Browse Topic: Failure analysis
ABSTRACT Problem: The traditional four (4) methods for improving reliability; 1) High design safety margin, 2) Reduction in component count or system architectural complexity, 3) Redundancy, and 4) Back-up capability, are often ignored or perceived as being excessively costly in weight, space claim as well as money. Solution 1: Discussed here are the practical and very cost effective methods for achieving improved reliability by Functional Interface Stress Hardening (FISHtm or FISHingtm). The Author has been able to apply FISH to eliminate 70-92% of unscheduled equipment downtime, within 30-60 days, for more than 30 of the Fortune 500 and many other large companies which utilize automation controls, computers, power electronics and hydraulic control systems. Solution 2: From Structured Innovation the 33 DFR Methods & R-TRIZ Tool can be used to grow or improve reliability, via rapid innovation. The R-TRIZ tool) is provided so that users can instantly select the best 2, 3 or 4 of these
ABSTRACT The age of large autonomous ground vehicles has arrived. Wherever vehicles are used, autonomy is desired and, in most cases, being studied and developed. The last barrier is to prove to decision makers (and the general public) that these autonomous systems are safe. This paper describes a rigorous safety testing environment for large autonomous vehicles. Our approach to this borrows elements from game theory, where multiple competing players each attempt to maximize their payout. With this construct, we can model an environment that as an agent that seeks poor performance in an effort to find the rare corner cases that can lead to automation failure
ABSTRACT To improve robustness of autonomous vehicles, deployments have evolved from a single intelligent system to a combination of several within a platoon. Platooning vehicles move together as a unit, communicating with each other to navigate the changing environment safely. While the technology is robust, there is a large dependence on data collection and communication. Issues with sensors or communication systems can cause significant problems for the system. There are several uncertainties that impact a system’s fidelity. Small errors in data accuracy can lead to system failure under certain circumstances. We define stale data as a perturbation within a system that causes it to repetitively rely on old data from external data sources (e.g. other cars in the platoon). This paper conducts a fault injection campaign to analyze the impact of stale data in a platooning model, where stale data occurs in the car’s communication and/or perception system. The fault injection campaign
ABSTRACT Significant Design for Reliability (DfR) methodology challenges are created with the integration of autonomous vehicle technologies via applique systems in a ground military vehicle domain. Voice of the customer data indicates current passenger vehicle usage cycles are typically 5% or less (approximately 72 minutes of use in a twenty-four hour period) [2]. The time during which vehicles currently lay dormant due to drivers being otherwise occupied could change with autonomous vehicles. Within the context of the fully mature autonomous military vehicle environment, the daily vehicle usage rate could grow to 95% or more. Due to this potential increase in the duty or usage cycle of an autonomous military vehicle by an order of magnitude, several issues which impact reliability are worth exploring. Citation: M. Majcher, J. Wasiloff, “New Design for Reliability (DfR) Needs and Strategies for Emerging Autonomous Ground Vehicles”, In Proceedings of the Ground Vehicle Systems
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 Camber Corporation, under contract with the TACOM Life Cycle Management Command Integrated Logistics Support Center, has developed an innovative process of data mining and analysis to extract information from Army logistics databases, identify top cost and demand drivers, understand trends, and isolate environmental issues. These analysis techniques were initially used to assess TACOM-managed equipment in extended operations in Southwest Asia (SWA). In 2009, at the request of TACOM and the Tank Automotive Research, Development and Engineering Center (TARDEC), these data mining processes were applied to four tactical vehicle platforms in support of Condition Based Maintenance (CBM) initiatives. This paper describes an enhanced data mining and analysis methodology used to identify and rank components as candidates for CBM sensors, assess total cost of repair/replacement and determine potential return on investment in applying CBM technology. Also discussed in this paper is the
ABSTRACT The M1 Abrams will be the primary heavy combat vehicle for the US military for years to come. Improvements to the M1 that increase reliability and reduce maintenance will have a multi-year payback. The M1 engine intake plenum seal couples the air intake plenum to the turbine inlet, and has opportunities for improvement to reduce leakage and intake of FOD (foreign object debris) into the engine, which causes damage and premature wear of expensive components
