Abstract: The present disclosure provides for predictive part maintenance by generating a reliability curve for an aircraft based on historic removals; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the aircraft part into a given aircraft; tracking a number of cycles of the given instance of the aircraft part based on operations of the given aircraft in which the given instance of the aircraft part is installed; and in response to the number of cycles of the given instance of the aircraft part satisfying the removal threshold, transmitting a service alert to an operator of the given aircraft.

Abstract: Techniques for intelligently predicting bundles of replacement parts. These techniques include determining a plurality of maintenance events for a plurality of replacement parts. The determining includes identifying one or more replacement parts for a maintenance event, based on one or more replacement part events occurring within a time period related to the maintenance event. The techniques further include generating one or more clusters of replacement parts based on the plurality of maintenance events, and predicting one or more bundles of replacement parts, based on the clusters, wherein each bundle comprises a plurality of replacement parts.

Abstract: A method of managing in-service operation of a fleet of aircraft is provided that includes generating a schedule of flights through an air transportation network that includes airports, and that includes direct flight routes or flight connections between the airports. The method includes performing a fleet assignment of the fleet of aircraft to the schedule of flights based on availability metric values for respective aircraft of the fleet of aircraft, and dispatching the fleet of aircraft serve the schedule of flights to according to the fleet assignment. In this regard, performing the fleet assignment includes, for an aircraft of the fleet of aircraft, accessing reliability metric values for parts of the aircraft. Availability scores for the aircraft are determined based on the reliability metric values from respective ones of the data sources, and the availability scores are combined to determine an availability metric value for the aircraft.

Abstract: Techniques for intelligently predicting bundles of replacement parts. A plurality of maintenance events for a plurality of replacement parts are determined based on historical data related to the plurality of replacement parts. Each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts. A plurality of clusters of replacement parts are generated based on the plurality of maintenance events. A plurality of bundles of replacement parts are predicted based on the clusters. Each bundle includes a plurality of replacement parts.

Abstract: The present disclosure provides for predictive part maintenance by identifying component service programs indicating one or more components to track usage of in one or more aircraft for a given time period; tracking component usage in the one or more aircraft for the one or more components to identify scheduled removals for instances of the one or more components within the given time period; amalgamating component removals across the one or more aircraft for the one or more components; amalgamating component demand across the component service programs for the one or more components based on the scheduled removals and the component removals; and generating and transmitting an inventory alert based on the component demand.

Abstract: The present disclosure provides for predictive part maintenance by generating a reliability curve for an aircraft based on historic removals; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the aircraft part into a given aircraft; tracking a number of cycles of the given instance of the aircraft part based on operations of the given aircraft in which the given instance of the aircraft part is installed; and in response to the number of cycles of the given instance of the aircraft part satisfying the removal threshold, transmitting a service alert to an operator of the given aircraft.

Abstract: The present disclosure provides for predictive part maintenance by identifying component service programs indicating one or more components to track usage of in one or more aircraft for a given time period; tracking component usage in the one or more aircraft for the one or more components to identify scheduled removals for instances of the one or more components within the given time period; amalgamating component removals across the one or more aircraft for the one or more components; amalgamating component demand across the component service programs for the one or more components based on the scheduled removals and the component removals; and generating and transmitting an inventory alert based on the component demand.

Abstract: The present disclosure provides for predictive part maintenance by generating a reliability curve for an aircraft based on historic removals; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the aircraft part into a given aircraft; tracking a number of cycles of the given instance of the aircraft part based on operations of the given aircraft in which the given instance of the aircraft part is installed; and in response to the number of cycles of the given instance of the aircraft part satisfying the removal threshold, transmitting a service alert to an operator of the given aircraft.

Abstract: Techniques for intelligently predicting bundles of replacement parts. A plurality of maintenance events for a plurality of replacement parts are determined based on historical data related to the plurality of replacement parts. Each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts. A plurality of clusters of replacement parts are generated based on the plurality of maintenance events. A plurality of bundles of replacement parts are predicted based on the clusters. Each bundle includes a plurality of replacement parts.

