Data is received that includes a feed of images of a plurality of objects passing in front of each of a plurality of inspection camera modules forming part of each of a plurality of stations. The stations can together form part of a quality assurance inspection system. The objects when combined or assembled, can form a product. The received data derived from each inspection camera module can be separately analyzed using at least one image analysis inspection tool. The analyzing can include visually detecting a unique identifier for each object. The images are transmitted with results from the inspection camera modules along with the unique identifiers to a cloud-based server to correlate results from the analyzing for each inspection camera module on an product-by-product basis. Access to the correlated results can be provided to a consuming application or process via the cloud-based server.

An abnormality symptom analyzing device comprises: a data request receiver to perform request and reception of data, required for analyzing an abnormality symptom(s), including measurement information obtained from a sensor(s) of a facility, an abnormality symptom(s) detected from the measurement information and inspection information of the facility; a data storage device to store the data acquired by the data request receiver; a coordination circuitry to perform the registration of data to display a screen display component(s) on a display device and the correspondence of data coordinated between screen display components to a screen display component(s) coordinated, and to store information modified according to input contents from an input device, whereby modification contents are coordinated to the screen display components; and a component graphics-drawing management circuitry to acquire data through the coordination circuitry, and to display details information of the facility and propagation information of an abnormality symptom(s) in signals.

According to one embodiment, a manufacturing apparatus control system includes a defect rate detector, a significant difference tester and a defect determining unit. The defect rate detector extracts a first apparatus passage history having a first defect rate. The defect rate detector detects a third defect rate by excluding a second apparatus passage history having a second defect rate from the first apparatus passage history. The significant difference tester calculates a significant difference test value. The defect determining unit extracts a third apparatus passage history based on the third defect rate and the significant difference test value.

A production system and method may comprise a first production processing machine capable of processing a workpiece and a second production processing machine capable of processing the workpiece. The production system and method may also comprise a workpiece transfer device, the workpiece transfer device moving the workpiece from the first production processing machine to the second production processing machine, an inspection device identifying whether the workpiece meets at least one specification of the workpiece, and a computing device in communication with the inspection device notifying a user whether the workpiece is compliant with the at least one specification where the computing device is operative communication with either or both of the first production processing machine and the second production processing machine whereby the computing device alters operation of either or both of the first and second production processing machines.

A method for automatic quality inspection of an aeronautical part, includes detecting faults on an image of the aeronautical part using a trained artificial neural network; training an auto-encoder on a database, by projecting each image of the database onto a small mathematical space in which the images follow a predefined probability law; for each image of the database, calculating a plurality of metrics; supervised training of a classifier from the calculated metrics; detecting faults or anomalies in the image of the aeronautical part using the auto-encoder and the classifier.

In general, according to one embodiment, a data processing apparatus includes a processor including hardware. The processor acquires state data related to a state of each product and order data related to order related to manufacturing of each product. The processor calculates a first score by using the acquired state data and the acquired order data. The first score regards a change in the acquired state data and is based on a first change model. The first change model represents the change in the state data using the order data. The processor outputs the first score.

A method for monitoring process instances that are being executed and for eliminating process anomalies in the process instances is provided. In a monitoring step, a sensor, using a sensor definition, monitors a process log with respect to a process anomaly described in the sensor definition, and detects process instances or processes that have the process anomaly described in the sensor definition. In a correction step, for a process instance detected in the monitoring step, the process anomaly is eliminated at the runtime of the process instance.

“A system controls the quality of products in output from a cutting machine, for instance, a log saw that has at least one cutting disc and a sharpening wheel. The control system comprises a PLC controller suitable for controlling the cutting and sharpening process and an inspection system suitable for scanning at a specific time instant, a set of quality values of a roll in output from the cutting machine. A processing unit receives the set of quality values and sends to the PLC controller a command aimed at performing an action consisting of increasing or decreasing one of the following parameters: sharpening time, sharpening frequency, and differential speed between the wheel and the disc, based on the comparison between the set of quality values and a reference quality value, and the action carried out at the previous time instant”.

An industrial monitoring system comprises a monitoring device attached to an industrial device by a bond. Sensor data collected by the monitoring device during a commissioning period is received and used to train a machine learning model. Subsequent to the commissioning period, additional sensor data is collected by the monitoring device. An abnormal state of the bond between the monitoring device and industrial device is determined based on the additional sensor data and a characteristic inferred by the trained machine learning model. A notification of the abnormal state is generated.

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.