Abstract: A method for determining structural vibration mode characteristics includes preparing a digital image correlation (DIC) dataset for the component, applying a dynamic mode decomposition (DMD) technique to the DIC dataset to determine a plurality of DMD modes, selecting at least one dominant DMD mode from the plurality of DMD modes, classifying each of the at least one selected dominant DMD mode as a standing wave or a traveling wave, and determining structural vibration mode characteristics of the component by extracting the structural vibration mode characteristics from each of the at least one selected dominant DMD mode having a standing wave classification.

Abstract: A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

Abstract: A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

Abstract: A method of inspecting a component for anomalous regions includes recording a plurality of sensor readings using one or more sensors, each reading corresponding to a different region of the component; determining an appearance metric for each reading; and determining a nominal appearance metric based on individual values of the appearance metric for a first subset of the readings. The method includes, for a particular sensor reading outside the first subset: determining a difference between the nominal appearance metric and the appearance metric of the particular sensor reading; updating the nominal appearance metric based on the particular sensor reading; and, based on the difference exceeding a threshold: determining that the particular sensor reading is anomalous and corresponds to an anomalous region of the component, and recording additional sensor readings of the anomalous region using one or more sensing parameters that differ from those used to record the anomalous sensor reading.

Abstract: A method for controlling a touchless lavatory is disclosed herein. The method includes collecting, by a processor, sensor data from a sensor, the sensor data including an ambient light measurement and an indication of motion, aggregating, by the processor, the collected sensor data for a time window, extracting, by the processor, features from the aggregated sensor data, and determining, by the processor, a probability of an activity occurring based on the extracted features. The method further includes activating, by the processor, a motor based on the determining that the activity has a high probability of occurring.

Abstract: A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

Abstract: A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for indication confirmation for detecting a sub-surface defect, the set of instructions comprising: an instruction to initialize a transducer starting location and a transducer orientation responsive to a prior determination of a potential flaw location; an instruction to optimize an observation point of the transducer responsive to the transducer starting location and the transducer orientation responsive to a flaw response model; an instruction to move the transducer to the observation point location and orientation; an instruction to collect the scan data at the observation point location and orientation; and an instruction to analyze the scan data to extract a measure of the flaw response model; and an instruction to update the flaw response model.

Abstract: A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

Abstract: A method for determining structural vibration mode characteristics includes preparing a digital image correlation (DIC) dataset for the component, applying a dynamic mode decomposition (DMD) technique to the DIC dataset to determine a plurality of DMD modes, selecting at least one dominant DMD mode from the plurality of DMD modes, classifying each of the at least one selected dominant DMD mode as a standing wave or a traveling wave, and determining structural vibration mode characteristics of the component by extracting the structural vibration mode characteristics from each of the at least one selected dominant DMD mode having a standing wave classification

Abstract: Methods and systems for assessing compliance of aircraft engines are described. The method comprises selecting a test population from a set of deployed aircraft engines based on one or more selection criteria, each of the deployed aircraft engines having associated thereto design requirements and field data; selecting at least one parameter that is present in both the design requirements and the field data of the test population, the design requirements comprising design time-series profiles, the field data comprising field time-series profiles; comparing the field time-series profiles to the design time-series profiles for the at least one parameter using a similarity metric to obtain field statistics; comparing the field statistics to expected statistics from the design requirements; and generating a compliance assessment output of the test population based on a difference between the field statistics and the expected statistics.

Abstract: Systems and methods for defining mission profiles for a new engine are described. The method comprises selecting deployed engines from a set of existing engines based on components of the new engine using a first similarity metric; collecting field data associated with the deployed engines, the field data comprising usage and operating conditions for the deployed engines creating representative mission profiles from the field data using a second similarity metric; and defining the mission profiles for the new engine using the representative mission profiles.

Abstract: Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.

Abstract: A method for nondestructive inspection of a component, the method includes determining a first pulse-echo scan from a first side of a component; determining a second pulse-echo scan from a second side of the component; determining a through-transmission scan based on the first pulse-echo scan, the second pulse-echo scan, and a model of the component, the model comprises a rigid internal structure of the component; and classifying the component based on comparing the through-transmission scan to a “gold” model.

Abstract: A method of sensor node position determination for a sensor network is provided. A coverage distribution is defined based on a number of sensor nodes and sensor footprints of the sensor nodes. A desired position for each of the sensor nodes is determined based on the coverage distribution and a prior probability distribution defined on a bounded domain for the number of sensor nodes as a minimization of a distance between the coverage distribution and the prior probability distribution. The desired position to configure the sensor nodes is output.

Abstract: A method of monitoring an additive manufacturing build process includes first and second phases. The first phase includes depositing a powder layer onto a powder bed. A topographical profile of the powder bed is captured with a profilometer. An image of the powder bed is captured with a camera. The image and topographical profile are combined to create a data set that is transferred to a machine learning algorithm. A set of training data is generated and includes a set of deviations from a nominal model. The second phase includes depositing a powder layer onto the powder bed. An image of the powder bed is captured and compared to the set of training data. Deviations from the nominal model of the first powder bed are determined. Any deviations that are greater than a numerical threshold are labelled and identified as a defect which includes its type and severity.

Abstract: A method for monitoring an additive manufacturing process during fabrication of a component part is disclosed. In various embodiments, the method includes the steps of selecting a sensing matrix; orienting a sensor toward a surface of the component part; generating a discrete time signal, based on data obtained from the sensor, the discrete time signal being representative of a process condition of the component part while the component part is undergoing the additive manufacturing process; compressing the discrete time signal using the sensing matrix to form a compressed measurement signal; and storing the compressed measurement signal in a storage device while the component part is undergoing the additive manufacturing process. In various embodiments, selecting the sensing matrix comprises selecting a basis function. In various embodiments, the basis function is determined using a random time sampling.

Abstract: A method for nondestructive vibrothermography inspection of a component, the method includes generating ultrasonic excitations in a component over a range of frequencies; determining a thermal signature in the component from the excitations; registering a model with the thermal signature; determining damage based on the thermal signal and model; and classifying the component based on the determining.

Abstract: A method for nondestructive inspection of a component, the method includes determining a first pulse-echo scan from a first side of a component; determining a second pulse-echo scan from a second side of the component; determining a through-transmission scan based on the, first pulse-echo scan, the second pulse-echo scan, and a model of the component, the model comprises a rigid internal structure of the component; and classifying the component based on comparing the through-transmission scan to a “gold” model.

Abstract: A method for nondestructive vibrothermography inspection of a component, the method includes generating ultrasonic excitations in a component over a range of frequencies; determining a thermal signature in the component from the excitations; registering a model with the thermal signature; determining damage based on the thermal signal and model; and classifying the component based on the determining.

Abstract: A system and method are provided to perform loads-based structural health monitoring (LBSHM) of a dynamical system. The method includes receiving, by at least one computer, sensing data responsive to sensing at least one of a parametrical state and a response of the dynamical system, and determining a Koopman mode and a Koopman eigenvalue. The Koopman mode represents a correlation between the sensor data output by the plurality of sensors. The Koopman eigenvalue represents a frequency component associated with the sensor data and growth or decay of energy associated with the sensor data. The method further includes generating, by the at least one computer, an estimation model to determine a linear estimation based on the Koopman mode and the Koopman eigenvalue that estimates a load response of the dynamical system based on growth or decay of energy associated with the sensor data.