Abstract: A method performed by a computing device for implementing a dynamic outbound firewall. The method includes creating a localhost virtual private network (VPN) service, intercepting, using the localhost VPN service, outbound network traffic originated by the computing device, and responsive to detecting a first domain name service (DNS) query in the intercepted outbound network traffic, converting the DNS query to a first secure DNS query and sending the first secure DNS query to a trusted external DNS recursive resolver using a secure DNS protocol, receiving a first secure DNS response that includes a first DNS resolution result, adding an entry for the first DNS resolution result to a cache, and dropping a first outbound packet detected in the intercepted outbound network traffic based on a determination that the destination of the first outbound packet does not match an entry in the cache.

Abstract: The present disclosure provides examples of methods and systems for estimating an operational state of a semiconductor chip of a power semiconductor device which are based on determining a parameter indicative of an operational state of the semiconductor chip including an indication of uncertainty of the operational state based on the received operational data. The present disclosure further provides examples of methods and systems for predicting remaining useful life of a semiconductor chip.

Abstract: A system for detecting an event is provided. The system includes a computing device including at least one processor in communication with at least one memory device. The at least one processor is programmed to execute a model for analyzing a time series of data, receive a labeled time series of data including a plurality of variables at a plurality of points in time, analyze the labeled time series of data, generate a causal graph of an event based on the analysis, calculate a predicted value for one or more variables of the plurality of variables at a specific point in time, compare the predicted value to an observed value for the one or more variables, and adjust the model based on the comparison.

Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.

Abstract: According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Abstract: According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Abstract: The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation.