Browse Topic: Failure modes and effects analysis (FMEA)
Opening a tailgate can cause rain that has settled on its surfaces to run off onto the customer or into the rear loadspace, causing annoyance. Relatively small adjustments to tailgate seals and encapsulation can effectively mitigate these effects. However, these failure modes tend to be discovered relatively late in the design process as they, to date, need a representative physical system to test – including ensuring that any materials used on the surface flow paths elicit the same liquid flow behaviours (i.e. contact angles and velocity) as would be seen on the production vehicle surfaces. In this work we describe the development and validation of an early-stage simulation approach using a Smoothed Particle Hydrodynamics code (PreonLab). This includes its calibration against fundamental experiments to provide models for the flow of water over automotive surfaces and their subsequent application to a tailgate system simulation which includes fully detailed surrounding vehicle geometry
The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements, arises the need for increased electric current supply to motors. Increased amperage through the stator causes higher losses resulting in elevated temperature across the motor components and its housing. In most of the cases, stator is mounted on the housing through interference fit to avoid any slippage during operation conditions. High temperature across the stator and housing causes significant thermal expansions of the components which is uneven in nature due to the differences in corresponding coefficient of thermal expansion (CTE) values. Housings are generally made of aluminium and tends to expand more having higher value of CTE than that of steel core of stator which may give rise to a failure mode related to stator slippage. To address this slippage if the amount of interference fit is increased, that’ll result in another failure mode
Designing for the durability of motor vehicles requires accounting for various stress factors, including tractive loads, electrical loads, thermal loads, and structural loads. For electric vehicle propulsion systems, it is crucial to consider not just the magnitude and repeats of these loads but also their temporal sequence throughout the vehicle’s lifespan. The order and timing of these loads influence factors such as, charge and discharge cycles or active motor heating, which ultimately impact the damage to the propulsion system components like the cell and the motor. Traditionally, lifetime loads for durability assessments are derived from a single-user load profile consisting of a set of ‘representative’ drive cycles accounting for the cumulative damage equivalent to the real-world damage covered under warranty. This profile is typically based on historical usage data, user scenarios, and industry experience, but may not capture the diverse failure modes of the different propulsion
A 20-cell self-humidifying fuel cell stack containing two types of MEAs was assembled and aged by a 1000-hour durability test. To rapidly and effectively analyze the primary degradation, the polarization change curve is introduced. As the different failure modes have a unique spectrum in the polarization change curve, it can be regarded as the fingerprint of a special degradation mode for repaid analysis. By means of this method, the main failure mode of two-type MEAs was clearly distinguished: one was attributed to the pinhole formation at the hydrogen outlet, and another was caused by catalyst degradation only, as verified by infrared imaging. The two distinct degradation phases were also classified: (i)conditioning phase, featuring with high decay rate, caused by repaid ECSA change from particle size growth of catalyst. (ii) performance phase with minor voltage loss at long test duration, but with RH cycling behind, as in MEA1. Then, an effective H2-pumping recovery is conducted
This document presents minimum criteria for the design and installation of LED assemblies in aircraft. The use of "shall" in this specification expresses provisions that are binding. Nonmandatory provisions use the term "should."
SBW(Steer-by-wire) is a steering system that transmits the driver’s request and gives feedback to the driver through electrical signals. This system eliminates the mechanical connection of the traditional steering system, and can realize the decoupling of the steering wheel and the road wheel. In addition, this system has a perfect torque feedback system, which can accurately and delicately feedback the road surface information to the driver. However, vehicle driving deviation is one of the most common failure modes affecting vehicle performance in the automotive aftermarket, this failure mode can exacerbates tire wear, reducing their life cycle, at the same time, the driver must apply a counter torque to the steering wheel for a long time to maintain straight-line travel during driving. This increases the driver’s operational burden and poses safety hazards to the vehicle’s operation. Based on the steer-by-wire system and vehicle driving deviation characteristics, this paper proposes
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
Automobiles are incorporated with advanced technologies to improve riding experience, safety, and vehicle management. Considering riding experience, major concern prevails in starting and charging system. For quick start and stop, implemented Integrated Starter Generator (ISG) in two wheelers. The ISG system consists of an ISG machine and ISG controller. ISG machine acts as motor during cranking and generator during charging, controlled by ISG controller. Automation kit is made with the help of real sensors, actuators, and microcontroller to monitor and log the performance characteristics of ISG system during te sting in rig level. Sensors continuously monitor the performance parameters and once the parameters are not meeting the specification, actuators stop the testing and raise the indication. All tested data are stored in cloud and taken for analysis. This automation kit served two purposes. One is eliminated test running on the failure sample for full long testing duration. Second
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