US Army Ground Vehicle Crew Compartment Halon Replacement Program

2000 ◽  
Author(s):  
Mike Clauson ◽  
Steve McCormick ◽  
Hal Cross
Keyword(s):  
Ground Vehicle ◽  
Us Army ◽  
Halon Replacement

A Stochastic Approach to Integrated Vehicle Reliability Prediction

2009 ◽  
Author(s):  
Justin Madsen ◽  
Dan Ghiocel ◽  
David Gorsich ◽  
David Lamb ◽  
Dan Negrut
Keyword(s):  
Integrated Approach ◽  
Stochastic Approach ◽  
Reliability Prediction ◽  
Operational Environment ◽  
Research Project ◽  
Element Analysis ◽  
Ground Vehicle ◽  
Us Army ◽  
University Of Wisconsin ◽  
Prediction Approach

This paper addresses some aspects of an on-going multiyear research project of GP Technologies in collaboration with University of Wisconsin-Madison for US Army TARDEC. The focus of this research project is to enhance the overall vehicle reliability prediction process. A combination of stochastic models for both the vehicle and operational environment are utilized to determine the range of the system dynamic response. These dynamic results are used as inputs into a finite element analysis of stresses on subsystem components. Finally, resulting stresses are used for damage modeling and life and reliability predictions. This paper describes few selected aspects of the new integrated ground vehicle reliability prediction approach. The integrated approach combines the computational stochastic mechanics predictions with available statistical experimental databases for assessing vehicle system reliability. Such an integrated reliability prediction approach represents an essential part of an intelligent virtual prototyping environment for ground vehicle design and testing.

scholarly journals Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Brandon Hansen ◽  
Cody Coleman ◽  
Yi Zhang ◽  
Maria Seale
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
A Priori ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
A Priori Knowledge ◽  
Data Sets ◽  
Ground Vehicle ◽  
Us Army ◽  
Priori Knowledge

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels. Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effective, but extremely time-consuming; thus, an automated approach to labelling is preferred. The approach described in this paper examines key aspects of the fault text relevant to enabling automatic labeling. A method was developed based on the hypothesis that a given fault description could be generalized into a category. This method uses various natural language processing (NLP) techniques and a priori knowledge of ground vehicle faults to assign classes to the maintenance fault descriptions. The core component of the method used in this paper is a Word2Vec word-embedding model. Word embeddings are used in conjunction with a token-oriented rule-based data structure for document classification. This methodology tags text with user-provided classes using a corpus of similar text fields as its training set. With classes of faults reliably assigned to a given description, supervised machine learning with these classes can be applied using related maintenance information that preceded the fault. This method was developed for labeling US Army Ground Vehicle Maintenance Records, but is general enough to be applied to any natural language data sets accompanied with a priori knowledge of its contents for consistent labeling. In addition to applications in machine learning, generated labels are also conducive to general summarization and case-by-case analysis of faults. The maintenance components of interest for this current application are alternators and gaskets, with future development directed towards determining the RUL of these components based on the labeled data.

Multibody Modeling of Vehicles in a Virtual Prototyping Environment

1995 ◽  
Author(s):  
Kishore Sai Vejju ◽  
Jeffrey S. Freeman
Keyword(s):  
Driving Simulator ◽  
System Modeling ◽  
Virtual Prototyping ◽  
Vehicle Body ◽  
Multibody Modeling ◽  
Modeling Tool ◽  
Ground Vehicle ◽  
Us Army ◽  
The Us ◽  
Kinematic Models

Abstract This paper presents concepts involved in the theory and implementation of a vehicle body and suspension modeling tool as part of the software development for the National Advanced Driving Simulator (NADS). The NADS will be a state-of-the-art, operator-in-the-loop ground vehicle simulator, which can be applied to both human factors and vehicle virtual prototyping studies. By applying the modeling tool developed in this study, vehicle kinematic models can be easily created and tested, either using off-line engineering analysis packages or using operator-in-the-loop simulators, such as the NADS. Vehicles are complex systems containing multiple bodies, joints and force generating elements. Manually modeling these systems for kinematic and/or dynamic analysis is tedious and prone to errors. This creates a need for a modeling tool which reduces modeling time, increases modeling accuracy and is easy to use. This paper discusses the concepts involved in developing a modeling tool for the topology analysis and assembly of the multibody vehicle model. Suspension system modeling is briefly described, along with an example employing the US Army HMMWV vehicle.

