Sharon Monica Jones

Predictive safety analytics: inferring aviation accident shaping factors and causation

This paper illustrates the development of an object-oriented Bayesian network (OOBN) to integrate the safety risks contributing to an in-flight loss-of-control aviation accident. With the creation of a probabilistic model, inferences about changes to the states of the accident shaping or causal factors can be drawn quantitatively. These predictive safety inferences derive from qualitative reasoning to conclusions based on data, assumptions, and/or premises, and enable an analyst to identify the most prominent causal factors leading to a risk factor prioritization. Such an approach facilitates a mitigation portfolio study and assessment. The model also facilitates the computation of sensitivity values based on perturbations to the estimates in the conditional probability tables. Such computations lead to identifying the most sensitive causal factors with respect to an accident probability. This approach may lead to vulnerability discovery of emerging causal factors for which mitigations do not yet exist that then informs possible future R&D efforts. To illustrate the benefits of an OOBN in a large and complex aviation accident model, the in-flight loss-of-control accident framework model is presented.

A RISK-BASED DECISION SUPPORT TOOL FOR EVALUATING AVIATION TECHNOLOGY INTEGRATION IN THE NATIONAL AIRSPACE SYSTEM

Commercial aviation, one of the most critical national and international modes of transport, is a highly complex, dynamic domain. From a systems perspective, there are numerous interrelated infrastructural components and stakeholders that challenge analytical modeling. Perhaps more than any other domain, aviation is typically on the forefront of developing and applying new technologies. The Aviation System Risk Model (ASRM) is a risk-based decision support system prototype designed to evaluate the impacts of new safety technologies/ interventions. The process utilizes an analytic generalization framework to develop an integrated approach to model the complex interactions of causal factors. Bayesian probability theory is being used for model quantification and Bayesian decision theory provides an analytical method to evaluate the possible impacts of new interventions. The entire process is supported by expert judgments. Subsequently, the analytical methodology is encoded as a Probabilistic Decision Support System (PDSS). The resultant PDSS is a riskinformed decision support tool that aids the evaluation of the possible relative impact of single as well as multiple technologies on aviation safety system risk. Presenting a maintenance-related accident scenario provides an illustration of the possible use of the PDSS.

AN OVERVIEW OF THE NASA AVIATION SAFETY PROGRAM ASSESSMENT PROCESS

The objectives of the NASA Aviation Safety Program (AvSP) are (1) to develop and demonstrate technologies that reduce aircraft accident rate and (2) to develop technologies that reduce aviation injuries and fatalities when accidents do occur. The primary goal of Program Assessment is to examine the set of products in the AvSP portfolio to determine their projected impact on increasing aviation safety. This portfolio analysis is conducted using the following metrics: technical risk, implementation risk, fatal accident rate, safety benefits/cost and projected impact on safety risk. This paper provides an overview of the process that will be used for the final assessment of the NASA Aviation Safety Program.

Automated anomaly detection for orbiter high-temperature reusable surface insulation

The description, analysis, and experimental results of a method for identifying possible defects on high temperature reusable surface insulation (HRSI) of the Orbiter thermal protection system (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.