Disclosed is a method of determining at least one tolerance band limit value for a technical variable under test. The method includes obtaining the at least one tolerance band limit value from sample tolerance band limit values of different samples, wherein the samples comprise values of the technical variable under test of the associated sample, wherein obtaining the at least one tolerance band limit value comprises using a location measure of a distribution according to which the sample tolerance band limit values are distributed, wherein the technical variable under test is distributed according to an underlying extreme value distribution function, wherein each of the sample tolerance band limit values is calculable using a sample-specific conditional probability distribution function which is a function of sample values of the sample, and wherein the technical variable relates to a physical characteristic of a product that is producible in an industrial mass production process.

The present invention provides methods and systems for manufacturing process control of photovoltaic products. Some embodiments relate to a method for tracking wafers for photovoltaic products with respect to which production tool processed them and their position within that production tool. Some embodiments relate to measuring and characterizing the critical-to-quality parameters of the partially-finished photovoltaic products emerging from the production tool in question. Some embodiments relate to display and visualization of the measured parameters on a computer screen, such that the parameters of each production unit can be directly observed in the context of which production tools processed them, which location within a specific production tool they were located in during processing, and which batch, or in the case of continuous processing, what time, the unit(s) was/where processed.

A prediction model generator of a prediction system determines as an explanatory variable, one or more status values among status values associated with a training sample to be used for generation of a prediction model, based on an importance with respect to the training sample, determines an interval to be used for prediction by evaluating accuracy of prediction with the determined explanatory variable with an interval included in a search interval being successively varied, and determines a model parameter for defining the prediction model by evaluating plural indicators for the prediction model defined by each model parameter, with the model parameter defining the prediction model being successively varied, under a condition of the determined explanatory variable and the determined interval.

An intellectual quality management method is disclosed. A heatmap risk interface is created according to the required data and the parameter configuration which are calculated using a time dependent risk priority number (RPN) equation. An intellectual audit scheduling algorithm is defined via the heatmap risk interface to automatically generate at least one audit plan. An audit program corresponding to the audit plan is performed and a plurality of problem points are selected. Intellectual root cause category recommendation is performed to the questions points. intellectual corrective actions and preventive action recommendations are performed to the problem points according to the intellectual root cause category recommendation to obtain at least one optimum corrective action and at least one preventive action. Corrective actions are performed to each audit unit according to the corrective action to solve the problem points and prevention actions are performed to each audit unit according to the preventive action.

Methods for providing feedback control of converters for converting glass tubes to glass articles include a model predictive control framework. The methods include operating the converter, providing target values for attributes of the glass articles or glass tubes, measuring the attributes for the glass articles and glass tubes, conditioning the measurement data to remove outlier data points and calculating statistics representative of the measured attributes, and determine updated settings for one or more process parameters from the previous settings, the statistical properties, and the target values, where the updated settings are those that minimize an objective control function for the converter. The methods further include adjusting the process parameters to the updated settings. The model predictive control framework enables feedback control of the converter that compensates for disturbances that act on the process.

A manufacturing defect factor searching method includes: classifying manufacturing monitoring data into a set of non-defective products having an inspection result indicating a non-defective product and a set of defective products having the inspection result indicating a defective product, in accordance with a correspondence relationship between the manufacturing monitoring data and product inspection data indicating the inspection result of the product manufactured in the manufacturing line, the manufacturing monitoring data being collected from a manufacturing line of a product and being multivariate; estimating, for each item of the manufacturing monitoring data, a mixture distribution function approximating to a statistical distribution of each of the set of non-defective products and the set of defective products; resolving the mixture distribution function into components; and generating a list of items including a resolved component having a correlation with a manufacturing quality defect from among items of the manufacturing monitoring data.

Embodiments of the present disclosure provides a method for detecting variation value comprising selecting a numerical value in a first time interval as a comparison basis; selecting a numerical value in a second time interval as a inspection interval; statistically identifying the numerical value of the inspection interval and the numerical value of the comparison basis are to detect a variation value.

A plant measurement system is arranged to monitor plant and/or plant organ movements in real-time and relies on small scale digital sensor technology. The sensor is foreseen of an affixing means easily attached to a plant or plant organ to capture dynamic plant movements. The sensor module can be integrated in large plant monitoring setups and has wireless capabilities, enabling the use in remote locations. The system determines the orientation of its sensors to assess movements over time and can process the digital data of multiple sensor modules in real-time. These data are readily available for visualization of continuous plant and/or plant organ movements. The system can be applied for monitoring plant development, stress responses and adaptation responses to internal and external stimuli. Real-time plant information is provided to detect stress conditions and enable precise environmental control.

A production equipment monitoring system 20 including: an anomaly index determination unit 23 determining an anomaly index a of a production equipment 10, based on a feature quantity obtained from an equipment information on the production equipment 10; a relevance determination unit 24 determining a degree of relevance D between each of anomalous conditions predicable to occur in the production equipment 10 and an observed condition of the production equipment 10; a detection threshold determination unit 25 determining a single detection threshold th for detecting a degree of anomaly A of the production equipment 10, based on anomaly thresholds at which are thresholds of the anomaly index a corresponding respectively to the anomalous conditions, and on the degree of relevance D; and an anomaly degree detection unit 26 detecting the degree of anomaly A of the production equipment 10, based on the anomaly index a and the detection threshold th.

The invention relates to a method and system for monitoring a plurality of critical assets (102a-102n) associated with a production/process management system using one or more edge devices (104a-104n). The one or more edge devices (104a-104n) track operations of the plurality of critical assets (102a-102n), which comprises obtaining consolidated information related to the operations of the plurality of critical assets (102a-102n). The one or more edge devices (104a-104n) then derive insights corresponding to the plurality of critical assets (102a-102n) based on the consolidated information using descriptive analytics and an AI/ML model (114) and derive a set of actionable insights to optimize the operations. The derived insights are then rendered in a real-time consolidated view, to enable a user to take immediate actions and decisions in relation to the plurality of critical assets (102a-102n).