Browse Topic: Failure modes and effects analysis (FMEA)
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 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 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 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 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 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 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 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 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
This SAE Aerospace Standard (AS) defines the requirements for air cycle air conditioning systems used on military air vehicles for cooling, heating, ventilation, and moisture and contamination control. General recommendations for an air conditioning system, which may include an air cycle system as a cooling source, are included in MIL-E-18927E and JSSG-2009. Air cycle air conditioning systems include those components which condition high temperature and high pressure air for delivery to occupied and equipment compartments and to electrical and electronic equipment. This document is applicable to open and closed loop air cycle systems. Definitions are contained in Section 5 of this document
The purpose of air conditioning (AC) duct packing is multifaceted, serving to prevent condensation, eliminate rattle noise, and provide thermal insulation. A critical aspect of duct packing is its adhesive quality, which is essential for maintaining the longevity and effectiveness of the packing's functions. Indeed, the challenge of achieving adequate adhesivity on AC ducting parts is significant due to the harsh operating conditions to which these components are subjected. The high temperatures and presence of condensation within the AC system can severely compromise the adhesive's ability to maintain a strong bond. Moreover, the materials used for these parts, such as HDPE, often have low surface energy, which further hinders the formation of a durable adhesive bond. The failure of the adhesive under these conditions can lead to delamination of the duct packing, which can result in customer inconvenience due to rattling noises, potential electrical failures if condensed water
This standard defines requirements for the identification, assessment, mitigation, and prevention of risk in the manufacturing process through the application of Process Flow Diagrams (PFDs), Process Failure Mode and Effects Analysis (PFMEA) and Control Plans throughout the life cycle of a product. This standard aligns and collaborates with the requirements of AS9100, AS9102, AS9103, and AS9145. The requirements specified in this standard apply in conjunction with and are not alternative to contractual and applicable statutory and regulatory requirements. In case of conflict between the requirements of this standard and applicable statutory or regulatory requirements, the latter shall take precedence
Verification and validation (V&V) is the cornerstone of safety in the automotive industry. The V&V process ensures that every component in a vehicle functions according to its specifications. Automated driving functionality poses considerable challenges to the V&V process, especially when data-driven AI components are present in the system. The aim of this work is to outline a methodology for V&V of AI-based systems. The backbone of this methodology is bridging the semantic gap between the symbolic level at which the operational design domain and requirements are typically specified, and the sub-symbolic, statistical level at which data-driven AI components function. This is accomplished by combining a probabilistic model of the operational design domain and an FMEA of AI with a fitness-for-purpose model of the system itself. The fitness-for-purpose model allows for reasoning about the behavior of the system in its environment, which we argue is essential to determine whether the
The Aerospace Industry's drive towards zero defects has seen a significant shift to prevent defects and improve product quality during the design phase, instead of waiting until post-production inspection to discover and troubleshoot problems. Trying to ensure zero defects during the post-production inspection phase is too late in the product life cycle because it can lead to substantial costs. Aerospace Engine Supplier Quality (AESQ) introduced the Advanced Product Quality Planning (APQP) [2] process to realize zero defects. In APQP Phase 2 [2], Product and Design Development, a key output is performing a Design Failure Modes and Effects Analysis (DFMEA). Moog has effectively implemented a DFMEA process that adeptly identifies and mitigates design risks. This work showcases Moog's successful deployment of DFMEA, exemplifying the industry best practices. This work also presents simplified and innovative interpretations of DFMEA definitions and approaches. By addressing defects during
The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements gives rise to the need for increased efficiency and power density of the motors in the Electric Drive Unit (EDU). Internal Permanent Magnet (IPM) motor is one of the best suited options in such scenarios because of its primary advantages of higher efficiency and precise control over torque and speed. In the IPM motor, permanent magnets are mounted within the rotor body to produce a resultant rotating magnetic field with the 3-phase AC current supply in the stator. IPM configuration provides structural integrity and high dynamic performance as the magnets are inserted within the rotor body. Adhesive glue is used to install the magnets within the laminated stack of rotor. High rotational speed of rotor introduces centrifugal loading on the magnets which can result in multiple failure modes such as the debonding of the magnet, and high radial
The global electric and hybrid aircraft market utilizing lithium-ion Energy Storage Systems (ESS) as a means of propulsion, is experiencing a period of extraordinary growth. We are witnessing the development of some of the most cutting-edge technology, and with that, some of the most complex challenges that we as an industry have ever faced. The primary challenge, and the most critical cause of concern, is a phenomenon known as a “Thermal Runaway”, in which the lithium-ion cell enters an uncontrollable, self-heating state, that if not contained, can propagate into a catastrophic fire in the aircraft. A Thermal Runaway (TR) can be caused by internal defects, damage, and/or abuse caused by an exceedance of its operational specifications, and it is a chemical reaction that cannot be stopped once the cell has reached its trigger temperature. There are many technical papers that explore the characteristics of battery cells and the TR as a failure mode, but the failure mechanism(s) are still
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