An error diagnosis method includes: the parameter value obtaining step of obtaining multiple parameter values; the error detection step of calculating a Mahalanobis distance from a unit space based on the obtained parameter values and diagnosing whether or not error is caused at the real machine based on the calculated Mahalanobis distance; the error portion estimation step of estimating a error portion of the real machine based on the Mahalanobis distance calculated at the error detection step; and the matching determination step of structuring an error analyzing model for analyzing the real machine based on the error portion of the real machine estimated at the error portion estimation step and determining whether or not an output analytical signal of the real machine obtained by analysis of the error analyzing model and the output signal output from the real machine match with each other.

Embodiments are directed towards automatically identifying, configuring, monitoring, controlling, managing, and maintaining a machine, via collection computers in communication with the machine components. The components include ID Tags that store identification data, such as a component type and a unique identifier. Interrogation of the ID Tags enables the automatic identification and configuration of the machine. Data provided by the sensors, during usage of the machine, enables the remote monitoring and managing of the usage, as well as maintaining of the machine. Machine maintenance includes automatically predicting and scheduling the replacement of various components. Embodiments provide suggestions for suppliers of replacement components, as well as suggestions for alternative components that may be better optimized for the configuration and usage of the machine. Heuristics and crowd-generated data, via machine user social networks, inform predictive analyses employed to automatically identify, configure, manage, operate, and maintain the machine.

The invention relates to an industrial system comprising machines, systems for controlling machines connected by a first communication network, and a gateway intended to connect the first communication network to a second communication network. The gateway comprises a memory and comprises a processor configured to copy to the memory first data transmitted over the second communication network and relating to the operation of the machines.

A method and apparatus for detecting an abnormality of a manufacturing facility is disclosed. According to an example embodiment of the present disclosure, a learning model generating method for manufacturing facility abnormality detection may include receiving a measured value for a normal state of a manufacturing facility collected through a multi-sensor on a time-by-time basis, generating a learning model including a predetermined weight set and training the learning model using the measured value, and determining, using the learning model, a threshold corresponding to a boundary between the normal state and an abnormal state of the manufacturing facility and a criterion for determining the abnormal state in a local window representing a predetermined time interval.

Systems and methods for autonomous provision replenishment are disclosed. Parts used in a manufacturing process are stored in an intermediate stock queue. When the parts are consumed by the manufacturing process and the number of parts in the queue falls below a threshold, a provision-replenishment signal is generated. One or more self-driving material-transport vehicles, a fleet-management system, and a provision-notification device.

A computer-implemented method using a processing unit and including providing to or determining by the processing unit a totality of process variables which is available for processing for the plurality of actuators and/or sensors of a subunit, using the processing unit to determine a subset of process variables out of the totality of process variables processing, during production of a production lot, those process variables which belong to the determined subset of process variables for those process variables which do not belong to any of the determined subset of process variables, and processing a selected number of process variables. In addition, a production cell has a processing unit configured to carry out the computer-implemented method, and computer program is provided to implement such a method.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. An embodiment of the present invention is configured to chronologically store log data in a first storage medium, and store attribute information indicating a relevance between a type of a failure expected to occur in a facility and each of a plurality of data users; The embodiment is further configured to select, at the occurrence of a failure in the facility, a data user who has a relevance to a type of the failure from among the plurality of data users based on the attribute information, selectively read log data relating to an operation state of the facility in which the failure has occurred, and transmits the read log data to the selected data user.

A system for monitoring one or more conditions of an automation system of a semiconductor factory includes one or more instrumented substrates, one or more sealable containers and one or more system servers. The one or more instrumented substrates include one or more sensors. The one or more sensors measure one or more conditions of the one or more instrumented substrates as the one or more sealable containers transport the one or more instrumented substrates through the semiconductor factory. The one or more sealable containers also receive sensor data from the one or more sensors included on the one or more instrumented substrates. The one or more system servers are configured to receive the sensor data from the one or more sealable containers. The one or more servers are configured to identify one or more deviations in the measured one or more conditions.

A substrate processing system includes: an acquiring unit configured to acquire process data of each step when each step included in a predetermined process is executed under different control conditions; an extracting unit configured to divide each step into a first section in which the process data fluctuates and a second section in which the process data is converged, and extract first data belonging to the first section and second data belonging to the second section from the process data; and a monitoring unit configured to monitor the process data by comparing one or both of an evaluation value that evaluates the first data and an evaluation value that evaluates the second data with corresponding upper and lower limit values.

A safety interlock recommendation system includes at least one process data source, an edge device, wherein the process data source is configured for providing IOS device stream data to the edge device; wherein the edge device comprises an operational technology edge application unit, OT edge application unit, and a stream analysis unit; wherein the OT edge application unit is configured for providing operation technology stream data, OT stream data; wherein the stream analysis unit comprises an online machine learning model, being configured for determining online analysis data using the provided process stream data and the provided OT stream data; wherein the OT edge application unit is configured for determining a short-term recommendation using the online analysis data.