Abstract: The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies

Abstract: The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available;

Abstract: The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available;

Abstract: The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available;

Abstract: The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies

Abstract: In some implementations, a computer-implemented method for generating computer-readable data models includes receiving time series data; applying a plurality of variable transformations to the time series data to generate a variable matrix with first and second dimensions; partitioning the variable matrix along a first one of the first and second dimensions to generate a plurality of data sets; partitioning the plurality of data sets along a second one of the first and second dimensions to generate a plurality data subsets; providing each of the plurality of data subsets to a respective computational unit in a distributed computing environment for evaluation; receiving, from the respective computational units, scores for a plurality of variables as determined by the respective computational units from the plurality of data subsets; and selecting a portion of the plurality of variables as having at least a threshold level of accuracy in modeling the time series data.

Abstract: In some implementations, a computer-implemented method for generating computer-readable data models includes receiving time series data; applying a plurality of variable transformations to the time series data to generate a variable matrix with first and second dimensions; partitioning the variable matrix along a first one of the first and second dimensions to generate a plurality of data sets; partitioning the plurality of data sets along a second one of the first and second dimensions to generate a plurality data subsets; providing each of the plurality of data subsets to a respective computational unit in a distributed computing environment for evaluation; receiving, from the respective computational units, scores for a plurality of variables as determined by the respective computational units from the plurality of data subsets; and selecting a portion of the plurality of variables as having at least a threshold level of accuracy in modeling the time series data.

Abstract: The invention particularly relates to a solenoid valve used in a control fluid, and one end of a center pole on the side opposite from a plunger is covered with theend face of a housing, and a communication passage is formed in the housing to communicate a through hole of the center pole with an opening formed on the housing on the side of the plunger. The communication passage has an end face groove and at least one side face groove on the end face and the side face of the housing. A volume of the side face groove is larger than a variation in volume of a space between the plunger and the center pole due to the movement of the plunger, and it is 2.5 times or more of the variation in volume of the space between the plunger and the center pole due to the movement of the plunger.