A machining step monitoring apparatus includes a data segmentation unit, a data length adjuster, and a determination processor. The data segmentation unit defines machining start time and machining end time of each single cycle, and segments, from a first physical quantity that temporally changes during the cycle machining in the machining machine, first measurement data indicating the first physical quantity from the machining start time to the machining end time for each single cycle. The data length adjuster generates the second measurement data by adjusting the data length of the first measurement data to match with the data length of the determination reference data indicating, as a reference, a change in the first physical quantity during the single cycle when the machining machine is normally operating. The determination processor compares the second measurement data with the determination reference data to determine whether the machining machine is normally operating.

A machining step monitoring apparatus includes a data segmentation unit, a data length adjuster, and a determination processor. The data segmentation unit defines machining start time and machining end time of each single cycle, and segments, from a first physical quantity that temporally changes during the cycle machining in the machining machine, first measurement data indicating the first physical quantity from the machining start time to the machining end time for each single cycle. The data length adjuster generates the second measurement data by adjusting the data length of the first measurement data to match with the data length of the determination reference data indicating, as a reference, a change in the first physical quantity during the single cycle when the machining machine is normally operating. The determination processor compares the second measurement data with the determination reference data to determine whether the machining machine is normally operating.

An example robotic welding system, includes: a robotic manipulator configured to manipulate a welding torch; and a robotic controller, comprising: a processor; and a machine readable storage medium comprising machine readable instructions which, when executed by the processor, cause the processor to, in response to initiation of a robotic welding procedure involving the robotic manipulator: prior to starting the robotic welding procedure, output at least one of a visual notification or an audible notification proximate to the robotic manipulator; and after satisfying at least one weld-ready condition, control the robotic manipulator to perform the robotic welding procedure using the welding torch.

An alarm notification system configured to assist an operator so that the operator can effectively carry out a teaching operation, etc. The alarm notification system includes: a storing section configured to, with respect to a past alarm which occurred when a program generated by a teach pendant was executed, store alarm data including a name of the program and a number of a line of the program when the alarm occurred; a judging section configured to judge as to whether or not an alarm prediction condition using the alarm data stored in the storing section is satisfied, when the program is executed again; and an alarm predicting section configured to notify the operator who is carrying out teaching of the robot of alarm information relating to the alarm, when the alarm prediction condition is satisfied.

The objective of the present invention is to acquire maintenance information easily when an alarm is generated. This control device for controlling an industrial machine is provided with: a monitoring unit which monitors the industrial machine to detect an abnormality in the industrial machine; an information acquiring unit which acquires alarm information relating to an alarm pertaining to the abnormality detected by the monitoring unit, and maintenance information relating to maintenance for dealing with the abnormality; and a display control unit which causes the acquired alarm information and maintenance information to be displayed on a display device.

Provided are a setting code generating device, an industrial machine, a setting code generating method, and a setting code generating program capable of confirming information output externally from an industrial machine. This setting code generating device is provided with: an identification information output unit for outputting, to a display, identification information relating to an industrial machine, including machine information relating to the industrial machine and state information relating to the state of the industrial machine; a setting code generating unit for generating a setting code obtained by encoding the identification information output by the identification information output unit, on the basis of an instruction received by way of an operating unit; and a setting code output unit for outputting the setting code generated by the setting code generating unit.

The invention relates to a method (100) for controlling film production, in which at least one film is produced according to at least one formula information (R), the following steps being carried out: initiation of execution (210) of a film production process such that a film is produced on the basis of a specific formulation information (R); determination of monitoring information (I) during the execution (210) of the process; determination of at least one fingerprint (F) on the basis of the specific formula information (R) and on the basis of the monitoring information (I) such that the execution (210) of the process is characterised on the basis of the fingerprint (F); and comparison of the fingerprint (F) with a control matrix (M) such that at least one comparative result is determined in order to control the film production on the basis of the control matrix (M) as an evaluation basis.

To prevent a collision of a movable part without generating an alarm excessively in manual feed. A machine learning device includes: a state observation unit that acquires, as input data, manual feed state information including a manual feed pulse waveform at a time of a manual feed operation in any manual feed operation performed in a machine tool capable of manual feed; a label acquisition unit that acquires label data indicating a distance by which a movable part of the machine tool moved within a predetermined time immediately after the manual feed pulse waveform of the manual feed state information included in the input data; and a learning unit that executes supervised learning by using the input data acquired by the state observation unit and the label data acquired by the label acquisition unit, and generates a learned model.

To prevent a collision of a movable part without generating an alarm excessively in manual feed. A machine learning device includes: a state observation unit that acquires, as input data, manual feed state information including a manual feed pulse waveform at a time of a manual feed operation in any manual feed operation performed in a machine tool capable of manual feed; a label acquisition unit, that acquires label data indicating a distance by which a movable part of the machine tool moved within a predetermined time immediately after the manual feed pulse waveform of the manual feed state information included in the input data; and a learning unit that executes supervised learning by using the input data acquired by the state observation unit and the label data acquired by the label acquisition unit, and generates a learned model.

An alarm handling system in plant process automation with a data processing device includes: at least one interface for accessing and/or processing one or more process signals and for determining corresponding process variables; an alarm configuration device for accessing and/or providing alarm configuration information including at least one setpoint for one or more determined process variables; and a prediction device for determining and processing a current rate of change of at least one process variable to predict how long it will take and/or a period until and/or predict at which date and/or time a provided setpoint and/or threshold is reached and/or crossed, and/or for determining whether and/or when at least one of the monitored and/or determined process variable values will cross the respective setpoint.