System and method that includes mapping temperature values from a two dimensional (2D) thermal image of a component to a three dimensional (3D) drawing model of the component to generate a 3D thermal model of the component; mapping temperature values from the 3D thermal model to a 2D virtual thermal image corresponding to a virtual thermal camera perspective; and predicting an attribute for the component by applying a prediction function to the 2D virtual thermal image.

A product inspection device and method for correctly calculating consumer and producer risks irrespective of the type of distribution of products. A characteristic value representing a predetermined product characteristic is measured for each product as a product measurement value, and a standard deviation of measurement variations in measurement results is calculated as a measurement value standard deviation. The products are determined to be conforming based on whether the measured product measurement value falls within a range of a product standard. Consumer and producer risks are calculated based on the measurement variations. The calculated consumer and producer risks are respectively and successively added up and it is determined whether the number of products having undergone the adding have reached a predetermined number. If so, the added up consumer risk and producer risk are divided by the number of products to calculate a final consumer risk and a final producer risk.

The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

Performance mapping of equipment performance parameters by capturing, mapping, and/or structuralizing equipment performance data of a device for installation in a system. This includes generating performance maps which outline the expected feature performance parameter behavior of the equipment based on a set of operating parameters that capture the operating conditions. Each performance parameter on the map is representative of an operating point of specific operating conditions taken at a particular point in time. In one example, a performance parameter can be defined by an individualized set of parameter coefficients which in turn are dependent on instantaneous operating conditions. With the performance maps determined individually for devices as part of the system, and stored along with a time of testing, activities such as continuous commissioning, monitoring and verification, preventative maintenance, fault detection and diagnostics, as well as energy performance benchmarking and long term monitoring can be performed.

Provided herein are processes for preparing fluorescent 1-cyano-2-substituted isoindole compounds or N-substituted phthalazinium compounds, comprising reacting an aromatic dialdehyde or aromatic aldehyde-ketone compound with a material that contains primary amino or hydrazine groups, and assaying methods involving the processes thereof.

Provided herein are processes for preparing fluorescent 1-cyano-2-substituted isoindole compounds or N-substituted phthalazinium compounds, comprising reacting an aromatic dialdehyde or aromatic aldehyde-ketone compound with a material that contains primary amino or hydrazine groups, and assaying methods involving the processes thereof.

A press machine includes: a learning-model generating unit that uses one data from among data collected from sensors, as an objective variable, and uses data other than the one data as an explanatory variable to perform machine learning to generate a learning model for the one data, the generation being performed for all the data; a predicted-value calculating unit that inputs an actually measured value of data other than one data from among the data collected from the sensors, into the learning model for the one data to calculate a predicted value of the one data, the calculation being performed for all the data; a degree-of-abnormality calculating unit that calculates a degree of abnormality based on a difference between an actually measured value and a predicted value of the data; and a degree-of-abnormality outputting unit that outputs the calculated degree of abnormality.

A press machine includes: a learning-model generating unit that uses one data from among data collected from sensors, as an objective variable, and uses data other than the one data as an explanatory variable to perform machine learning to generate a learning model for the one data, the generation being performed for all the data; a predicted-value calculating unit that inputs an actually measured value of data other than one data from among the data collected from the sensors, into the learning model for the one data to calculate a predicted value of the one data, the calculation being performed for all the data; a degree-of-abnormality calculating unit that calculates a degree of abnormality based on a difference between an actually measured value and a predicted value of the data; and a degree-of-abnormality outputting unit that outputs the calculated degree of abnormality.

Selection of a batch file that causes an analysis device to analyze a sample successively is received by a receiver. Batch analysis data that represents an analysis result and corresponds to the batch file, selection of which is received, is acquired from a database device. Standard information, for verifying validity of an analysis performed by the analysis device, which corresponds to the batch file, selection of which is received, is acquired by a standard information acquirer from the database device. A report that describes an analysis result represented by the batch analysis data and an evaluation result in regard to validity of an analysis performed by the analysis device is created by a creator based on the acquired batch analysis data and the acquired standard information.