Browse Topic: Risk assessments
Noise phenomena in automobiles caused by the stick-slip effect are increasingly among the most frequent reasons for customer complaints and therefore represent a critical vehicle quality attribute. To proactively address such issues, stick-slip testing of contacting material pairs is commonly applied during development. However, the predictive capability of current stick-slip test methods remains limited, particularly when highly flexible materials and realistic, stochastic excitation conditions are involved. The flexibility of sealing systems often allows the actual relative motion at the contact interface to be accommodated through adhesion and elastic deformation, thereby delaying or even preventing sliding. To date, this effect has not been represented by any characteristic parameter in conventional stick-slip testing. Instead, existing evaluations focus exclusively on the analysis of occurring stick-slip oscillations. For the initiation of stick-slip phenomena, however, not only
The intent of this standard is to establish a framework to assure that all evaporators conforming to its requirements demonstrate an acceptable health and safety environment for vehicle occupants as determined from the completed risk assessment. R-744 and low pressure (i.e., non-transcritical refrigerants with a critical temperature between 85 and 120 °C) mobile air conditioning (MAC) refrigerant evaporators shall meet the testing and labeling requirements of this standard. SAE J639 contains a list of all refrigerants considered acceptable for use in mobile thermal systems for which this standard applies when the refrigerant is used in a direct expansion architecture. SAE J639 also requires an assessment to be performed to minimize reasonable risks in MAC systems. The evaporator (as designed and manufactured) shall be part of that risk assessment, and it is the responsibility of the vehicle manufacturer to ensure all relevant aspects of the evaporator are included. It is the
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
In this Q&A, Audrey Turley, director of lab operations – biosafety at Nelson Laboratories, spoke with Medical Design Briefs about the critical importance of monitoring and managing material changes in medical devices. Even seemingly minor shifts — such as switching suppliers or altering processing steps — can introduce unknown additives or variations that impact biocompatibility and, ultimately, patient safety. Turley discusses how manufacturers can effectively document and justify changes, maintain regulatory compliance, and strengthen supplier relationships to ensure ongoing device safety. She also shares insights into trends shaping post-pandemic supply-chain strategies and the growing emphasis on proactive risk assessment and communication across the product lifecycle.
Thermal runaway in lithium-ion batteries represents a critical safety challenge, particularly in high-voltage battery systems used in electric vehicles and stationary energy storage. A comprehensive understanding of the multi-scale processes that initiate and propagate thermal runaway is essential for the development of effective safety measures and design strategies. This study provides a structured theoretical overview of the thermal runaway phenomenon across four hierarchical levels: electrode, single cell, module, and high-voltage battery system. At the electrode level, thermal runaway initiation is linked to electrochemical and chemical degradation mechanisms such as solid electrolyte interphase decomposition, separator breakdown, and internal short circuits. These processes lead to highly exothermic reactions that, at the cell scale, can result in rapid temperature increases, gas generation, and overpressure. On the module and system levels, thermal runaway can propagate through
This standard is for use by organizations that procure and integrate EEE Parts. These organizations may provide EEE Parts that are not integrated into assemblies (e.g., spares and/or repair EEE Parts). Examples of such organizations include, but are not limited to, the following: Original Equipment Manufacturers; contract assembly manufacturers; maintenance, repair, and overhaul (MRO) organizations; and suppliers that provide EEE Parts or assemblies as part of a service. These requirements are intended to be applied (or flowed down as applicable) through the supply chain to all organizations that procure and integrate EEE Parts and/or systems, subsystems, or assemblies. The mitigation of Counterfeit EEE Parts in this standard is risk based. These mitigation steps will vary depending on the criticality of the application and desired performance and reliability of the equipment/hardware. The requirements of this document are used in conjunction with the organization’s higher-level
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable
This document describes a process that may be used to perform the ongoing safety assessment for (1) GAR aircraft and components (hereafter, “aircraft”), and (2) commercial operators of GAR aircraft. The process described herein is intended to support an overall safety management program. It is associated with showing compliance with regulations and also establishing and meeting internal company safety standards. The process described herein identifies a systematic means, but not the only means, to assess continuing airworthiness. Ongoing safety management is an activity dedicated to assuring that risk is identified and properly eliminated or controlled. The safety management process includes both safety assessment and economic decision-making. While economic decision-making (factors related to scheduling, parts, and cost) is an integral part of the safety management process, this document addresses only the ongoing safety assessment process. This ongoing safety assessment process
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
Aircraft Certification is a mature and complex bureaucracy that has successfully ensured a very high degree of safety of aircraft design, construction, operation and maintenance. Outside of a very few doing the work, there is a general lack of knowledge of certification details. For novel technologies such as electric power, and innovative configurations such as multi-rotors, the rules are far less mature and still emerging and so also poorly understood. Within the Advanced Air Mobility (AAM) initiative, many new aircraft developments are underway using novel configurations, and the public announcements of regulatory progress toward FAA or EASA Type Certification capitalize on this ignorance by being vague or even misleading. Honeywell conceived the Regulatory Readiness Level (RRL) indicator as an objective measure of certification status to serve the AAM industry and ecosystem, with applicability across aviation. The released RRL Version 1 now enables credible, objective assessment of
Airworthiness certification of aircraft requires an Airworthiness Security Process (AWSP) to ensure safe operation under potential unauthorized interactions, particularly in the context of growing cyber threats. Regulatory authorities mandate the consideration of Intentional Unauthorized Electronic Interactions (IUEI) in the development of aircraft, airborne software, and equipment. As the industry increasingly adopts Model-Based Systems Engineering (MBSE) to accelerate development, we aim to enhance this effort by focusing on security scope definitions – a critical step within the AWSP for security risk assessment that establishes the boundaries and extent of security measures. However, our findings indicate that, despite the increasing use of model-based tools in development, these security scope definitions often remain either document-based or, when modeled, are presented at overly abstract levels, both of which limit their utility. Furthermore, we found that these definitions
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