The present invention includes: (a) a method for improving data to be processed for bottleneck detection, by cleaning corrupt or outlier data; (b) a method for improved analysis of bottleneck data using a plurality of rules for categorization; and (c) a method for improved display and/or allowing improved user feedback for bottleneck data using multivariate analysis and display. These methods can be used alone, or preferably be combined in whole or in part together to improve performance of an industrial process. A system is also provided.

There is provided a configuration that includes a device-status-monitoring controller that stores, in a storage section, device data generated by the apparatus; an analysis-support controller that acquires the device data related to abnormality analysis information from the storage section based on basic information that includes: information that defines an abnormal event, at least one of the device data corresponding to the abnormal event, and step information indicating a step where the at least one of the device data is generated; and recipe-specific information that includes at least a recipe name; and a display device that displays the device data related to the abnormality analysis information in a manner that goes back to a past time from a time when a latest recipe specified by the recipe name is executed.

A plant control system includes a plant system and a control system controlling the plant system. Runtime conditions of an operating point of the plant control system are received. The runtime conditions include a runtime state of the plant system, a runtime output of the plant system, and a runtime control action applied to the plant system. Reference conditions of a reference point corresponding to the operating point are determined. Stability radius measures of a state difference, an output difference, and a control action difference are computed. One or more of an observability anomaly indicator, health observability indicator, tracking performance anomaly indicator, tracking performance health indicator, controllability anomaly indicator, and controllability health indicator are determined based on respective spectral correlations between two of the stability radius measure of the output difference, the stability radius measure of the state difference, and the stability radius measure of the control action difference.

Techniques for qualifying for use in an overall manufacturing process items produced by a bulk manufacturing process that has a plurality of batch effects are presented. The techniques can include obtaining a collection of items produced by a bulk manufacturing process that has a plurality of batch effects; measuring a quantifiable property of a sample of items from the collection of items; developing a linear mixed model for the quantifiable property based on the measuring; determining a statistical process control standard deviation for the collection of items based on the linear mixed model; computing a statistical process control parameter from the statistical process control standard deviation; determining that at least a portion of the collection of items conform to the statistical process control parameter; accepting at least a portion of the collection of items; and using at least a portion of the collection of items in the overall manufacturing process.

Securing communications from a process plant to a remote system includes a data diode disposed there between that allows data to egress from the plant but prevents ingress of data into the plant and its associated systems. Process plant data from the secure communications is then analyzed to detect conditions occurring at process plant entities in the process plant using various machine learning techniques. When the process plant entity is a valve, the mode of operation for the valve is determined and a different analysis is applied for each mode in which a valve operates. Additionally, the process plant data for each valve is compared to other valves in the same process plant, enterprise, industry, etc. Accordingly, the health of each of the valves is ranked relative to each other and the process plant data for each valve is displayed in a side-by-side comparison.

A state change detection unit obtains the data generation probability on the basis of the values of observation data and the value of a parameter of a prior distribution, obtains, on the basis of the data generation probability, a run length probability distribution of the time-series observation data acquired up to the current time point as a condition, and detects a change in the state of a facility on the basis of the run length probability distribution. Furthermore, an update unit updates the value of the parameter of the prior distribution using the values of the observation data, to generate the prior distribution to be used for calculating the data generation probability at a next time point.

A system for process control of a composite fabrication process comprises an automated composite placement head, a vision system, and a computer system. The automated composite placement head is configured to lay down composite material. The vision system is connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material. The computer system is configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions based on the inconsistencies.

A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

The invention pertains to a method of manufacturing a population of parts produced with a manufacturing device, based on the analysis of at least one statistical indicator representative of a characteristic dimension of the parts, according to which: a) a sample comprising a number n of parts is collected from among the parts produced with the manufacturing device; b) the characteristic dimension of each part of the sample is measured, and a measured value of the statistical indicator is calculated for the sample; c) a mathematical expectation of the proportion of parts which are noncompliant with respect to a specification regarding the characteristic dimension is calculated, said calculation being performed on the basis of the measured value of the statistical indicator for the sample collected and of the number n of parts of the sample; d) the mathematical expectation of the proportion of parts that are noncompliant calculated is compared with a threshold value of proportion of noncompliant parts; e) the manufacture of the parts is steered as a function of the results of the comparison of step d).

A state change detection unit obtains the data occurrence probability based on the values of observation data and the value of a parameter of a prior distribution, obtains, based on the data generation probability, a run length probability distribution when the time-series observation data acquired up to the current time point is used as a condition, and detects a change in the state of a facility based on the run length probability distribution. An update unit updates the value of the parameter of the prior distribution using the value of the observation data, to generate the prior distribution for calculating the data occurrence probability at a next time point. In cases when the current time point is determined to be a change point in the state of the facility, the state change detection unit also searches for a change indication point based on the run length probability distribution.