A production system, a drive and an operating method provide the functions of quality measurement and mechanism diagnosis. The production system has a hierarchical processing structure. The drive includes a real system driving module and a virtual system driving module. The real system driving module generates a mechanism real operation parameter information. In a system identification mode, the drive creates at least one virtual mechanism model. The drive generates a mechanism stimulation operation parameter information according to a processing strategy of a controller, the mechanism real operation parameter information and the at least one virtual mechanism model. The controller performs a quality measurement operation, performs a mechanism diagnosis operation and/or adjusts the processing strategy according to the mechanism real operation parameter information and the mechanism stimulation operation parameter information.

A production management system 100 includes an operating state acquisition apparatus 20a and a production management apparatus 10. The operating state acquisition apparatus 20a includes a detector 21a that is retrofitted to be mounted on a production equipment 31 disposed on a manufacturing line L1 or retrofitted to be disposed in a vicinity of the production equipment 31, and which outputs a detection signal indicating an operating state of the production equipment 31, and includes a transmitter 22a that transmits the detection signal. The production management apparatus 10 includes a generator for generating information on production state of the manufacturing line L1 by use of the detection signal received from the operating state acquisition apparatus 20a, and includes a display device for displaying the generated information on production state.

An operation support system for supporting an operation to each facility installed in a plant, the operation support system may include, but is not limited to, a data storage that stores historical data which represent information regarding operations performed to the facility as history, and at least one operation support device portable by a field operator, wherein the operation support device may include a position information acquirer configured to acquire a current position of the operation support device, and to output current position information, an inspection information processor configured to add information regarding the operation performed to the facility, and to output the current position information regarding position where the operation was performed to at least the facility that is associated with the historical data to data storage, and an operation supporter configured to represent information regarding the operation performed to the facility as operation information based on the historical data, and to enable one or more further functions regarding the operation associated with the operation information to be executed.

A device includes a catalog storage unit that stores a catalog having versatility; a catalog control unit that specifies the catalog corresponding to a target machine tool to be subjected to state determination based on machine tool information including an item of facility type and acquires the catalog from the catalog storage unit; a feature quantity extraction unit that extracts a feature quantity from sensor data detected from the target machine tool; a sensor data processing unit that performs state determination of the target machine tool based on the feature quantity distribution included in the acquired catalog and the feature quantity extracted from the sensor data; a feature quantity tuning unit that performs tuning of the feature quantity distribution by mapping the extracted feature quantity to the feature quantity distribution; and a catalog updating unit that updates the catalog based on the feature quantity distribution after tuning.

A method includes identifying sets of part data associated with substrate processing equipment. Each of the sets of part data includes corresponding part values and a corresponding part identifier. Each of the sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment. The method further includes generating sets of aggregated data. Each of the sets of aggregated data includes a corresponding set of part data of the sets of part data and a corresponding set of additional non-part data of sets of non-part data. The method further includes causing, based on the sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.

The present invention relates to a processing line and a method for controlling a food processing line, the food processing line comprising a plurality of processing stations and at least one utility supply station. Further at least one food product sensor is provided, and at least one utility sensor and at least one processing sensor. A processing line controller is provided comprising a data collection module, input means for specifying at least one desired food product output characteristic, input means for specifying a nominal operating condition, an anomaly detection module configured to detect an anomaly, and a root cause module configured to determine a root cause of the detected anomaly. A corrective measure module is configured to determine a corrective measure in response to a detected anomaly and to provide the corrective measure to at least one physical actuator.

In order to realize stable quality control, provided is a quality control device (1) having an input device which receives data such as an operation condition of each device (21) to (26) in a production system (20) for producing a product; a calculation unit which assigns a value of the operation condition to a correlation formula calculated in advance and calculates a value derived from the correlation formula; and a determination unit which performs good or bad determination on a quality of a workpiece in each process, on the basis of a result calculated by the calculation unit. Further, when bad is determined as a result of the good or bad determination, the quality control device (1) calculates an appropriate value of the operation condition and sets the value to each device (21) to (26).

A production process analysis method for stabilizing the quality of the products or services. A production process analysis method includes: a step for identifying a good lot included in a group determined to be the most excellent with respect to each of a plurality of states constituting a production process; a step for classifying, in the case where at least one good lot is not shared among the plurality of states, the plurality of states into an arbitrarily selected selection state and other non-selection states, and determining again a highest-ranking group in the non-selection state that includes the good lot in the selection state as the most excellent group; and a step for identifying factors that characterize the group determined as the most excellent.

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 in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

A system for controlling a parameter relative to the quality of a tire being processed includes at least one system for detecting a parameter relative to one or more tires being processed. A control unit is programmed for comparing a succession of values of the parameter by comparing each value with at least one discard threshold and preferably with at least one warning threshold. The discard threshold divides a discard interval from an acceptable interval. The warning threshold belongs to the acceptable interval and defines one or more warning intervals. The control unit is also programmed for controlling the trend of the succession of values with respect to the discard threshold. A method for controlling quality of production of tires.