Northrop Grumman Systems Corporation

Sunil Dixit

Richard R Ramroth

Nathan R Good

A method, implemented by a computing system on-board a vehicle, differentiates whether an anomaly originating from a hardware component of the vehicle is caused by a cybersecurity threat, by a degradation of the performance of the hardware component, or by both. States of the respective nodes in a first group of nodes of the first hardware component are compared with a stored table of sets of states of nodes in the first group. A determination is made of whether the anomaly associated with the first hardware component is caused by a cybersecurity threat or by health degradation of the first hardware component based on the comparison of the states of the nodes of the first group with the sets of possible states of the respective nodes where each set is associated with one of a cybersecurity threat and health degradation.

Adaptive cybersecurity for vehicles

A method, implemented by a ground-based computing system, identifies cybersecurity threats originating from proposed updates to software, firmware or configuration file instructions resident in hardware components in a fleet of vehicles. In a proposed update, a valid encrypted signature block of the developer must be present. The proposed update is contained as a last file of a series of linked sequential blockchain files of prior updates. Upon a determination that the proposed update contains a valid signature block and is the last file in a validated series of linked blockchain files, the update is analyzed for any cybersecurity threats. If no cybersecurity threats are found, the update is transmitted to the vehicles in the fleet for implementation in the respective hardware components.

Cybersecurity for configuration and software updates of vehicle hardware and software based on fleet level information

A ground-based computing system receives data of performance parameters for like components disposed on like aircraft, and determines corresponding levels of degradation and rates of change of degradation for the respective like components. A fleet-level of degradation for groups of like components is generated based on analysis of the combined degradations of the like components in the respective group and models of components revised. A predicted time for maintenance for each like component is determined based on the corresponding at least one of the RUL and SOH of the like component, thereby enabling cost effective maintenance determinations for components based on a fleet-level information. The ground-based computing system transmits a modified component model to the like aircraft to replace a prior version of the component model which is used to generate on-board degradation analysis, thereby enhancing the accuracy of on-board degradation analysis based on fleet level data.

Fleet level prognostics for improved maintenance of vehicles

A ground-based computing system receives data of performance parameters for like components disposed on like aircraft, and determines corresponding levels of degradation and rates of change of degradation for the respective like components. A fleet-level of degradation for groups of like components is generated based on analysis of the combined degradations of the like components in the respective group. At least one of a remaining useful lifetime (RUL) and a state-of-health (SOH) for each of the respective like components is determined based on a comparison of the levels of degradation for each of the like components and the fleet-level of degradation of the group of like components. A predicted time for maintenance for each like component is determined based on the corresponding at least one of the RUL and SOH of the like component, thereby enabling cost effective maintenance determinations for components based on a fleet-level information.

An exemplary method implemented by a computing system determines a prediction of degradation of components in a complex vehicle to enable cost effective maintenance and enhance vehicle operational availability (vehicle readiness for missions) based on currently measured performance-based parameters associated with the respective components. Residues from models of the components reflect differences between performance as determined by the models of the components and currently measured actual performance parameters. The residues are used determine a level of degradation and a rate of change of degradation for the respective components. The remaining useful life (RUL) of the respective components is the projected/predicted time of remaining acceptable performance of the respective component, and is based on the current degradation level, the rate of change of degradation, and a stored threshold level of degradation that is a maximum amount of degradation that is acceptable.

Prognostics for improved maintenance of vehicles

A method implemented by a computing system identifies anomalies that represents potential off-normal behavior of components on an aircraft based on data from sensors during various modes of operations including in-flight operation of the aircraft. High-frequency sensor outputs monitor performance parameters of respective components. Anomaly criteria is dynamically selected and the high-frequency sensor outputs are compared with the anomaly criteria where the comparison results determine whether potentially off-normal behavior exists. A conditional anomaly tag is inserted in the digitized representations of first high-frequency sensor outputs where the corresponding comparison results indicate potentially off-normal behavior. At least the digitized representations of the first high-frequency sensor outputs containing the conditional anomaly tag are sent to a computer-based diagnostic system for a final determination of whether the conditional anomaly tag associated with the respective components represents off-normal behavior for the respective components.

High frequency sensor data analysis and integration with low frequency sensor data used for parametric data modeling for model based reasoners

Methods and apparatus are provided for an integrated vehicle health management system that uses a model based reasoner to develop real-time diagnostic and maintenance information about a vehicle. A model having nodes is used to represent sensors, components, functions and other elements of the system. Parameterized input sensors provide flexible definitions of each node. Once developed, the model is loaded into an operational flight program to provide real-time failure analysis of the vehicle.

Parametric data modeling for model based reasoners

An exemplary method provides for automatically curing a detected behavior anomaly in executing target software during the continuing execution of the target software. Ranges of parameters of acceptable behaviors are stored. One behavior is detected that is outside the range of parameters for acceptable behavior for the corresponding behavior. A probability of success is predicted for restoring the corresponding behavior to acceptable behavior. First bytes associated with the behavior anomaly are replaced with the other bytes upon the predicted probability of success exceeding a predetermined success threshold, thereby automatically implementing a likely cure of a detected anomaly in the target software during the continuous execution of the target software.

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Torrance, CA, United States of America

Sunil Dixit