Different cores of a multicore processor are used to provide diagnostics of sophisticated hardware without full redundancy by static assignment of the cores during individual cycles of the control program and comparison of the outputs. A method of automatically generating diverse programs for execution by these cores may modify one program to compile two different instructions without functionally changing that program through the use of DeMorgan equivalents and diverse compiler optimizations.

A manufacturing process monitoring apparatus capable of determining a manufacturing process is anomaly, without requiring any threshold value for determining the as anomaly is provided. The manufacturing process monitoring apparatus includes a data conversion unit configured to convert process data of a manufacturing facility, a feature value analysis unit configured to analyze the converted data based on information on feature values, a data restoration unit configured to restore data for each of a plurality of categories based on the information on the feature values and information on the analyzed result, a similarity calculation unit configured to calculate a similarity for each of the plurality of categories based on the data used when being analyzed and the restored data, a category determination unit configured to determine a category of the data based on the similarity for each of the plurality of categories, a category classification unit configured to classify the category to which the process data belongs, and a process state diagnostic unit configured to diagnose a state of the manufacturing process based on a result of comparison between the determined category and the classified category.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that obtains accuracy of a cause of the irregularity based on a proportion of process data having the abnormality degree calculated by the abnormality determination unit satisfying a predetermined criterion to the plurality of types of pieces of process data, by using causal relation information defining a combination between the cause and the irregularity, which appears as an influence resulting from the cause, of each of the plurality of types of pieces of process data.

A drawing apparatus according to the embodiment includes a chamber configured to house a processing target; a drawing part configured to draw a predetermined pattern on the processing target with a charged particle beam; a resistance measuring part configured to measure a resistance value of the processing target via a grounding member grounding the processing target in the chamber; a receiver configured to receive earthquake information; a controller configured to stop a drawing process in the chamber when the receiver receives the earthquake information; and an arithmetic processor configured to determine whether the processing target is grounded on a basis of the resistance value from the resistance measuring part, wherein the controller resumes the drawing process when the arithmetic processor determines that the processing target is grounded after the drawing process is stopped.

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.

A device operating in a normal mode receives a request to switch to a functional safety mode, the request including a selection of a safety actuator and one or more of a plurality of secondary actuators. Responsive to receiving the request, the device transmits a copy of a safety message from the safety actuator to a first microcontroller to a second microcontroller. The device validates the safety message at the first microcontroller and the second microcontroller, the validation resulting in first validation values from the first microcontroller and second validation values from the second microcontroller. The device validates the first validation values against the second validation values, and, responsive to successfully validating the safety message at the first microcontroller and the second microcontroller, and successfully validating the first validation values against the second validation values, commands the device to transition from the normal mode to the functional safety mode.

An abnormality detector includes a signal output unit for detecting a sign of an abnormality based on a physical quantity acquired from a manufacturing machine and outputting a signal; and a machine learning device including state observation unit for observing, as a state variable representing a present state of the environment, physical quantity data indicating the physical quantity related to an operation of the manufacturing machine from the manufacturing machine; a label data acquisition unit for acquiring, as label data, operation state data indicating an operation state of the manufacturing machine; a learning unit for learning the operation state of the manufacturing machine with respect to the physical quantity, using the state variable and the label data; and an estimation result output unit for estimating the operation state of the manufacturing machine using a learning result by the learning unit and outputting an estimation result.

An abnormality in a device of interest is detected on the basis of the distance from a predetermined reference curve to a point represented by a first index value and a second index value in a two-dimensional plane having the first index and the second index as axes.

A system may include one or more drives configured to receive a first alternative current (AC) voltage from a power source and provide a second AC voltage to one or more load devices. The system may further include one or more safety relay devices configured to couple the power source to the one or more drives and a communication device, where the communication device includes a processor. The processor may be configured to receive a first dataset via an Ethernet communication protocol, where the first dataset may include safety connection data indicative of one or more safety commands for controlling one or more operations of the one or more safety relay devices. The processor may further be configured to transmit a second dataset to the one or more safety relay devices via a subnet infrastructure, where the second dataset includes the one or more safety commands.