Browse Topic: Failure modes and effects analysis (FMEA)
Puddling is a crucial process in rice cultivation, involving the preparation of the soil in a flooded field to create a soft, muddy seedbed. There are two classifications for puddling: full cage and half cage. Full cage puddling involves replacing the rear wheels of the tractor with steel paddle wheels, which are used to till the rice paddies directly without any additional implement. In the half cage puddling, the rear wheels remain on the tractor, and a smaller cage or paddle wheel is attached to the outside. Considering the field size, the operator often releases the clutch very quickly after a speed or direction change. This generates torque spikes, which are harmful to Transmission Gears and Clutches. This can lead to gear teeth bending fatigue failure due to repeated higher bending stresses. In this paper, a study related to how to reduce overall product development time by simulating bending fatigue failure of gear in lab environment is presented. A systematic approach is used
Prognostics and Health Management (PHM) is framework for electrical/mechanical components in heavy machines represents a transformative approach that harnesses cutting-edge sensing technologies and analytics to predict and elevate reliability and efficiency of agricultural/construction machinery. By using advanced data collection and sophisticated analytics, PHM achieves real-time monitoring of critical performance parameters such as voltage, current, temperature, and operational cycles, along with field data mapped with GPS coordinates as well as environmental conditions. This capability allows for the early detection of anomalies and potential failures, thereby enhancing operational reliability. Data collected from the machine will be pushed to the server periodically and whenever any failure is detected advanced AI algorithms on machine and server will analyze the information and link to collected data which will be used to identify possible failures or assess the safety of the
Functional safety is driven by number of standards like in automotive its driven by ISO26262, in Aerospace its driven by DO-178C, and in Medical its driven by IEC 60601. Automotive electronic controllers must adhere to state-of-the-art functional safety standard provided by ISO26262. A critical functional safety requirement is the Fault Handling Time Interval (FHTI), which includes the Fault Detection Time Interval (FDTI) and Fault Reaction Time Interval (FRTI). The requirements for FHTI are derived from Failure Mode Effect Analysis (FMEA) conducted at the system level. Various fault categories are analyzed, including electrical faults (e.g., short to battery, short to ground, open circuits), systemic faults (e.g., sensor value stuck, sensor value beyond range), and communication faults (e.g., incorrect CAN message signal values). Controllers employ strategies such as debouncing and fault time maturity to detect these faults. Numerous FDTI requirements must be verified to ensure
Virtual reality (VR), Augmented Reality (AR) and Mixed reality (MR) are advanced engineering techniques that coalesces physical and digital world to showcase better perceiving. There are various complex physics which may not be feasible to visualize using conventional post processing methods. Various industrial experts are already exploring implementation of VR for product development. Traditional computational power is improving day-by-day with new additional features to reduce the discrepancy between test and CFD. There has been an increase in demand to replace actual tests with accurate simulation approaches. Post processing and data analysis are key to understand complex physics and resolving critical failure modes. Analysts spend a considerable amount of time analyzing results and provide directions, design changes and recommendations. There is a scope to utilize advanced features of VR, AR and MR in CFD post process to find out the root cause of any failures occurred with
This manuscript presents a comprehensive study on the integration of Safety Analyses with Technical Safety Requirements (TSRs) to enhance functional safety in complex automotive systems and off-highway applications. It emphasizes the importance of systematically identifying potential hazards and translating them into precise, actionable TSRs that guide the design, implementation, and validation of safety-critical systems. By aligning safety analysis techniques—such as Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA)—with ISO 26262, the study demonstrates how safety goals can be effectively transformed into technical specifications that ensure robust system behavior under fault conditions. Part 1 outlines the use of Failure Modes and Effects Analysis (FMEA) to identify potential failure modes and single point faults across system, subsystems, and components. FMEA assesses the severity, likelihood, and detectability of these failures, guiding the development of
Eaton's decompression engine braking technology for medium and heavy-duty diesel engines delivers high braking power and provides several advantages to the commercial truck owner. Eaton offers rocker arm-based 1 stroke, 1.5 stroke, and 2 stroke systems for overhead cam and cam in block engine architectures. The Compression Release (CR) engine brake avoids overheating and fading of primary friction brake. It reduces or eliminates the need for a driveline retarder. One of the failure modes for Engine Brake (EB) system is excessive lateral displacement of the exhaust valve, caused by non-uniform pressure distribution across the valve during Brake Gas Recirculation (BGR) and Compression Release modes. This excessive deformation is referred to as Valve Wagging. Valve wagging significantly affects the structural stability of the engine brake mechanism. Analyzing its behavior is essential to minimize excessive wear on valve guide and Valve Seat Insert in new designs. Since evaluating the
This SAE Standard applies to equipment to be used with R-1234yf refrigerant only. It establishes requirements for equipment used to recharge R-1234yf to an accuracy level that meets Section 9 of this document and purity levels defined in SAE J2099. Refrigerant service equipment is required to ensure adequate refrigerant recovery to reduce emissions and provide for accurate recharging of mobile air-conditioning systems. Equipment shall be certified to meet all performance requirements outlined in this document and international/regional construction and safety requirements as outlined in this document.
In the era of Industry 4.0, the maintenance of factory equipment is evolving with new systems using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, Artificial Intelligence, and machine learning techniques to detect early signs of faults, types of faults, locations of faults, etc. Bearings and gears are among the most common components, and cracking, misalignment, rubbing, and bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated machine learning-based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system, consisting of the gears, bearings, shafts, and housing, was developed as a co-simulation between MSC ADAMS (dynamic simulation tool) and MATLAB (Mathematical tool). The gear and bearing models were developed mathematically, while
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
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
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
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
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