Browse Topic: Failure analysis
This paper presents the first systematic examination of Large Language Model (LLM) capabilities for automating the development of Failure Mode and Effects Analysis (FMEA) utilizing architectural diagrams as input. Although prior research has examined LLMs for FMEA tasks, our methodology incorporates innovative aspects, such as the direct analysis of architectural diagrams for component extraction, prediction of failure modes, causes, estimation of risk and a human-in-the-loop (Hu-IL) validation framework. We examine the capability of general-purpose LLMs to accurately automate the creation of FMEA by formulating a methodology that extracts components and signals from architectural diagrams, conducts automated component classification, and produces a comprehensive FMEA form sheet encompassing Severity, Occurrence, and Detectability (S/O/D) scoring. Our methodology is grounded in structured prompt engineering theory, utilizing scope bounding techniques to reduce hallucination while
Due to the spot weld and mechanical fastener share the similar characteristics to join sheets together with differences in deformation behavior around joint region, a novel spot joint element (user-defined element) consists of regular Mindlin shell elements and equations for different kinematic constraints is proposed to simplify the spot joint representation in lightweight automotive structures. The novel spot joint element can not only provide accurate deformation behavior around joint region but also output mesh-insensitive structural stresses at virtual nodes with the use of traction-based structural stress method for fatigue failure analysis. In this investigation, the structural stress distributions around joint circumference in the lap-shear specimens with spot weld or fastener are first calculated to validate the accuracy of the novel spot joint element. Then, the structural stresses along different cross-sections emanating from joint are also calculated for the specimens with
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with the widely used Si-IGBT. However, SiC MOSFET is a more expensive device, installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET from over temperature. When a junction temperature estimation
Reliable monitoring of the internal state of lithium-ion batteries (LIBs) is crucial for mitigating potential safety hazards. The incorporation of a reference electrode (RE) within the battery constitutes a vital approach for achieving single-electrode monitoring and understanding changes in electrode state during cycling. Among these, the lithium-copper reference electrode (Li-Cu RE) is particularly cost-effective and straightforward to prepare, being fabricated by depositing lithium onto a copper wire. However, Li-Cu RE exhibits a relatively short effective lifespan during long-term cycling, thereby limiting its practical application. In this work, based on a self-fabricated three-electrode single-layer pouch cell, the microstructural changes before and after failure of the Li-Cu RE were characterized and analyzed, revealing its failure evolution process. Post-failure microstructures observations exhibit marked porosity in the electrode, attributed to substantial depletion of surface
This paper carried out the fire failure analysis of valve-regulated lead-acid battery in communication equipment room. Through disassembly and observation of the battery and iron frame of battery cabinet in the area of fire origin, we obtained the key residual traces and used the physical and chemical analysis methods such as macroscopic/microscopic morphology, EDS, X-ray and metallographic, it was finally judged that the leakage of the battery electrolyte lead to the connection of the battery electrode plate and the iron frame and subsequently the electric heating fault caused the fire accident. Furthermore, we put forward some suggestions according to the existing problems, which may contribute to the prevention of similar failures.
With the growing global demand for sustainable energy and high-performance mobile devices, lithium metal solid-state batteries (LMBs) have emerged as a research hotspot in the field of energy storage due to their exceptional high energy density and significant safety advantages. However, the growth of lithium dendrites and their penetration through the solid electrolyte remain key issues leading to battery short-circuiting and failure. To date, there has been a lack of effective in situ research methods to reveal the failure mechanisms, which has severely restricted the commercialization of LMBs. This study innovatively employs in situ electrochemical impedance spectroscopy (EIS) to investigate lithium plating behavior in symmetric cells during critical current density (CCD) tests under room temperature and elevated temperature conditions. By analyzing characteristic signals at 1 MHz, this study presents the in situ impedance changes at the grain boundaries and interfaces of the
The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.
For any fleet or logistics manager, the specter of a downed Class 8 truck is a constant concern. The costs aren't just in parts and labor; they're in lost productivity, missed deadlines and potential damage to your reputation. While many factors can sideline a heavy-duty vehicle, one of the most persistent and costly culprits is hydraulic system failure. These failures often trace back to a single, preventable issue: contamination.
How engineers can ensure safety, reliability and quality in aerospace systems. Courbevoie, Île-de-France In an industry where failure is not an option and precision is paramount, aerospace manufacturers and suppliers are constantly seeking components and system solutions that deliver trusted reliability, performance, and compliance. Industry standards are a key part of achieving these high expectations, bringing together global leaders in the mobility industries to create defined, repeatable methods and consistent processes. One of these aerospace standards is AS1895 developed by SAE International - a critical standard due to the need for durable components that can withstand extreme conditions and offer high performance: high-temperature resistance, pressure sealing, and long service life with a cost-effective installation method. Leading aerospace companies such as Eaton and Honeywell have been manufacturing components that meet this standard for a long period of time.
As electric vehicles (EVs) become more advanced, so ensuring the reliability of critical components like the motor and Motor Control Unit (MCU) is essential. This paper presents a digital twin model designed to predict failures in motor and MCU components using machine learning. The approach focuses on detecting early signs of failure through real-world data and advanced analytics. We collected thermal and performance data from field vehicles, capturing both normal (healthy) and abnormal (faulty) operating conditions. Using this dataset, we developed and trained an Auto Encoder-based machine learning model that learns what “normal” looks like and flags deviations as potential issues. One key outcome of this study is the successful early prediction of Insulated Gate Bipolar Transistor (IGBT) degradation, where the system identified subtle behavioral changes long before any visible failure symptoms appeared. This digital twin acts as a virtual replica of the physical components
This study addresses the challenge of ensuring the durability of closed couple exhaust manifolds in the compact engine bays of modern vehicles, focusing on a longitudinally mounted 1.2L 4-cylinder engine. The original sheet metal Exhaust manifold design failed the thermal fatigue bench durability test, requiring a complete redesign to improve strength without changing materials. Initial simulation predictions significantly deviated from physical test results, with repeated cracks observed during accelerated thermal fatigue bench testing, despite simulations predicting a higher number of cycles before failure. This difference highlighted the need for a deeper understanding of the manifold's failure modes, primarily thermal fatigue, and mechanical vibration during engine transients. The design of experiment (DOE) approach was used to find the effect of different parameters e.g., gas temperature, surface temperature, air flow, thermal gradient, on the durability result & also to
Electric vehicles present unique challenges in electromagnetic compatibility testing due to compact packaging, high-frequency switching systems. This paper presents a systematic debugging methodology for identifying radiated emission and radiated immunity issues in these EV platforms. A comprehensive approach is outlined, covering radiated emission measurement; Bulk Current Injection based immunity simulation, and near-field probing techniques. For RI evaluation, BCI testing in the 20 to 400 MHz range is used to simulate radiated threats on the vehicle's power and signal harnesses and handy transmitter near field injections for higher frequency simulation. For RE diagnosis, conducted emission measurements on vehicle harnesses are performed using current probes to capture high-frequency currents. Additionally, near-field electric probes are used at the component to identify dominant noise sources such as DC-DC converters, Motor control unit, and improperly grounded shielding. Case
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