Disclosed is a system and method to track the consumption and use period of various consumable and/or durable welding-type products via tracking the consumption and/or use of various other consumable and/or durable welding-type products. The system and method may provide alerts when the use period of a consumable and/or durable welding-type product satisfies a threshold and therefore should be replaced.

Methods and systems for monitoring a brush holder assembly and/or detecting wear of a brush in a brush holder assembly are disclosed. One method includes sending data from a plurality of remote monitoring locations to a central control unit, where the data may be evaluated in order to monitor states of brushes at a plurality of remote electrical facilities. For example, multiple images of a marker tracking longitudinal movement of the brush may be acquired. A comparison of the images, for example, a comparative imaging technique, such as pixel-by-pixel comparison, may then be performed in order to evaluate a condition of the brush, such as the wear rate, wear state, or life expectancy of the brush.

An information management system includes: a storage section storing a data model created based on product manufacturing planning, the data model corresponding to an area where manufacturing achievement data from the manufacturing process is accumulated; an information collection section configured to collect various types of data forming the manufacturing achievement data from a data source involved in the manufacturing process; and a manufacturing management section configured to store the manufacturing achievement data in the associated data model. The manufacturing management section is configured to use the various types of data to create the manufacturing achievement data according to a data structure definition template for organizing the various types of data in association with a thing performed and a situation during the performance in the manufacturing process.

A numerical control system detects a state amount indicating a state of an injection operation of an injection molding machine, generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value, generates or updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the injection operation.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

A method for collecting machine data from a machine comprising the following steps: collecting image information displayed on a graphical user interface of a machine and transmitting the collected information to a computer unit; masking the collected information or information derived therefrom to define data regions; extracting alphanumeric characters from at least one data region by means of a text recognition program; writing the alphanumeric characters into a data structure; and storing or outputting the data structure.

A failure sign detection system 100 includes: a data storage unit 160 that retains data at the time of normal operation of each action, as learning data, with respect to a manufacturing operation of a manufacturing apparatus 2 composed of a plurality of actions; a sensor (acceleration sensor 6) that measures the manufacturing operation of the manufacturing apparatus 2; an action detection unit 110 that detects an action start of each action in the manufacturing operation; a divided data collection unit 180 that divides measured data, which is measured by the acceleration sensor 6, into divided data for each action and collects the divided data; and a data analysis unit 140 that analyzes an abnormality of each action on the basis of a comparison between the divided data and the learning data of each action.

A method for a device to monitor performance of a manufacturing machine which is equipped with a fixture for holding a workpiece, the fixture having at least one fixing hole, includes obtaining actual coordinate values of the fixing hole in a plurality of machining processes and calculating a standard deviation of the coordinates in at least one axial direction according to the actual coordinate values and a preset standard coordinate value. A risk index in at least one axial direction is calculated according to the standard deviation and a preset tolerance and a determination made as to whether risk index exceeds a preset threshold. A warning is sent to the machine and supervising engineer if the risk exceeds the preset threshold. A machine monitoring device and a computer readable storage medium are also provided.

Disclosed is a system and method to track the consumption and use period of various consumable and/or durable welding-type products via tracking the consumption and/or use of various other consumable and/or durable welding-type products. The system and method may provide alerts when the use period of a consumable and/or durable welding-type product satisfies a threshold and therefore should be replaced.

A method and a system for controlling a lot risk score based dynamic lot measurement on the basis of equipment reliability index are provided. The method for controlling a measurement, according to an embodiment of the present invention, calculates an equipment reliability index of specific equipment for a specific process in semiconductor manufacturing, calculates a risk score of the specific equipment for the specific process on the basis of an equipment reliability index, and determines, on the basis of the risk score, whether to measure a semiconductor product processed by the specific equipment for the specific process. Therefore, differential quality monitoring and management is possible according to the equipment reliability index, a measuring instrument can be efficiently used, quality and yield can be improved through timely measurement, and management convenience can be increased through automatic and dynamic lot measurement control.