Abstract: A method predicting and avoiding faults that result in a shutdown of a wind turbine includes receiving operational data of the wind turbine. The method also includes predicting, via a predictive model, current or future behavior of the wind turbine using the operational data. Further, the method includes determining, via a fault detection model, whether the current or future behavior indicates an upcoming short- or long-term fault occurring in the wind turbine. Moreover, the method includes determining, via a prescriptive action model, a corrective action for the wind turbine based on whether the future behavior of the wind turbine indicates the upcoming short- or long-term fault occurring in the wind turbine. Thus, the method also includes implementing the corrective action during operation to prevent the upcoming short- or long-term fault from occurring.

Abstract: A noise suppressing heat exchanger (also referred to as heat sink) includes a plurality of heat dissipating fins formed with baffles. The baffles suppress noise from a fan by slowing air flow and creating internal reflections within the heat exchanger that reflect noise away from the air flow path, absorbing sound energy and potentially setting up standing waves which dissipate noise via destructive interference. Other embodiments may be described and/or claimed.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to monitor thermal degradation of a compute device. One such method includes calculating, by executing instructions with processor circuitry, a thermal degradation value based on an equation, the equation generated based on testing of thermal interface materials having varying degrees of degradation. The method also includes comparing, by executing instructions with the processor circuitry, the thermal degradation value to a thermal degradation threshold to determine whether the thermal degradation threshold is satisfied, and, when the thermal degradation threshold is satisfied, triggering generation of a thermal degradation alert.

Abstract: The example embodiments are directed to a system and method which can perform a text-based search for a temporal data pattern in time-series data. The text-based search process is significantly faster than a distance measurement-based search performed based on temporal pattern comparisons. In one example, the method may include storing previously recorded temporal patterns of time-series data, determining a set of optimal bin boundaries based on the previously recorded temporal patterns, where the set of optimal bin boundaries divide the observed range of time-series data into a plurality of discrete bins each labeled with a respective symbol, transforming the previously recorded temporal patterns of time-series data into symbol strings based on the set of optimal bin boundaries, where a symbol string is based on data points in the plurality of discrete bins, and storing the symbol strings within a symbol storage.

Abstract: According to some embodiments a competence module is provided to: receive an objective; select a machine learning model associated with the objective; receive data from the at least one data source; determine at least one next input based on the received data; determine whether the at least one next input is in a competent region or is in an incompetent region of the machine learning model; when the at least one next input is inside the competent region, generate an output; determine an estimate of uncertainty for the generated output; when the uncertainty is below an uncertainty threshold, the machine learning model is competent and when the uncertainty is above the uncertainty threshold, the machine learning model is incompetent; and operate the physical asset based on one of the competent and incompetent state of the machine learning model. Numerous other aspects are provided.

Abstract: The example embodiments are directed to a system and method which can perform a text-based search for a temporal data pattern in time-series data. The text-based search process is significantly faster than a distance measurement-based search performed based on temporal pattern comparisons. In one example, the method may include storing previously recorded temporal patterns of time-series data, determining a set of optimal bin boundaries based on the previously recorded temporal patterns, where the set of optimal bin boundaries divide the observed range of time-series data into a plurality of discrete bins each labeled with a respective symbol, transforming the previously recorded temporal patterns of time-series data into symbol strings based on the set of optimal bin boundaries, where a symbol string is based on data points in the plurality of discrete bins, and storing the symbol strings within a symbol storage.

Abstract: According to some embodiments, system and methods are provided, comprising a competence module to receive data from at least one data source; a memory for storing program instructions; a competence processor, coupled to the memory, and in communication with the competence module, and operative to execute program instructions to: receive an objective; select a machine learning model associated with the objective; receive data from the at least one data source; determine at least one next input based on the received data; determine whether the at least one next input is in a competent region or is in an incompetent region of the machine learning model; when the at least one next input is inside the competent region, generate an output of the machine learning model; determine an estimate of uncertainty for the generated output of the machine learning model; when the uncertainty is below an uncertainty threshold, determine the machine learning model is competent and when the uncertainty is above the uncertainty t

Abstract: A system and method including, for each component of a system, defining filter flags that identify measurements that correspond to a particular operating condition of the respective component, the identified measurements being sensor measurements relevant to build a predictive model of expected output for each component of the system; defining input sensors for each of the components; defining at least one output sensor for each of the components; filtering data from the system based on the defined filter flags for each respective component; building, based on the defined input sensors for each respective component, a predictive model for the defined output sensor; determining a divergence between actual data values and expected values predicted by the model for each respective component; determining a component-specific anomaly score for each component of the system; and storing a record of the component-specific anomaly score for each component of the system.

Abstract: In one aspect, a method includes: receiving information defining a plurality of different actions that may be performed by users; receiving information indicating a relative frequency at which each of the different actions was performed by each of a plurality of users over each of one or more periods of time; determining a plurality of different characteristic behaviors based at least in part on the information indicating the relative frequency at which each of the different actions was performed by each of the plurality of users over each of one or more periods of time, wherein each one of the different characteristic behaviors defines a relative frequency of performance of each of the different actions; receiving information indicating a relative frequency at which each of the different actions was performed by a user over a period of time; and determining a representation of the relative frequency at which each of the different actions was performed by the user over the period of time as a weighted combina

Abstract: In one aspect, a method includes: receiving information defining a plurality of different actions that may be performed by users; receiving information indicating a relative frequency at which each of the different actions was performed by each of a plurality of users over each of one or more periods of time; determining a plurality of different characteristic behaviors based at least in part on the information indicating the relative frequency at which each of the different actions was performed by each of the plurality of users over each of one or more periods of time, wherein each one of the different characteristic behaviors defines a relative frequency of performance of each of the different actions; receiving information indicating a relative frequency at which each of the different actions was performed by a user over a period of time; and determining a representation of the relative frequency at which each of the different actions was performed by the user over the period of time as a weighted combina

Abstract: A system and method for text-mining to conduct diagnostics and prognostics using temporal multi-dimensional sensor observations is disclosed. A computer device stores historical time-series data for a plurality of systems. The computer device collects current time-series data from one or more sensors of a first system. The computer device compares the current time-series data to the historical time-series data to identify patterns in both the current time-series data and the historical time-series data. The computer device generates a failure likelihood prediction for the first system based on the identified patterns in the current time-series data and the historical time-series data.