Using Modeling and Simulation to Evaluate Traction of Track Vehicles

2000 ◽  
Author(s):  
David D. Gunter ◽  
Michael D. Letherwood
Keyword(s):  
Dynamic Performance ◽  
Vehicle Design ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Us Army ◽  
3 Dimensional ◽  
Tractive Effort ◽  
The Us ◽  
Vehicle Systems ◽  
Time Required

Abstract The US Army Tank-automotive and Armaments Command (TACOM) has the mission of procuring and managing the US Army’s fleet of wheeled and tracked vehicles. TACOM’s Tank Automotive Research, Development and Engineering Center (TARDEC) provides engineering and scientific support directed at maximizing the capability of all Department of Defense (DOD) ground vehicle systems and ensuring the safety of their personnel. In order to reduce the time required to deploy troops and equipment, engineers and scientists at TARDEC have been investigating modifications to ground vehicles that lead to overall increases in performance, especially in the areas of off-road mobility, and on-road stability and handling. This paper describes an effort to assess the dynamic performance of a track laying (tracked) Recovery Vehicle towing a disabled tracked vehicle whose weight is approximately equal to that of the Recovery Vehicle. Specifically, this paper will describe techniques employed to develop a 3-dimensional dynamic model of the vehicle combination, and apply the model to evaluate towing performance of the recovery vehicle. It also describes measures aimed at minimizing incidences of jackknifing when braking on downhill slopes, as well as vehicle design modifications that were modeled and simulated in efforts to reduce the combination’s jackknife vulnerability. These modifications included tow bar schemes that locked-out inter-vehicle yaw, and external surge brakes mounted on the towed vehicle. Techniques used to model and simulate the tractive effort available to the Recovery Vehicle on varied soil types are described as are analyses used to determine the combination’s ability to climb grades. Vehicle modifications aimed at increasing the tractive effort available, such as tow bar pitch orientation and track shoe geometry changes are also described.

Seeded Fault Testing of Military Ground Vehicles as a Pathway to Condition Based Maintenance

2010 ◽  
Author(s):  
Matthew J. Hillegass ◽  
Eric L. Rabeno
Keyword(s):  
System Analysis ◽  
Condition Based Maintenance ◽  
Engine Oil ◽  
Actual Condition ◽  
Material System ◽  
Fault Testing ◽  
Ground Vehicle ◽  
Us Army ◽  
The Us ◽  
Vehicle Systems

The performance of military ground vehicle systems is being degraded due to high operation tempo and exposure to extreme environments while performing in-theater service. To address this issue, the US Army is implementing a policy of Condition Based Maintenance which is supported by the Army Material System Analysis Activity (AMSAA). The vision of this policy is to base the maintenance of systems upon the actual condition of the system and not upon time- or distance-based schedules. This capability will be enabled by the application of usage, diagnostic and prognostic processes executed on a Health and Usage Monitoring System (HUMS) installed on these vehicle systems. A thorough understanding of the ways in which the system condition is degenerated and the ability of the HUMS to detect, identify, and communicate all conditions that require maintenance in a timely manner are key requirements of these processes. Seeded Fault Testing is the critical means of fulfilling these requirements. A joint Seeded Fault Testing project between AMSAA and the US Army Aberdeen Test Center (ATC) has been initiated to gain a thorough understanding of ground vehicle system condition degeneration and HUMS implementation of products and processes that can accurately identify and communicate it. A military vehicle underwent exhaustive testing in support of this project. The vehicle was subjected to specific use scenarios while carefully controlled faults are induced in engine, transmission, and other key mechanical subsystems that would degrade vehicle performance and degenerate system condition. The vehicle’s induced faults included lowered coolant levels to simulate leakage, restriction of air flow across radiators and filters to simulate dust and debris accumulation, and lowered transmission and engine oil levels to simulate leakage and usage. The objective of this project was to use the results from the seeded fault tests to establish critical thresholds, trends, and patterns that will be the basis of the creation and implementation of real-time HUMS-based algorithms that predict faults, warn operators and maintainers of imminent failures, and provide a sound foundation for Condition Based Maintenance.

A Descriptive Study of US Army Soldiers Referred to, Evaluated By And Enrolled In The Army Substance Abuse Program, 1988-2003

2006 ◽  
Author(s):  
Ilyssa E. Hollander ◽  
Nicole S. Bell ◽  
Margaret Phillips ◽  
Paul J. Amoroso ◽  
Les MacFarling
Keyword(s):  
Substance Abuse ◽  
Descriptive Study ◽  
Us Army ◽  
Substance Abuse Program ◽  
Army Soldiers

Human Factors Evaluation of 360 Panoramic View Camera System for Ground Vehicle

2007 ◽  
Author(s):  
Cheng Li Wei ◽  
Ang Cher Wee ◽  
Chan Wai Herng ◽  
Ying Meng Fai
Keyword(s):  
Human Factors ◽  
Panoramic View ◽  
Camera System ◽  
Ground Vehicle
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