Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method that includes applying, by one or more processors, a topic model to a document to generate a plurality of topics for the document. Each topic of the plurality of topics includes a corresponding group of words in the document. The method also includes performing a parts of speech (POS) tagging operation on the document to tag each word of a particular topic of the plurality of topics with a first label or a second label. Words tagged with the first label are designated as a first part of speech, and words tagged with the second label are designated as a second part of speech. The method further includes filtering the particular topic from the plurality of topics in response to a determination that each word of the particular topic is tagged with the first label.

Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, where obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: Autonomous behaviors in a multiagent adversarial scene, including: assigning, by a scene manager, to each friendly agent of plurality of friendly agents, a role comprising an engagement to an adversarial agent of one or more adversarial agents; assigning, by the scene manager, to each friendly agent of the plurality of friendly agents, a policy; and wherein each friendly agent of the plurality of friendly agents is configured to determine, based on a tactical model corresponding to the assigned policy, one or more actions.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method includes receiving time series source data that is associated with a source asset and that includes a set of classification labels. The method also includes receiving time series target data that is associated with a target asset and that lacks classification labels. The method further includes determining time series representations from the time series source data and the time series target data. The method also includes, based on the set of classification labels included in the time series source data and at least on raw time series data or the time series representations, generating a classifier operable to classify unlabeled data associated with the target asset. The raw time series data includes the time series source data and the time series target data.