Abstract: A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Abstract: A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.

Abstract: A method for collecting and processing user input. In some embodiments the method includes presenting a first user with a prompt for eliciting a first response, the first response including a numerical portion including one or more numbers, and an explanatory portion; receiving, from the first user, the first response; receiving from each of a plurality of other users, a respective response of a plurality of other responses; and displaying, to the first user, an ordered list of other responses. Within the ordered list, a second response, of the plurality of other responses, may be earlier than a third response, of the plurality of other responses, the second response being, according to a measure of distance, more distant, than the third response, from the first response.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

Abstract: An aircraft fault detection system including at least one aircraft data logging device that captures a collection of time-series flight data, from at least one aircraft subsystem, for at least one flight leg of an aircraft flight, and an aircraft controller coupled to the at least one aircraft data logging device. The aircraft controller groups the collections of time-series flight data into a plurality of test states, the plurality of test states being determined from a plurality of training states and one or more of the test states is different from other test states in the plurality of test states, generates at least one test transition matrix based on the plurality of test states and determine anomalous behavior of the at least one aircraft subsystem based on the at least one test transition matrix, and forecasts a fault within the at least one aircraft subsystem based on the anomalous behavior.

Abstract: Described is a system for structuring rationales for collaborative forecasting between users of a crowdsourcing platform. For a given forecasting question, the system produces a forecasting rationale model from a combination of variables related to users and topics in a discussion of the users' forecasting rationale for making an initial forecast of an event. A relationship between the variables is determined, and based on the relationship between the variables, a prediction of each user's performance in making the initial forecast. Based on the predictions, top performing users and their forecasting rationales are selected, and the forecasting rationales of the top performing users are shared with other users of the crowdsourcing platform, allowing the other users to revise their initial forecasts in response to the shared forecasting rationales, resulting in revised forecasts. A forecast of the event that combines the revised forecasts is then output.

Abstract: A vehicle system prognosis apparatus including sensor(s) for detecting a characteristic of a vehicle system and generating at least one time series of condition indicator values, and a processor that receives the at least one time series and generates an analysis model, for the characteristic, that is trained with one or more of the at least one time series, that are obtained from the one or more sensors with the vehicle system operating under normal conditions, extracts from the at least one time series one or more features embodying an indication of a health of the vehicle system, generates a quantified health assessment of the vehicle system by quantifying the one or more features based on a normal distribution of the one or more features from the analysis model, and communicates the quantified health assessment of the vehicle system to an operator or crew member of the vehicle.

Abstract: Described is a system for decreasing the frequency of large cascading failures in a transmission network. Based on sensors distributed throughout the transmission network, the system determines if a cascading failure is present in a transmission network. Following determination of the cascading failure, the system activates at least one switch of a plurality of switches distributed in the transmission network in order to switch transmission lines, thereby altering connectivity in the transmission network.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

Abstract: A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Abstract: A method includes receiving input data including a plurality of feature vectors and labeling each feature vector based on a temporal proximity of the feature vector to occurrence of a fault. Feature vectors that are within a threshold temporal proximity to the occurrence of the fault are labeled with a first label value and other feature vectors are labeled with a second label value. The method includes determining, for each feature vector of a subset, a probability that the label associated with the feature vector is correct. The subset includes feature vectors having labels that indicate the first label value. The method includes reassigning labels of one or more feature vectors of the subset having a probability that fails to satisfy a probability threshold and, after reassigning the labels, training an aircraft fault prediction classifier using supervised training data including the plurality of feature vectors and the labels.

Abstract: An aircraft fault detection system including at least one aircraft data logging device configured to capture parametric flight data from at least one aircraft subsystem, and an aircraft controller coupled to the data logging device. The controller being configured to group the parametric flight data from the at least one aircraft subsystem into a plurality of test states, one or more of the test states being different from other test states in the plurality of states, generate at least one test transition matrix based on the plurality of test states and determine anomalous behavior of the at least one aircraft subsystem based on the at least one test transition matrix, and forecast faults within the at least one aircraft subsystem based on the anomalous behavior of the at least one aircraft subsystem determined from the at least one test transition matrix.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

Abstract: Described is a system for extracting multi-scale hierarchical clustering on customer observables (COs) data in a vehicle. The system selects a parameter for a set of incident data of COs data. Simplicial complexes are generated from the COs data based on the selected parameter. Face networks are generated from the simplicial complexes. For each face network, a set of connected components is extracted. Each connected component is transformed to a cluster of related COs, resulting in a first extracted relation between COs. The first extracted relation is used to automatically generate an alert at a client device when a second extracted relation different from the first extracted relation results from the transformation.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: Described is a system for identification of correlations in customer observables (COs). The system extracts key phrases representing COs from textual inputs from multiple data sources, wherein the COs are related to a consumer product. A unified hypergraph is constructed that models co-occurrences of COs. The unified hypergraph includes nodes and types of hyperedges connecting the nodes, where COs are represented by nodes and data sources are represented by different types of hyperedges. Each node of the unified hypergraph is embedded into a latent feature space. The unified hypergraph is partitioned into clusters within the latent feature space, where each cluster contains correlated CO data. The correlated CO data from a cluster are used to generate and provide targeted messages specific to the consumer product to a display device.

Abstract: In an example, a method for identifying associated events in an aircraft is described. The method includes obtaining sensor data, obtaining fault code data, generating a set of events, where each event occurs over a time interval over which either (i) the sensor data indicates an anomalous measurement or (ii) a fault code associated with a particular aircraft subsystem of the aircraft was signaled, calculating a value of statistical dependence between the set, based on the value exceeding a threshold, constructing a network representing the set as a sequence of related events and further representing a temporal order in which the sequence occurred, indexing, in a summary table stored in memory and separate from the sensor data and the fault code data, the sequence and the value, and controlling a display device to display the summary table and a visual representation of the network.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.