Abstract: Techniques for intelligently predicting bundles of replacement parts. A plurality of maintenance events for a plurality of replacement parts are determined based on historical data related to the plurality of replacement parts. Each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts. A plurality of clusters of replacement parts are generated based on the plurality of maintenance events. A plurality of bundles of replacement parts are predicted based on the clusters. Each bundle includes a plurality of replacement parts.

Abstract: The present disclosure provides for predictive part maintenance by identifying component service programs indicating one or more components to track usage of in one or more aircraft for a given time period; tracking component usage in the one or more aircraft for the one or more components to identify scheduled removals for instances of the one or more components within the given time period; amalgamating component removals across the one or more aircraft for the one or more components; amalgamating component demand across the component service programs for the one or more components based on the scheduled removals and the component removals; and generating and transmitting an inventory alert based on the component demand.

Abstract: The present disclosure provides for predictive part maintenance by generating a reliability curve for an aircraft based on historic removals; setting a removal threshold on the reliability curve; tracking an installation of a given instance of the aircraft part into a given aircraft; tracking a number of cycles of the given instance of the aircraft part based on operations of the given aircraft in which the given instance of the aircraft part is installed; and in response to the number of cycles of the given instance of the aircraft part satisfying the removal threshold, transmitting a service alert to an operator of the given aircraft.

Abstract: A computing device for use in detecting maintenance events and maintenance intervals is provided. The computing device is configured to receive aircraft usage data for at least one carrier, identify at least one potential maintenance event in the usage data, identify at least one actual maintenance event in the usage data at least by comparing the usage data for a first aircraft of the at least one carrier to a second aircraft of the at least one carrier during each potential maintenance event, determine an indicator used by each carrier for scheduling maintenance events, determine a maintenance interval for each carrier for an aircraft model associated with at least the first aircraft.

Abstract: Techniques for intelligently predicting bundles of replacement parts. A plurality of maintenance events for a plurality of replacement parts are determined based on historical data related to the plurality of replacement parts. Each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts. A plurality of clusters of replacement parts are generated based on the plurality of maintenance events. A plurality of bundles of replacement parts are predicted based on the clusters. Each bundle includes a plurality of replacement parts.

Abstract: A part reliability determination system and method are used to determine a reliability of at least one part of a vehicle. The reliability determination system and method include a part life prediction control unit that determines a remaining life of the part(s) based on current life data of the part(s) in relation to a part life distribution related to the part(s).

Abstract: A part reliability determination system and method are used to determine a reliability of at least one part of a vehicle. The reliability determination system and method include a part life prediction control unit that determines a remaining life of the part(s) based on current life data of the part(s) in relation to a part life distribution related to the part(s).

Abstract: A computing device for use in detecting maintenance events and maintenance intervals is provided. The computing device is configured to receive aircraft usage data for at least one carrier, identify at least one potential maintenance event in the usage data, identify at least one actual maintenance event in the usage data at least by comparing the usage data for a first aircraft of the at least one carrier to a second aircraft of the at least one carrier during each potential maintenance event, determine an indicator used by each carrier for scheduling maintenance events, determine a maintenance interval for each carrier for an aircraft model associated with at least the first aircraft.

Abstract: Systems and methods for aircraft cabin noise analysis are disclosed. In one embodiment, a method includes receiving a first request for an aircraft cabin noise analysis, wherein the request comprises a plurality of flight parameters associated with a first flight mission, retrieving, from a computer readable memory, a plurality of noise parameters associated with the plurality of flight parameters for the first flight mission, determining a total noise exposure parameter associated with the first flight mission, generating an output comprising the total noise exposure on a user interface, and generating a warning on a user interface when the total noise exposure exceeds a threshold. In some embodiments, the monitored system may be implemented in a computing system or as logic instructions recorded on a computer readable medium.

Abstract: Systems and methods for aircraft cabin noise analysis are disclosed. In one embodiment, a method includes receiving a first request for an aircraft cabin noise analysis, wherein the request comprises a plurality of flight parameters associated with a first flight mission, retrieving, from a computer readable memory, a plurality of noise parameters associated with the plurality of flight parameters for the first flight mission, determining a total noise exposure parameter associated with the first flight mission, generating an output comprising the total noise exposure on a user interface, and generating a warning on a user interface when the total noise exposure exceeds a threshold. In some embodiments, the monitored system may be implemented in a computing system or as logic instructions recorded on a computer readable medium.