Aerodynamic stall based loss of control in flight is a major cause of fatal flight accidents. In a typical takeoff, a flight's airspeed continues to increase as it gains altitude. However, in some cases, the airspeed may drop immediately after takeoff and when left uncorrected, the flight gets close to a stall condition which is extremely risky. The takeoff is a high workload period for the flight crew involving frequent monitoring, control and communication with the ground control tower. Although there exists secondary safety systems and specialized recovery maneuvers, current technology is reactive; often based on simple threshold detection and does not provide the crew with sufficient lead time. Further, with increasing complexity of automation, the crew may not be aware of the true states of the automation to take corrective actions in time. At NASA, we aim to develop decision support tools by mining historic flight data to proactively identify and manage high risk situations encountered in flight. In this paper, we present our work on finding precursors to the anomalous drop-in-airspeed (ADA) event using the ADOPT (Automatic Discovery of Precursors in Time series) algorithm. ADOPT works by converting the precursor discovery problem into a search for sub-optimal decision making in the time series data, which is modeled using reinforcement learning. We give insights about the flight data, feature selection, ADOPT modeling and results on precursor discovery. Some improvements to ADOPT algorithm are implemented that reduces its computational complexity and enables forecasting of the adverse event. Using ADOPT analysis, we have identified some interesting precursor patterns that were validated to be operationally significant by subject matter experts. The performance of ADOPT is evaluated by using the precursor scores as features to predict the drop in airspeed events.

In this work we consider the problem of anomaly detection in heterogeneous, multivariate, variable-length time series datasets. Our focus is on the aviation safety domain, where data objects are flights and time series are sensor readings and pilot switches. In this context the goal is to detect anomalous flight segments, due to mechanical, environmental, or human factors in order to identifying operationally significant events and highlight potential safety risks. For this purpose, we propose a framework which represents each flight using a semi-Markov switching vector autoregressive (SMS-VAR) model. Detection of anomalies is then based on measuring dissimilarities between the model's prediction and data observation. The framework is scalable, due to the inherent parallel nature of most computations, and can be used to perform online anomaly detection. Extensive experimental results on simulated and real datasets illustrate that the framework can detect various types of anomalies along with the key parameters involved.

We develop an algorithm for automatic discovery of precursors in time series data (ADOPT). In a time series setting, a precursor may be considered as any event that precedes and increases the likelihood of an adverse event. In a multivariate time series data, there are exponential number of events which makes a brute force search intractable. ADOPT works by breaking down the problem into two steps - (1) inferring a model of the nominal time series (data without adverse event) by considering the nominal data to be generated by a hidden expert and (2) using the expert's model as a benchmark to evaluate the adverse time series to identify suboptimal events as precursors. For step (1), we use a Markov Decision Process (MDP) framework where value functions and Bellman's optimality are used to infer the expert's actions. For step (2), we define a precursor score to evaluate a given instant of a time series by comparing its utility with that of the expert. Thus, the search for precursors is transformed to a search for sub-optimal action sequences in ADOPT. As an application case study, we use ADOPT to discover precursors to go-around events in commercial flights using real aviation data.

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and aviation. Support vector regression (SVR) is a popular technique for modeling the input-output relations of a set of variables under the added constraint of maximizing the margin, thereby leading to a very generalizable and regularized model. However, for a dataset with m training points, it is challenging to build SVR models due to the O(m3) cost involved in building them. In this paper we propose ParitoSVR - a parallel iterated optimizer for Support Vector Regression in the primal that can be deployed over a network of machines, where each machine iteratively solves a small (sub-)problem based only on the data observed locally and these solutions are then combined to form the solution to the global problem. Our proposed method is based on the Alternating Direction Method of Multipliers (ADMM) optimization technique. Unlike many other existing techniques, ParitoSVR is provably convergent to the results obtained from the centralized algorithm, where the optimization has access to the entire data set. The experimental results show that the algorithm is scalable both with respect to accuracy and time to convergence. We use ParitoSVR to identify flights having anomalous fuel consumption from a large fleet-wide commercial aviation database containing thousands of flights. Along with the algorithmic contributions, this paper also describes the process of deployment of the ADMM-based SVR method on a multicore architecture, namely, the NASA Pleiades supercomputing infrastructure. We have been successful in running ParitoSVR on millions of training data points and hundreds of compute nodes.

The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than state-of-the-art methods while still guaranteeing the same outliers. By combining simple but effective indexing and disk block accessing techniques, we have developed a sequential algorithm iOrca that is up to an order-of-magnitude faster than the state-of-the-art. The indexing scheme is based on sorting the data points in order of increasing distance from a fixed reference point and then accessing those points based on this sorted order. To speed up the basic outlier detection technique, we develop two distributed algorithms (DOoR and iDOoR) for modern distributed multi-core clusters of machines, connected on a ring topology. The first algorithm passes data blocks from each machine around the ring, incrementally updating the nearest neighbors of the points passed. By maintaining a cutoff threshold, it is able to prune a large number of points in a distributed fashion. The second distributed algorithm extends this basic idea with the indexing scheme discussed earlier. In our experiments, both distributed algorithms exhibit significant improvements compared to the state-of-the-art distributed method [13].

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequences of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.