The present invention relates to a method for the automatic monitoring of an electrotechnical work flow and to a corresponding device. The method comprises the following steps: establishing (S0) a first bidirectional communications link (F1) between a mobile communications unit (1) and a mobile voltmeter (10); starting (S1) a work step sequence program on a mobile communications unit (1) by means of an input device (1b) of the mobile communications unit (1); outputting (S2) a work step instruction (A) to disconnect a live electronic component (K1) in order to effect an absence of voltage at the electronic component (K1) on an outputting device (1a) of the mobile communications unit (1) by the work step sequence program; automatically or manually confirming (S3) the disconnection of the electronic component (K1) at the mobile communications unit (1); following the confirmation (S3) of the disconnection of the electronic component (K1), outputting (S4) a work step instruction (A) to check the absence of voltage at the electronic component (K1) by means of the mobile voltmeter (10) on the outputting device (1a) of the mobile communications unit (1) by the work step sequence program; checking (S5) the absence of voltage at the electronic component (K1) by means of the mobile voltmeter (10); communicating (S6) the result of the check from the mobile voltmeter (10) to the mobile communications unit (1); if the result of the check indicates an absence of voltage, automatically confirming (S8) the check of the absence of voltage from the electronic component (K1) by outputting a confirmation signal by the mobile communications unit (1); if the result of the check does not indicate an absence of voltage, outputting (S9a) a first alarm signal by the mobile communications device (1).

Methods, apparatus, systems and articles of manufacture are disclosed to manage process excursions. An example apparatus includes a digital twin comparer to determine when a product fails to satisfy a tolerance metric of a digital twin, and a fingerprint manager to generate a fingerprint corresponding to a sensor pattern. The example apparatus also includes a node interfacer to determine a number of workstations of a process control system that exhibit the fingerprint, and an excursion statistics calculator to invoke a corrective action for respective ones of the number of workstations, the corrective action based on a threshold count of the number of workstations that exhibit the fingerprint.

According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.

According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.

A machining failure detection device includes a machining light measurement unit that measures machining light generated at a machining point during machining; a machining sound measurement unit that measures machining sound generated at the machining point; and a computation unit that determines whether a machining failure has occurred in the machining. The computation unit includes a feature extraction unit, a determination value calculation unit, and a determination unit. The feature extraction unit extracts a machining light feature from a machining light signal measured by the machining light measurement unit, and extracts a machining sound feature from a machining sound signal measured by the machining sound measurement unit. The determination value calculation unit calculates a combined failure determination value on the basis of the machining light feature and the machining sound feature. The determination unit compares the combined failure determination value with a determination criterion to determine a failure.

A quality review management system may be used to analyze the operation of manufacturing processes within a plant based on data collected by various data sources in the plant, such as batch executive applications, to automatically detect, store, and display exceptions within those processes for use by a quality review engineer to determine if the process operation meets certain quality standards. The quality review management system includes a configuration application that enables a user to create one or more exception rules, an exception engine that analyses process data using the rules to detect one or more exceptions within the process, and a review application that enables quality review personnel to review each determined exception for resolution purposes.

Multilevel production processes are integrated utilizing as input a relatively lower production level. Activities at the lower production level producing an end product and physical parameters that influence these activities are identified, the identified physical parameters are measured with sensors, and a quota fulfillment probability index for an activity is determined using the measured physical parameters. The quota fulfillment probability index is supplied to a production entity operating at a consecutive, relatively higher production level than the activities at the relatively lower production level.

Provided is a technology for recording effective data, as a result of inspection on a to-be-inspected object. This inspection assistance device is provided with: a reception unit that receives information about a to-be-inspected object; an acquisition unit that acquires image data captured by an imaging device; and a recording control unit that, in the case when the imaging time of the image data falls within a prescribed time range based on the time at which the information was received being set as a reference, and when the to-be-inspected object indicated by the received information coincides with the to-be-inspected object recognized from the image data, records the information pertaining to the result of inspection on the to-be-inspected object and the information about the to-be-inspected object in association with each other.

Provided is a technology for recording effective data, as a result of inspection on a to-be-inspected object. This inspection assistance device is provided with: a reception unit that receives information about a to-be-inspected object; an acquisition unit that acquires image data captured by an imaging device; and a recording control unit that, in the case when the imaging time of the image data falls within a prescribed time range based on the time at which the information was received being set as a reference, and when the to-be-inspected object indicated by the received information coincides with the to-be-inspected object recognized from the image data, records the information pertaining to the result of inspection on the to-be-inspected object and the information about the to-be-inspected object in association with each other.

A method and a computer program for discovering possible errors in a production process for manufacturing metal products. The method involves removing at least one process parameter value from the cluster or adding at least one process parameter to the cluster. Second Z-score values, which are compared with the first Z-score values, are then determined for the thus-altered cluster. The changes in the Z-score values provide suggestions for troubleshooting and process optimization which are direct and can be implemented immediately.