ABSTRACT In light of the cancellation of MIL-STD 1629A on 4 August 1998 with no superseding document, this paper outlines the tailoring of an effective industry tool for risk identification and prioritization that will lead to more reliable weapon systems for the warfighter, with reduced total ownership costs. The canceled MIL-STD 1629A used Failure Mode Effects and Criticality Analysis (FMECA) which is similar in method to FMEA but with an added factor called Criticality for prioritization. In FMEA approach, criticality is addressed by the Risk Priority Number (RPN) and other ways to prioritize risk beyond those single criteria. Tank Automotive Research Development and Engineering Center (TARDEC), Systems Engineering Group (SEG) has tailored the FMEA’s Severity, Occurrence, and Detection ranking tables to suit DOD Systems by developing an additional scale (1 – 5) for severity and occurrence parameters for the existing industry scale (1 – 10). This will facilitate transitioning risks
ABSTRACT Program offices and the test community all desire to be more efficient with respect to testing but currently lack the analytical tools to help them fit early subsystem level testing into a framework which allows them to perform assessments at the system level. TARDEC initiated a Small Business Innovative Research (SBIR) effort to develop and deploy a system reliability testing and optimization tool that will quantify the value of subsystem level tests in an overall test program and incorporate the results into system level evaluations. The concept software, named the Army Lifecycle Test Optimization (ALTO) tool, provides not only the optimization capability desired, but also other key features to quickly see the current status, metrics, schedule, and reliability plots for the current test plan. As the user makes changes to the test plan, either by running the optimization or adjusting inputs or factors, the impacts on each of these areas is computed and displayed
ABSTRACT This paper identifies the failure modes of military track bushings during lab testing and looks at correlation of lab tests failure modes with those found in field testing failures. In an effort to understand and duplicate the failures seen in the field, a track shoe was modified to measure the displacement (magnitude and direction) of the bushing pin relative to the inside diameter of the track shoe bore. Utilizing Hall Effect Technology and a small data acquisition system, test course data was recorded and analyzed. A specially designed bushing test machine, capable of testing the entire track pitch, was also designed and built in order to duplicate the field failure in a laboratory environment
ABSTRACT Use of Model-Based Design (MBD) processes for embedded controls software Development has been purported for nearly the last decade to result in cost, quality, and delivery improvements. Initially the business case for MBD was rather vague and qualitative in nature, but more data is now becoming available to support the premise for this development methodology. Many times the implementation of MBD in an organization is bundled with other software process improvements such as CMMI or industry safety standards compliance, so trying to unbundle the contributions from MBD has been problematic. This paper addresses the dominant factors for MBD cost savings and the business benefits that have been realized by companies in various industries engaged in MBD development. It also summarizes some key management best practices and success factors that have helped organizations achieve success in MBD deployment
ABSTRACT In today’s competitive market, OEMs are racing towards developing more efficient vehicles without sacrificing on its performance. In this process, they’re forced to evaluate new technologies and designs in various subsystems. Most of the sub-systems today have become “intelligent”, which means that the controllers have become quintessential for the system’s behavior. Equally important are the physical behavior of the plant that needs to be controlled. These two independent groups have their own design and development cycle and the challenge for the companies have been in bridging the gap so as to identify potential failure modes. This paper discusses an Architecture-driven Model Based Development process that can address the challenges posed during the development. Three key enabling technologies – Imagine.Lab System Synthesis, Imagine.Lab SysDM & Imagine.Lab AMESim are leveraged in this process
ABSTRACT High life cycle costs coupled with durability and environmental challenges of tracked vehicles in South West Asia (SWA) have focused R&D activities on understanding failure modes of track components as well as understanding the system impacts on track durability. The durability limiters for M1 Abrams (M1, M1A1, and M1A2) T-158LL track systems are the elastomeric components. The focus of this study is to review test methodology utilized to collect preliminary data on the loading distribution of a static vehicle. Proposed design changes and path forward for prediction of durability of elastomers at the systems level from component testing will be presented
ABSTRACT The use of lead-free components in electronic modules destined for defense applications requires a deep understanding of the reliability risks involved. In particular, pad cratering, tin whiskers, shock and vibration, thermal cycling and combined environments are among the top risks. Testing and failure analysis of representative assemblies across a number of scenarios, including with and without risk mitigations, were performed to understand reliability of lead-free assembly approaches, in comparison with leaded and mixed solder approaches. The results lead to an understanding of lead-free reliability and how to improve it, when required. This outcome is resulting in user acceptance of lead-free electronics, which is timely given the increasing scope of lead-free legislation
