Abstract: A computer-implemented method of detecting an anomalous action associated with a physical system includes developing, by a computing device a plurality of vectors, each vector indicative of an event that occurred at a specific time within the system, combining, with the computing device each vector that occurred within a predefined time duration into one of a plurality of master vectors, and performing, with the computing device a cluster analysis to group each master vector of the plurality of master vectors into one of a plurality of states. The method also includes determining, with the computing device a real-time master vector based at least in part on one or more events that occur within the predefined time duration, classifying, with the computing device the real-time master vector as a real-time state, and indicating that the real-time state is anomalous when the real-time state doesn't match one of the plurality of states.

Abstract: A method performed in an industrial control system where User and Entity Behavior Analytics (UEBA) is applied to specific actions that are performed within the industrial control system to detect security and safety anomalies related to actions of process engineers and plant operators. Malicious and non-malicious, as well as intentional and accidental, misuses of engineering workstations and human machine interfaces (HMIs) are detected.

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.

Abstract: A monitoring system includes an analytical engine system coupled to a sensor of an engine system. The analytical engine system is configured to receive data corresponding to operation of the engine system, to determine a distance metric corresponding to the operating parameters of the engine system, to compare the distance metric for a monitored lubricant temperature to a model threshold, and to generate a lubricant alert signal when the distance metric for the monitored lubricant temperature is greater than the model threshold. The received data includes the monitored lubricant temperature of a bearing and operating parameters of the engine system. The distance metric is based at least in part on the monitored lubricant temperature relative to a lubricant temperature statistical model, which is based at least in part on the operating parameters of the engine system.

Abstract: A monitoring system includes an analytical engine system coupled to a sensor of an engine system. The analytical engine system is configured to receive data corresponding to operation of the engine system, to determine a distance metric corresponding to the operating parameters of the engine system, to compare the distance metric for a monitored lubricant temperature to a model threshold, and to generate a lubricant alert signal when the distance metric for the monitored lubricant temperature is greater than the model threshold. The received data includes the monitored lubricant temperature of a bearing and operating parameters of the engine system. The distance metric is based at least in part on the monitored lubricant temperature relative to a lubricant temperature statistical model, which is based at least in part on the operating parameters of the engine system.

Abstract: Historical aircraft data recorded during flight associated with a plurality of aircraft engines and a plurality of prior aircraft flights is accessed and metrics from the aircraft data recorded during flight is automatically calculated on a per-flight basis, and a probabilistic model is employed to capture and represent relationships based on the calculated metrics, the relationships including a plurality of engine parameters and flight parameters. Conditional probability distributions are then calculated for a particular aircraft engine during a potential or historical aircraft flight based on the probabilistic model, engine parameter values associated with the particular aircraft engine, and flight parameter values associated with the potential or historical aircraft flight, and indications associated with the calculated conditional probability distributions are displayed.

Abstract: According to some embodiments, a structured requirements database may store at least one electronic file containing requirements for an industrial asset, the requirements comprising a structured textual description of the industrial asset. A processing unit may access the electronic file containing the requirements for the industrial asset and automatically execute a diagnosis model creation process using the structured textual description of the industrial asset to create a model for the industrial asset. The processing unit may then transmit, via a communication port, information associated with the model to a model based diagnosis engine.

Abstract: Historical aircraft data recorded during flight associated with a plurality of aircraft engines and a plurality of prior aircraft flights is accessed and metrics from the aircraft data recorded during flight is automatically calculated on a per-flight basis, and a probabilistic model is employed to capture and represent relationships based on the calculated metrics, the relationships including a plurality of engine parameters and flight parameters. Conditional probability distributions are then calculated for a particular aircraft engine during a potential or historical aircraft flight based on the probabilistic model, engine parameter values associated with the particular aircraft engine, and flight parameter values associated with the potential or historical aircraft flight, and indications associated with the calculated conditional probability distributions are displayed.

Abstract: A vehicle control system and method determine one or more designated speeds of a trip plan for a trip of a vehicle system along a route. The trip plan can designate the one or more designated speeds as a function of one or more of time or distance along the route for the trip. Geometry of the route that the vehicle system will travel along during the trip is determined, as well as one or more prospective forces that will be exerted on the vehicle system during movement of the vehicle system along the route for the trip based at least in part on the geometry of the route. The trip plan is revised to reduce at least one of the prospective forces by reducing at least one of the designated speeds of the trip plan based on the one or more prospective forces that are determined.

Abstract: A vehicle control system and method determine one or more designated speeds of a trip plan for a trip of a vehicle system along a route. The trip plan can designate the one or more designated speeds as a function of one or more of time or distance along the route for the trip. Geometry of the route that the vehicle system will travel along during the trip is determined, as well as one or more prospective forces that will be exerted on the vehicle system during movement of the vehicle system along the route for the trip based at least in part on the geometry of the route. The trip plan is revised to reduce at least one of the prospective forces by reducing at least one of the designated speeds of the trip plan based on the one or more prospective forces that are determined.

Abstract: Disclosed are a system, a computer-readable storage medium storing at least one program, and a computer-implemented method of remaining use estimation. An interface module receives measurement data indicative of a level of usage of an apparatus. A filter engine, based on first model data, generates a first value indicative of a probability that the RU of the apparatus reached a threshold RU value. The filter engine, based on second model data, generates a second value indicative of a probability of the received measurement data given that the RU of the apparatus reached the threshold RU value. The filter engine, based on the first and second values, generates output data that is indicative of a probability that the RU of the apparatus reached the threshold RU value.

Abstract: Disclosed are a system, a computer-readable storage medium storing at least one program, and a computer-implemented method of remaining life estimation. An interface module receives measurement data indicative of a level of usage of an apparatus. A data access module accesses first and second model data of the apparatus. The first model data includes discrete probabilities of a first set of respective remaining usage (RU) values. The second model data includes usage levels matched to a second set of respective RU values. A filter engine updates the first model data by neglecting a selected portion of the discrete probabilities of the first set of RU values. The selected portion corresponds to negative RU values. The filter engine generates an RU estimate of the apparatus based at least on the updated first model data and the second model data.

Abstract: Method and apparatus for generating or updating (e g , tuning) an aviation simulation scenario. One method for generating an aviation simulation scenario includes receiving user input corresponding to the aviation simulation scenario at a processing device. The method also includes accessing one or more input data sources collectively having multiple data sets using the processing device. The method includes transforming data sets into the aviation simulation scenario using the processing device based on the user input. The method also includes storing the aviation simulation scenario. The aviation simulation scenario is configured to be provided to an aviation simulator software for executing an aviation simulation.

Abstract: A method and system for monitoring and predicting the health of electro-mechanical systems and components includes collecting data for a fixed pattern of actuation of such system or component. This data is used to build statistical models that correspond to a normal state of the system or component. New measurements are compared to this model in order to monitor the health of the system or component. The comparison can be made using a distance calculation. The combination of new measurements with historical data provides the prediction for future health states of the system or component.

Abstract: A method and system for monitoring and predicting the health of electro-mechanical systems and components includes collecting data for a fixed pattern of actuation of such system or component. This data is used to build statistical models that correspond to a normal state of the system or component. New measurements are compared to this model in order to monitor the health of the system or component. The comparison can be made using a distance calculation. The combination of new measurements with historical data provides the prediction for future health states of the system or component.