A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

To provide a diagnosis device, a diagnosis method, and a diagnosis program capable of identifying a factor for defective machining. A diagnosis device comprises: a collection unit that collects machine data output during operation of a machine tool; a feature extraction unit that classifies the machine data according to an input factor for defective machining, and extracts a feature quantity from an aggregate of the machine data according to the input factor; and a determination unit that compares a feature quantity in the machine data output during actual machining by the machine tool with the feature quantity according to the factor, and determines a factor for defective machining based on a degree of match.

In a component mounting system, recovery processing is repeated until a recovery count number Nr is larger than or equal to a defined count number Nth in a case where a pickup defect of a component occurs, an elapsed time is measured from error stoppage of a component mounting machine to canceling of the error stoppage in which the component mounting machine is error-stopped when the recovery count number Nr is larger than or equal to the defined count number Nth, the defined count number Nth is increased within a range in which the defined count number does not exceed the upper limit value Nmax in a case where the elapsed time is shorter than a defined time Tth, and the defined count number Nth returns to an initial value in a case where the elapsed time is longer than or equal to the defined time Tth.

A plurality of production facilities and an analysis apparatus are connected through a fog network. The analysis apparatus performs a data analysis based on detection information of detectors acquired through the fog network and stores determination information relating to an abnormality of each of the plurality of production facilities or an abnormality of a production object as a result of the data analysis. Each of the plurality of production facilities determines an abnormality of the each of the plurality of production facilities or an abnormality of the production object based on the determination information stored in the analysis apparatus.

A real-time control system includes a faulty variable identification technique to implement a data-driven fault detection function that provides an operator with information that enables a higher level of situational awareness of the current and likely future operating conditions of the process plant. The faulty variable identification technique enables an operator to recognize when a process plant component is behaving abnormally to potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a stall of the process control system or a failure of the process plant component.

A technique for assisting a task of determining a tool that is a cause of a decrease in workpiece processing accuracy is provided. A machine tool capable of processing a workpiece using a plurality of tools includes a display and a control unit for controlling the machine tool. The control unit executes: a process of acquiring processing information that specifies a tool used in processing the workpiece and a processing route of the tool; a process of displaying, on the display, a three-dimensional model of the workpiece created based on the processing information; a process of receiving designation of a site with respect to the three-dimensional model displayed on the display; a process of determining, based on the processing information, an employed tool that has been involved in processing the designated site; and a process of displaying information on the employed tool on the display.

A defect pattern grouping method is disclosed. The defect pattern grouping method comprises obtaining a first polygon that represents a first defect from an image of a sample, comparing the first polygon with a set of one or more representative polygons of a defect-pattern collection, and grouping the first polygon with any one or more representative polygons identified based on the comparison.

Automatic process control of additive manufacturing. The system includes an additive manufacturing device for making an object and a local network computer controlling the device. At least one camera is provided with a view of a manufacturing volume of the device to generate network accessible images of the object. The computer is programmed to stop the manufacturing process when the object is defective based on the images of the object.

A product quality management system includes a production facility that produces a product having a target resulting parameter, estimation circuitry that estimates an active parameter for controlling the production facility in producing the product under a predetermined passive parameter condition, and control circuitry that controls the production facility based on the active parameter estimated by the estimation circuitry.

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.