ABSTRACT Traditionally, the life cycle management of military vehicle fleets is a lengthy and costly process involving maintenance crews completing numerous and oftentimes unnecessary inspections and diagnostics tests. Recent technological advances have allowed for the automation of life cycle management processes of complex systems. In this paper, we present our process for applying artificial intelligence (AI) and machine learning (ML) in the life cycle management of military vehicle fleets, using a Ground Vehicle fleet. We outline the data processing and data mapping methodologies needed for generating AI/ML model training data. We then use AI and ML methods to refine our training sets and labels. Finally, we outline a Random Forest classification model for identifying system failures and associated root causes. Our evaluation of the Random Forest model results show that our approach can predict system failures and associated root causes with 96% accuracy
ABSTRACT In this study, a styrene butadiene rubber, which is similar to the rubber used in road wheel backer pads of tracked vehicles, was investigated experimentally under monotonic and fatigue loading conditions. The monotonic loading response of the material was obtained under different stress states (compression and tension), strain rates (0.001/s to 3000/s), and temperatures (-5C to 50C). The experimental data showed that the material exhibited stress state, strain rate and temperature dependence. Fatigue loading behavior of the rubber was determined using a strain-life approach for R=0.5 loading conditions with varying strain amplitudes (25 to 43.75 percent) at a frequency of 2 Hz. Microstructural analysis of specimen fracture surfaces was performed using scanning electron microscopy and energy dispersive x-ray spectroscopy to determine the failure mechanisms of the material. The primary failure mechanisms for both loading conditions were found to be the debonding of particles on
Summary Combat vehicle designers have made great progress in improving crew survivability against large blast mines and improvised explosive devices. Current vehicles are very resistant to hull failure from large blasts, protecting the crew from overpressure and behind armor debris. However, the crew is still vulnerable to shock injuries arising from the blast and its after-effects. One of these injury modes is spinal compression resulting from the shock loading of the crew seat. This can be ameliorated by installing energy-absorbing seats which reduce the intensity of the spinal loading, while spreading it out over a longer time. The key question associated with energy-absorbing seats has to do with the effect of various factors associated with the design on spinal compression and injury. These include the stiffness and stroking distance of the seat’s energy absorption mechanism, the size of the blast, the vehicle shape and mass, and the weight of the seat occupant. All of these
ABSTRACT All CBM+ solutions must establish a business case considering cost of implementation and sustainment of value with a quantifiable return on investment. The business case must be traceable to specific failure modes, associated failure effects, criticality, and risk. Risk is not limited to safety and operational risks. Predictive systems by definition return both true and false predictions representing operational and financial risk from high false positive rates. There is also risk of losing operator confidence in predictive systems when there is a high false positive rate. All of these risks must be quantified and considered in the design and development of CBM+ systems. Model based approaches are effective in accelerating development, defining advanced functional characteristics, and efficiently testing dynamic effects of complex systems. CBM+ maintenance strategies rely on performance of complex systems
ABSTRACT Traditional engineering concerns such as lubrication and cooling are still present even as vehicle functions become more complex. The established solution to monitor fluid levels has been a sight glass or a dipstick. More complex machines demand continuous knowledge of fluid levels without adding to operator workload. Remote monitoring of vehicle health will become normal and expected by owners and operators of evolving vehicle designs. This dual function fluid level sensor provides both electronic and operator monitoring of vehicle fluids, as well as redundancy in the event of electronic failure. Grouping of sensor components that are considered more likely to fail into one group, aids replacement when necessary. By incorporating a traditional dipstick into a continuous electronic monitoring solution, either method of level monitoring is facilitated
ABSTRACT Situation: There are many advantages during development of a design that come from doing Design Failure Mode Effects Analysis (DFMEA). These advantages include more reliable, safer, self-diagnosing, designs with higher Availability. Strictly from a Design for Reliability (DFR) viewpoint, DFMEA is the key tool to; a. identify and prioritize most critical potential Failure Modes (FMs) of the design, before design development, b. Document critical FM effects and root causes, and c. facilitate corrective actions and DVP&R planning, and d. form a reliability model which can be used to track reliability over the life of the design. Problem: Since even small and simple designs often have a few hundred potential failure modes, preparing a good DFMEA is always a problem of Effectiveness vs., Efficiency. Traditionally it has been very hard to achieve Effectiveness when limited time, money and resources are available and the push for Efficiency, speed or deadlines, causes critical FMs to
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