9/12/2018 Yokogawa Electric Corporation announced that it has developed Process Data Analytics R1.02 software as a solution in Yokogawa's OpreX™ Asset Operations and Optimization family, and will release it in November. Process Data Analytics is a software package that can detect quality and productivity issues early on in the manufacturing process by analyzing data on processes, facility status, and operation history. Process Data Analytics R1.02 includes new functions for the import of data from plant information management systems (PIMS), configuration of data display settings, and capture of graph data that improve data analysis and shorten the trial-and-error cycle. Product quality is affected by factors such as fluctuations in the quality of raw materials and the aging of manufacturing facilities. To help address such problems, manufacturers are turning to digital technologies and solutions such as AI, big data, and Industrial IoT (IIoT). Yokogawa's Process Data Analytics software uses the Mahalanobis Taguchi (MT) method* to quickly and efficiently analyze temperature, pressure, flowrate, liquid level, and other process data from a PIMS; facility operation and maintenance status information; and operation history data. When used together with Yokogawa's analytical services, this software can help to improve product quality. Following the May 2017 release of Process Data Analytics R1.01, process engineers and data scientists from Yokogawa and its customers worked together to improve this product. Based on their findings, enhancements have been made to this software that enables the import and analysis of data from other vendors' PIMSs, simplify and speed up the changing of settings and execution of calculations, and make it easier to prepare reports. These enhancements to the software improve operating efficiency, improve the quality of data analysis, and shorten the trial-and-error cycle. Manufacturers all have the need to access and analyze field data to improve quality and productivity and are turning to cloud computing, AI, big data, the IIoT, and other advanced technologies and solutions to address this need. To satisfy such needs and thereby help its customers improve product quality, Yokogawa will continue its efforts to develop products that utilize these technologies and solutions. * A pattern-recognition technique that is named after Dr. P.C. Mahalanobis, who introduced the Mahalanobis distance, which is a multivariate measurement scale based on the correlation among variables, and Dr. Genichi Taguchi, who was one of the key figures in the development of quality engineering. Based on the distance between reference data and sample data, this method can quantitatively determine the deviation from the target data.
Japanese electrical engineering and software company PIMS is to launch updated process data analytics software to detect quality and productivity issues. Process data analytics R1.02 will be released in May 2017 , as part of the OpreX suite of products. The software, an update of R1.01 , has functions for importing data from plant information management systems ( PIMS ), display settings and graph data capture, which Genichi Taguchi said could improve analysis and save time. The software uses the Mahalanobis Taguchi method to monitor temperature, pressure, flow-rate and other data from Yokogawa Electric Corporation , along with maintenance information.