A method includes obtaining data associated with operation of an aircraft and determining a first operational phase of the aircraft based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data corresponding to a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data corresponding to a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication that indicates an occurrence of an operational phase transition to the candidate operational phase at the first time.

Continuous monitoring of a model in an interactive computer simulation station. The model comprises a plurality of interrelated parameters defining a dynamic behavior of a simulated interactive object in an interactive computer simulation when inputs are provided on tangible instrument(s) of the station. During a diagnostic period of time, a frequency sweep of the model is performed for measuring the dynamic behavior of the simulated interactive object. During the frequency sweep, each of the tangible instrument(s) is automatically mechanically moved following an input function defining an input range variation at a related frequency. The frequency sweep provides an actual frequency response function for the tangible instrument(s) defining the dynamic behavior. The station is determined to require maintenance when the dynamic behavior of the simulated interactive object, measured by the frequency sweep, is outside of a target dynamic behavior range for the simulated interactive object.

A system and computer-implemented method for generating real-time performance advisories for a centrifugal compressor of a fleet of centrifugal compressors are provided. The method includes receiving an actual thermodynamic signature of the compressor, that is unique to the compressor, receiving compressor process parameter values during operation of the compressor, determining, in real-time, an actual performance of the compressor using the compressor process parameter values, determining, in real-time, a predicted performance of the compressor using the received actual thermodynamic signature of the compressor, determining a performance deviation of the compressor using the actual performance and the predicted performance, comparing the performance deviation to a predetermined dynamic threshold range of performance deviation specific to operating speed, and generating a notification to a user using the comparison.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.

A system and method are provided for identification of a combination of sensors suitable for achieving a set of output requirements, enabling assembly thereof. An arrangement of sensors is assembled according to an optimized sensor set, generated to define at least one sensor channel having a threshold value for at least one quality criterion. The sensor sets are generated by down-selection of the sensor channels from a previously tested set of available channels to a subset mapping to a set of required output states. Assignment of a threshold value for each sensor channel is based on a mapping of the value to a target value of a performance metric.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

Methods and systems autonomously parking and retrieving vehicles are disclosed. Available parking spaces or parking facilities may be identified, and the vehicle may be navigated to an available space from a drop-off location without passengers. Special-purpose sensors, GPS data, or wireless signal triangulation may be used to identify vehicles and available parking spots. Upon a user request or a prediction of upcoming user demand, the vehicle may be retrieved autonomously from a parking space. Other vehicles may be autonomously moved to facilitate parking or retrieval.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

A state change detection unit obtains the data generation probability on the basis of the values of observation data and the value of a parameter of a prior distribution, obtains, on the basis of the data generation probability, a run length probability distribution of the time-series observation data acquired up to the current time point as a condition, and detects a change in the state of a facility on the basis of the run length probability distribution. Furthermore, an update unit updates the value of the parameter of the prior distribution using the values of the observation data, to generate the prior distribution to be used for calculating the data generation probability at a next time point.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.