as identical as possible. Both of these factors are addressed in the proposed statistics-based damage model. Scale effects
This document defines the steps and documentation required to perform a digital fiber optic link loss budget. This document does not specify how to design a digital fiber optic link. This document does not specify the parameters and data to use in a digital fiber optic link loss budget
This document defines a quantified means of specifying a digital fiber optic link loss budget: Between end users and system integrators Between system integrators and subsystem suppliers Between subsystem suppliers and component vendors The standard specifies methods and the margin required for categories of links
A structural load estimation methodology was developed for RLV-TD HEX-01 hypersonic experimental mission, the maiden winged body technology demonstrator vehicle of ISRO. Primarily the method evaluates time history of station loads considering effects of vehicle dynamics and structural flexibility. Station loads of critical structures are determined by superposition of quasi-static aerodynamic loads, dynamic inertia loads, control surface loads and propulsion loads based on actual physics of the system, improving upon statistical load combination approaches. The technique characterizes atmospheric regime of flight from vehicle loads perspective and ensures adequate structural margin considering atmospheric variations and system level perturbations. Features to estimate change in loads due to wind variability and atmospheric turbulence are incorporated into the load estimation methodology. Augmentation in loads due to structural flexibility is assessed along the trajectory using vehicle
This study was conducted to assess the occupant restraint use and injury risks by seating position. The results were used to discuss the merit of selected warning systems. The 1989-2015 NASS-CDS and 2017-2021 CISS data were analyzed for light vehicles in all, frontal and rear tow-away crashes. The differences in serious injury risk (MAIS 3+F) were determined for front and rear seating positions, including the right, middle and left second-row seats. Occupancy and restraint use were determined by model year groups. Occupancy relative to the driver was 27% in the right-front (RF) and 17% in the second row in all crashes. About 39% of second-row passengers were in the left seat, 15% in the center seat and 47% in the right seat. Restraint use was lower in the second row compared to front seats. It was 43% in the right-front and 32% in the second-row seats in all crashes involving serious injury. Restraint use increased with model year groups. It was 63% in the ‘61-‘89 MY vehicles and 90
The subsystem of front of dash (FOD) and instrument panel (IP) is a critical path to isolate the powertrain noise and road noise for vehicles. This subsystem mainly consists of sheet metal, dash mats, IP, and the components inside IP such as HVAC and wiring harness. To achieve certain level of cabin quietness, the sound transmission loss performance of this subsystem is usually used as a quantifier. In this paper, the sound transmission loss through the FOD and IP is investigated up to 10kHz, through both acoustic testing and numerical simulation. In the acoustic testing, the subsystem is cut from a vehicle and installed on the wall of two-rooms STL testing suite, with source room being reverberant and receiver room being anechoic. In the testing, various scenarios are measured to understand the contributions from different components. The numerical simulation is based on statistical energy analysis (SEA) because deterministic methods have difficulty to predict the STL up to 10k Hz due
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