In an information processing method of an embodiment, a computer is configured to divide observation data into training data and test data, select non-target data from the training data, select a parameter combination from a parameter set corresponding to the non-target data, classify the non-target data as clusters in a feature space of the parameter combination under clustering setting conditions, generate, for each of the clusters, a model trained to output target data included in the training data when the non-target data of the clusters is input, and reselect the parameter combination or change the clustering setting conditions such that a difference between the target data output from the model and the target data of the test data decreases.

An advisory system of a vessel that monitors variables of a vessel system inclusive of systems and subsystems that are used to operate the vessel. The advisory system may use machine-learning to learn from an operator (i) whether or not two variables are related to one another, and (ii) likelihood that a variable will reach a threshold, and, optionally, time until reaching the threshold. The system may receive operator feedback (i) to indicate whether the two variables are related to one another, and (ii) whether a behavior of the variable is normal or not normal. Thereafter, if a determination that the same two variables are related to one another and behaving in a similar manner, provide notification to the operator of the behavior. In response to determining that the variable is behaving (e.g., trending) in a similar manner that is not normal, providing a notification to the operator.

A stochastic configuration network based turbofan engine health parameter estimation method is disclosed. The stochastic configuration network based turbofan engine health parameter estimation method designed by the present invention combines the model based Kalman filter algorithm and the data-driven based stochastic configuration network, i.e. using the output of the stochastic configuration network as the compensation of the Kalman filter algorithm, so as to take into account the estimated result of the Kalman filter and the estimated result of the stochastic configuration network and improve the estimation accuracy of the original Kalman filter algorithm when the measurable parameters of the turbofan engine are less than the health parameters to be estimated. In addition, the present invention effectively reduces the accuracy loss caused by the poor structure of the neural network through the stochastic configuration network, and improves the generalization ability of the network.

A method of making a pressure bulkhead assembly of an aircraft includes determining an optimized position of splice angles such that splice surfaces of the splice angles will form a circumferential splice surface of the pressure bulkhead assembly with an optimized shape. The method further includes performing a virtual fit between the plurality of splice angles, at the optimized position, and an aft pressure bulkhead. The method also includes determining splice-angle-hole positions of splice-angle holes to be drilled in each one of the splice angles such that the splice-angle holes will correspond to aft-pressure-bulkhead holes, pre-drilled in the aft pressure bulkhead. The method further includes drilling the splice-angle holes in each one of the splice angles at the splice-angle-hole positions. The method also includes joining each one of the splice angles with the aft pressure bulkhead such that the splice surfaces form the circumferential splice surface with the optimized shape.

A stochastic configuration network based turbofan engine health parameter estimation method is disclosed. The stochastic configuration network based turbofan engine health parameter estimation method designed by the present invention combines the model based Kalman filter algorithm and the data-driven based stochastic configuration network, i.e. using the output of the stochastic configuration network as the compensation of the Kalman filter algorithm, so as to take into account the estimated result of the Kalman filter and the estimated result of the stochastic configuration network and improve the estimation accuracy of the original Kalman filter algorithm when the measurable parameters of the turbofan engine are less than the health parameters to be estimated. In addition, the present invention effectively reduces the accuracy loss caused by the poor structure of the neural network through the stochastic configuration network, and improves the generalization ability of the network.

A method and apparatus for positioning an end effector relative to a fuselage assembly. The end effector is positioned relative to an expected reference location for a reference point on the fuselage assembly using data from a first metrology system. After positioning the end effector relative to the expected reference location, an actual reference location for the reference point on the fuselage assembly is identified using data from a second metrology system. The end effector is positioned at an operation location based on the actual reference location identified.

A storage unit configured to store identification information assigned to each of a plurality of industrial machines in association with information indicating a flight position of an unmanned aircraft, an acquisition unit configured to acquire the identification information from at least one industrial machine from among the plurality of industrial machines, and a flight command generation unit configured to generate a flight command for flying the unmanned aircraft at the flight position stored in association with the identification information acquired by the acquisition unit are provided.

A method and apparatus for performing an assembly operation. A tool may be macro-positioned within an interior of a fuselage assembly. The tool may be micro-positioned relative to a particular location on a panel of the fuselage assembly. An assembly operation may be performed at the particular location on the panel using the tool.

A predictive maintenance system is disclosed. The system includes a network of analog and digital sensors, each sensor configured for measuring telemetry data associated with temperature levels, voltage levels, current levels, and other analog or digital parameters. The system includes microprocessors for receiving the (digitized) analog and digital telemetry data, tabulating and timestamping the raw telemetry datasets. The microprocessors compress the raw data and reduce its dimensionality by generating principal component sets from the raw data based on scalar parameters corresponding to machine learning algorithms stored to memory, the principal component sets capturing a majority of variances within the raw data. The principal component sets are organized into data packets including identifiers for the relevant algorithms. The data packets are transmitted via real time networks for either onboard storage or ground-based analysis.

A monitoring method, the purpose of which is, when a loss of power is detected in an aircraft engine, to generate an alarm in the form of a single message displayed on a display screen in the cockpit, in order to indicate if the level of damage suffered by the engine is critical or not. The steps implemented are based on alarm signals transmitted by a central processing unit of the engine and also on alarm signals transmitted by a diagnostic device for the onboard systems of the aircraft, in order to take account of both the situation of the engine and also the situation of the systems surrounding the engine which can be affected by damage to an engine.