In a previous blog post, we explained that contemporary marketing language or buzz words can create confusion. One example of a common buzz word that may cause confusion is the concept of “Digital Twin.” Customers are asking about it and vendors are promoting it. But, what exactly is a Digital Twin? We decided to start with the Wikipedia definition:
The digital Transformation – everyone and everything is a part of it in some way. In the 20th century, breakthroughs in technology allowed for the ever-evolving computing machines that we now depend upon so totally, we rarely give them a second thought. Even before the advent of microprocessors and supercomputers, there were certain notable scientists and inventors who helped lay the groundwork for the technology that has since drastically reshaped every facet of modern life.
New training dates have been added so now is the time to register for your dataPARC training held in Vancouver Washington just across the river from beautiful Portland, Oregon. Whether you need to escape the heat of summer, the cold of winter, or just need to get away from the plant, our hands-on training is your ticket to a welcome escape. Oh, did we mention the training?
It is that time of year again, time to gather with your peers and talk about some of the great benefits of dataPARC software. This year’s dataPARC user conference will be held at the Sentinel Hotel in Portland, Oregon from October 15- 18-2018. Besides getting to learn in a beautiful setting (Portland, OR in the fall – gorgeous!) the following are five reasons why you should attend:
The process industry is an industry that is consistently changing as processes are refined and innovative technologies are always changing the processing landscape. As complex systems are installed upgraded and monitored, expectations for profitability and smooth delivery of product remain. Soft sensors, with predictive models, provide scenarios in which estimations can drive decision-making and improve the reliability of current systems, often working hand in hand with their hard sensor counterparts thus creating comprehensive monitoring networks.
Buzzwords have always been a part of technology but recently it seems the usage has exploded. At the same time usage is growing, the terms themselves have changed and evolved. Many contemporary terms now include a wide spectrum of meaning in their definitions, applying to new applications and solutions brought to market. In some cases, you’re also seeing very liberal use of terms for marketing and sales. From our perspective, the more we can all talk a common language and the more we realize many terms are replacing old concepts, the better off we will be.
The first step toward understanding and optimizing a manufacturing process is to collect and archive data about the process. Ideally, the system used to accomplish this is a ”plant wide” information system, or PIMS, which collects not just process data, but also quality information and laboratory results, and operations information such as upcoming orders and inventory.
Historian packages were originally intended to be a support tool for operating personnel. Current and historical data was constantly displayed on a dedicated screen next to the primary control screens, and users were intended to interact with it at that location more or less continuously. As the historian became a one-stop source for all types of data throughout a facility, it became a tool that could benefit supervisory and management personnel as well. This led to the development of a variety of remote notification and reporting tools to meet the somewhat different needs of these individuals.
As an engineer in a manufacturing facility, you are excited that management has purchased and implemented a plant wide Information Management system, or PIM. This gives you the ability to collect and store process data, and to display both real time and historical process graphs which allow you and the operators to better understand the process. You can finally trend important process variables next to each other in order to visualize relationships that you suspect exist, and to use historical data for accurate diagnosis of problems, for example, was it lube oil pump failure, or loss of cooling water that led to the recent shutdown of a compressor?