Shorten trial and error cycles and increase production efficiency
R&D background
Product quality is affected by factors such as fluctuations in raw material quality and aging of manufacturing equipment. To help solve these problems, manufacturers are transforming digitally through technologies such as AI, Big Data, and Industrial Internet of Things (IIOT).
Yokogawa's process data analysis software uses the MahalanobisTaguchi (MT) method* to quickly and efficiently collect temperature, pressure, flow, level and other process data from the PIMS, analyze facility operation and maintenance status information, and run historical data. Used in conjunction with Yokogawa's analytical services, the software can effectively improve product quality.
Since the release of process data analysis R1.01 in May 2017, Yokogawa worked with customer process engineers and data experts to improve the product. Thanks to their efforts, the software has been improved to import and analyze PIMS data from other vendors, simplifying and accelerating setup changes and calculations, and making reports easier. These improvements in software improve operational efficiency and the quality of data analysis, and shorten the trial and error cycle.
Features: Import data from PIMS supporting OPC standard
In various factories around the world, a wide variety of data collection systems are used from different suppliers. For effective analysis, it is important to have access to all the data collected by these systems. The process data analysis software runs on a Windows® PC and can access files converted from PIMS, DCS and PLC to CSV format. Process Data Analysis R1.02 comes with an OPC Historical Data Access (HDA) interface, a global standard for data exchange in industrial automation and other fields. Thanks to this feature, the software can easily import data that supports the standard from PIMS.
Improved operability
To facilitate analysis of the data, process data analysis R1.02 allows for the overlay of data charts for multiple processes. It is now also possible to easily modify settings such as data start point and display color depending on manufacturing conditions. Benchmarks conducted by Yokogawa analysts show that these features reduce data analysis time by 80%.
Capture/share analysis results
To facilitate the exchange of data analysis results between engineers, Process Data Analysis R1.02 adds new features, allowing engineers to capture charts showing the results of the analysis and paste the data into any general reporting software. This eliminates the amount of work required to create a chart, and analysts can focus on research and discuss the results.
future development
Manufacturers need to access and analyze field data to improve quality and productivity, and move to cloud computing, artificial intelligence, big data, IIoT and other advanced technologies and solutions to address this need. In order to meet these needs and help customers improve product quality, Yokogawa will continue to develop products that utilize these technologies and solutions.
Mahalanobis Taguchi (MT) Method*: A pattern recognition technique named after Dr. P.C. Mahalanobis. Dr. Mahalanobis proposed the Mahalanobis distance (a multivariate measure based on the correlation between variables), and Dr. Genichi Taguchi is one of the key figures driving the development of quality engineering. Based on the distance between the reference data and the sample data, the method can quantitatively determine the deviation from the target data.
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