Implementations disclosed describe systems and techniques to detect anomalies in a manufacturing operation. The techniques include generating, using a plurality of outlier detection models, a plurality of outlier scores. The outlier scores are representative of a degree of presence, in a plurality of sensor statistics, of an anomaly associated with the manufacturing operation. Individual outlier scores are generated using a respective one of the plurality of outlier detection models. The techniques further include determining, using the outlier scores, a likelihood of the anomaly associated with the manufacturing operation.

Implementations disclosed describe systems and techniques to detect anomalies in a manufacturing operation. The techniques include generating, using a plurality of outlier detection models, a plurality of outlier scores. The outlier scores are representative of a degree of presence, in a plurality of sensor statistics, of an anomaly associated with the manufacturing operation. Individual outlier scores are generated using a respective one of the plurality of outlier detection models. The techniques further include determining, using the outlier scores, a likelihood of the anomaly associated with the manufacturing operation.

A method of generating an annual product quality review report includes defining at least one author for generating the report; selecting a chapter for inclusion in the report, wherein the chapter is associated with one or more predefined data requirements which predefined data requirements may be presented to the author for inclusion in the report; defining at least one system of record from a plurality of systems of record as a source of raw data for providing data to the one or more predefined data requirements. The at least one system of record provides raw data to the predefined data requirements through a tech fabric that causes the raw data to populate one or more enterprise object models to standardize the raw data.

The present disclosure relates to an artificial neural network (ANN) computing system comprising: a buffer configured to store data indicative of input data received from an input device; an inference engine operative to process data from the buffer to generate an interest metric for the input data; and a controller. The controller is operative to control a mode of operation of the inference engine according to the interest metric for the input data.

The present disclosure relates to an artificial neural network (ANN) computing system comprising: a buffer configured to store data indicative of input data received from an input device; an inference engine operative to process data from the buffer to generate an interest metric for the input data; and a controller. The controller is operative to control a mode of operation of the inference engine according to the interest metric for the input data.

A method of generating, at an IIOT device mounted to equipment of a drilling system, a relay variable, a measurement variable, or a control variable and a message having an IP address. Classifying, by application services of a cloud service provider, the relay variable, the measurement variable, or the control variable; identifying a category or a category and sub-category from a plurality of categories and sub-categories based on variable; cataloguing the relay variable, the measurement variable, or the control variable based on the category or the category and the sub-category; selecting from a library of catalogued relay variables, measurement variables, and control variables, at least one selected from a group comprising a parameter and a value; and identifying a pattern using a statistics based algorithm, the statistics based algorithm using a standard operating procedure, the parameter and the value, the pattern indicating a deviation in the standard operating procedure.

The present disclosure relates to an artificial neural network (ANN) computing system comprising: a buffer configured to store data indicative of input data received from an input device; an inference engine operative to process data from the buffer to generate an interest metric for the input data; and a controller. The controller is operative to control a mode of operation of the inference engine according to the interest metric for the input data.

The present disclosure relates to an artificial neural network (ANN) computing system comprising: a buffer configured to store data indicative of input data received from an input device; an inference engine operative to process data from the buffer to generate an interest metric for the input data; and a controller. The controller is operative to control a mode of operation of the inference engine according to the interest metric for the input data.

A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.

A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.