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This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. AS13002 defines the process for qualifying an Alternate Inspection Frequency Plan for suppliers within the aero-engine sector.  This two-day course will provide common requirements for developing and qualifying an alternate inspection plan, other than 100% inspection of all features.  This course is designed to cover the basic elements of the process to be applied to design characteristics (as defined in AS9102), and parts or inspection processes as defined by the purchaser.

Active safety and (ADAS) are now being introduced to the marketplace as they serve as key enablers for anticipated autonomous driving systems. Automatic emergency braking (AEB) is one ADAS application which is either in the marketplace presently or under development as nearly all automakers have pledged to offer this technology by the year 2022. This one-day course is designed to provide an overview of the typical ADAS AEB system from multiple perspectives.

This course will introduce participants to industry best practices for real-world aviation cyber-security risk-assessment, development & assurance. Participants will learn the information necessary to help minimize DO-326/ED-202-set compliance risks and costs, while also optimizing cyber-security levels for the development, deployment and in-service phases Topics such as aircraft security aspects of safety, systems-approach to security, security planning, the airworthiness security process, and security effectiveness assurance will be covered.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004.

The UN R155 regulation is the first automotive cyber security regulation and has made security a mandatory approval criterion for new vehicle types. This establishes internationally harmonized security requirements for market approval. As a result, the application of the regulation presents manufacturers and suppliers with the challenge of demonstrating compliance. At process level the implementation of a Cyber Security Management System (CSMS) is required while at product level, the Threat Assessment and Risk Analysis (TARA) forms the basis to identify relevant threats and corresponding mitigation strategies. Overall, an issued type approval is internationally recognized by the member states of the UN 1958 Agreement. International recognition implies that uniform assessment criteria are applied to demonstrate compliance and to decide whether security efforts are sufficient.

The finite element method is one of the most robust tools in structural analysis. Typically, the input parameters in a finite element model are assumed to be deterministic. However, in practice, almost all material and geometrical properties, including the load, possess randomness. The consideration of the probabilistic nature of these quantities is essential to effectively designing a system that is robust against the uncertainties arising due to the variation in the input parameters, the significance of which has been documented by NASA in “Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners”, 2011. Among the various techniques applicable for stochastic analysis, the perturbation method, which is based on a sound mathematical foundation derived from Taylor’s series expansion, is widely acknowledged for its much higher efficiency compared to the well-known Monte-Carlo method.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

In order to improve the obstacle avoidance ability of autonomous vehicles in complex traffic environments, speed planning, path planning, and tracking control are integrated into one optimization problem. An integrated vehicle trajectory planning and tracking control method combining a pseudo-time-to-collision (PTC) risk assessment model and model predictive control (MPC) is proposed. First, a risk assessment model with PTC probability is proposed by considering the differentiation of the risk on the relative motion states of the self and front vehicles, and the obstacle vehicles in the lateral and longitudinal directions. Then, a three-degrees-of-freedom vehicle dynamics model is established, and the MPC cost function and constraints are constructed from the perspective of the road environment as well as the stability and comfort of the ego-vehicle, combined with the PTC risk assessment model to optimize the control.

This document describes guidelines, methods, and tools used to perform the ongoing safety assessment process for transport airplanes in commercial service (hereafter, termed “airplane”). The process described herein is intended to support an overall safety management program. It is associated with showing compliance with the regulations, and also with assuring a company that it meets its own internal standards. The methods identify a systematic means, but not the only means, to assess ongoing safety.While economic decision-making is an integral part of the safety management process, this document addresses only the ongoing safety assessment process. To put it succinctly, this document addresses the “Is it safe?” part of safety management; it does not address the “How much does it cost?” part of the safety management.This document also does not address any specific organizational structures for accomplishing the safety assessment process.

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 to help a company establish and meet its own internal 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.

Abstract For taking counter measures in advance to prevent accidental risks, it is of significance to explore the causes and evolutionary mechanism of ship collisions. This article collects 70 ship collision accidents in Zhejiang coastal waters, where 60 cases are used for modeling while 10 cases are used for verification (testing). By analyzing influencing factors (IFs) and causal chains of accidents, a Bayesian network (BN) model with 19 causal nodes and 1 consequential node is constructed. Parameters of the BN model, namely the conditional probability tables (CPTs), are determined by mathematical statistics methods and Bayesian formulas. Regarding each testing case, the BN model’s prediction on probability of occurrence is above 80% (approaching 100% indicates the certainty of occurrence), which verifies the availability of the model. Causal analysis based on the backward reasoning process shows that H (Human error) is the main IF resulting in ship collisions.