A method of coordinating automated systems, the method includes providing a first automated system that is programmed with a set of predetermined operating instructions that correspond with automated system processing requirements, monitoring an operational status of the first automated system with a second automated system, automatically generating a second system action, with the second automated system, that is complimentary to a first system action of the first automated system, where the first system action corresponds to the set of predetermined operating instructions and the second system action depends on the operational status of the first automated system, and performing the second system action with the second automated system so that the second automated system cooperates with the first automated system to perform a predetermined operation.

A method for monitoring an aircraft engine vane wheel (22), which includes: acquiring at least one time signal relative to moments when the vane wheel blades (23) pass in front of a sensor (21); determining a common flight phase of the aircraft; for each flight in a series of flights of the aircraft, correlating at least part of each time signal with a predetermined flight phase; and for each blade (23), each flight, and each predetermined flight phase, measuring the mean position (24C), the so-called “balance position”, of the top of the blade. The invention also relates to a device for implementing such a method. One advantage of the invention is providing a diagnosis of the blades using a small number of sensors and low computing power.

Method and apparatus for unsupervised aircraft design. A plurality of design problem data and service event data for an aircraft is received from an electronic data repository. Embodiments communicate with sensors on the aircraft during flight operations and capturing service data and sensor data. A high order vector is generated for each received problem report and service event data and each high order vector is concatenated into a high order vector matrix. Embodiments generate a reduced order symptom-normalized matrix by factorization of the concatenated high order vector matrix and generate a similarity matrix from the symptom-normalized matrix. An impact score is computed for each in-service event data as a function of similar problem reports using the similarity matrix. Embodiments generate a priority matrix configured to identify service event data having high impact scores and communicate a real-time alert of the high impact scored service event.

The invention disclosed herein describes a supervised autonomy system designed to precisely model, inspect and process the surfaces of complex three-dimensional objects. The current application context for this system is laser coating removal of aircraft, but this invention is suitable for use in a wide variety of applications that require close, precise positioning and maneuvering of an inspection or processing tool over the entire surface of a physical object. For example, this system, in addition to laser coating removal, could also apply new coatings, perform fine-grained or gross inspection tasks, deliver and/or use manufacturing process tools or instruments, and/or verify the results of other manufacturing processes such as but not limited to welding, riveting, or the placement of various surface markings or fixtures.

A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.

An avionics network including a plurality of avionics components and a configuration monitoring device, which is connected by wire or wirelessly to the plurality of avionics components. The configuration monitoring device has at least one configuration data interface configured to receive a plurality of configuration parameters characterizing the operating status of the avionics components. The configuration monitoring device further includes a parameter filtering device connected to the configuration data interface and configured to filter a subset of the configuration parameters received. The configuration monitoring device additionally includes reference parameter storage, configured to store sets of reference values for configuration parameters, and a parameter comparison device, coupled to the reference parameter storage and the parameter filtering device, and configured to compare the subset of configuration parameters received and filtered by the parameter filtering device with a set of reference values for the configuration parameters stored in the reference parameter storage.

A system and method for performing management and diagnostic functions in an advanced process control (APC) system. An APC management computer retrieves operating process data from an APC control computer and performs an iterative step test on the APC system. The iterative step test modifies at least one test parameter of the operating process data and identifies changes to a set of remaining parameters of the operating process data resulting from modification of the test parameter. The APC management computer determines at least one process variable from the iterative step test and generates at least one process model based on the process variable. The APC management computer transmits the process model to the APC control computer.

A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.

A system for flight control for managing actuators for an electric aircraft is provided. The system includes a controller, wherein the controller is designed and configured to receive a sensor datum from at least a sensor, generate an actuator performance model as a function of the sensor datum, identify a defunct actuator of the electric aircraft as a function of the sensor datum and the actuator performance model, generate an actuator allocation command datum as a function of at least the actuator performance model and at least the identification of the defunct actuator, and perform a torque allocation as a function of the actuator allocation command datum.

A dual-interface coupler includes a utilities unit, a number of utility cables configured to provide a number of utilities to the utilities unit, a first coupling unit associated with the utilities unit, and a second coupling unit associated with the utilities unit. The first coupling unit is configured to mechanically couple the utilities unit to a first corresponding coupling unit and comprises a utility interface. The number of utilities are configured to flow from the utilities unit through the utilities interface. The second coupling unit is configured to mechanically couple the second coupling unit to a second corresponding coupling unit.