System Safety Modeling of Alternative Geofencing Configurations for small UAS

As is well known, the integration of small Unmanned Aircraft Systems (sUAS) or “drones” into the National Airspace System (NAS) has captured significant industry, academic, regulatory and media attention. For sUAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied from a system safety perspective to identify possible hazards and to assess mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. Using inductive logic, the ASRM may be used to evaluate underlying causal factors linked to the air vehicle and/or to the systems and procedures that lead to the unsafe state and the probabilistic interactions among these factors that contribute to the safety risk. The ASRM can also assess the projected impact of mitigations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides an analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a notional scenario that involves a sUAS being used for aerial surveillance in the siting of a wind turbine farm near the Yukon River in Alaska. It is conjectured that the sUAS interacts with a general aviation aircraft flying in the nearby vicinity from a local airport. The sUAS being used is a fixed wing-type where there is a failure of the separation assurance function since the sUAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. In the modeling approach, the time-dependent effects of wind velocity, wind sensor faults, and wind sensor accuracy are also included. In particular, the system safety study focuses on investigating the mitigating efficacy of alternative geofencing configurations

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper

Early Afternoon Concurrent Sessions: Critical Issues: Presentation: System Safety Modeling of Alternative Geofencing Configurations for Small UAS

As is well known, the integration of small Unmanned Aircraft Systems (sUAS) or “drones” into the National Airspace System (NAS) has captured significant industry, academic, regulatory and media attention. For sUAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied from a system safety perspective to identify possible hazards and to assess mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. Using inductive logic, the ASRM may be used to evaluate underlying causal factors linked to the air vehicle and/or to the systems and procedures that lead to the unsafe state and the probabilistic interactions among these factors that contribute to the safety risk. The ASRM can also assess the projected impact of mitigations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides an analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a notional scenario that involves a sUAS being used for aerial surveillance in the siting of a wind turbine farm near the Yukon River in Alaska. It is conjectured that the sUAS interacts with a general aviation aircraft flying in the nearby vicinity from a local airport. The sUAS being used is a fixed wing-type where there is a failure of the separation assurance function since the UAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. In the modeling approach, the time-dependent effects of wind velocity, wind sensor faults, and wind sensor accuracy are also included. In particular, the system safety study focuses on alternative geofencing mitigations proposed by Atkins (2014) such as using a single onboard processor that integrates the datalink, autopilot and geofencing functions vs. the use of a separate processor solely for the geofencing function. The alternative geofencing configurations are modeled as separate “objects” or sub-nets in a fault tree-type analysis. The geofencing fault tree analyses can then be iteratively linked with the top-level network to comparatively assess the efficacies of the alternative geofencing configurations on reducing the likelihood of the sUAS leaving its’ AO. The ASRM safety risk results for the notional scenario are presented and interpreted. It is suggested that the safety risk model may also be used to strategically assess alternative “assured containment” concepts as posited by Hayhurst et al. (2015). References: Atkins, E.M. (2014), “Autonomy as an enabler of economically-viable, beyond-line-of-sight, low-altitude UAS application with acceptable risk,” AUVSI Unmanned Systems, Orlando, FL, May 12-15, pp. 200-211. Hayhurst, K.J., N.A. Neogi, and H.A. Verstynen (2015), “A Case Study for Assured Containment,” International Conference on Unmanned Aircraft Systems (ICUAS), Denver Marriott Tech Center, Denver, CO, June 9-2, pp. 260-268

Reliability curve fitting for aging helicopter components

[[abstract]]This paper presents a comparison of alternative reliability curve fitting techniques for components of two model types of helicopters. Both mathematical function-based and neural network models were investigated. Preliminary results suggest that the neural network models compare very favorably with standard curve fitting techniques, and may provide better curve fitting for component reliability data from sparse data sets where the hazard rates are either constant or monotonically increasing.[[notice]]補正完

Comparison of proportional hazards models and neural networks for reliability estimation

[[abstract]]Because of increased manufacturing competitiveness, new methods for reliability estimation are being developed. Intelligent manufacturing relies upon accurate component and product reliability estimates for determining warranty costs, as well as optimal maintenance, inspection, and replacement schedules. Accelerated life testing is one approach that is used for shortening the life of products or components or hastening their performance degradation with the purpose of obtaining data that may be used to predict device life or performance under normal operating conditions. The proportional hazards (PH) model is a non-parametric multiple regression approach for reliability estimation, in which a baseline hazard function is modified multiplicatively by covariates (i.e. applied stresses). While the PH model is a distribution-free approach, specific assumptions need to be made about the time behavior of the hazard rates. A neural network (NN) is particularly useful in pattern recognition problems that involve capturing and learning complex underlying (but consistent) trends in the data. Neural networks are highly non-linear, and in some cases are capable of producing better approximations than multiple regression. This paper reports on the comparison of PH and NN models for the analysis of time-dependent dielectric breakdown data for a metal-oxide-semiconductor integrated circuit. In this case, the NN model results in a better fit to the data based upon minimizing the mean square error of the predictions when using failure data from an elevated temperature and voltage to predict reliability at a lower temperature and voltage.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]E

Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft

[[abstract]]Currently under phase 2 development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) contains ‘alert’ indicators of aircraft safety performance that can signal potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one component of SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft and/or aircraft components/equipment. SPAS contains performance indicators to assist safety inspectors in diagnosing an airline's safety ‘profile’ compared with others in the same peer class. This paper details the development of SDR prediction models for the DC-9 aircraft by analyzing sample data from the SDR database that have been merged with aircraft utilization data. Both multiple regression and neural networks are used to create prediction models for the overall number of SDRs and for SDR cracking and corrosion cases. These prediction models establish a range for the number of SDRs outside which safety advisory warnings would be issued. It appears that a data ‘grouping’ strategy to create aircraft ‘profiles’ is very effective at enhancing the predictive accuracy of the models. The results from each competing modeling approach are compared and managerial implications to improve the SDR performance indicator in SPAS are provided.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]E