How many cubic meters were produced this morning and what is my logistical capacity for the day?.How much primary energy has been used to produce this month.For how much time has a particular piece of equipment been in operation over the last 48 hours?.In more concrete terms, here are a few examples of the wide variety of questions that a data Historian can provide answers to: Similarly, two entities that in principle are perfectly identical, may have very different productivity ratios. It might thus be possible to bring to light a production problem, which at first sight seems unimportant and/or site-specific, but may in fact be jeopardizing an entire supply or production chain.
There may be interest in reviewing the performance of facilities according to their administrative sector, or in comparing different plants over different periods, etc. Comparative analysis may be called for across different plants or geographical areas, for example.
More broadly, correlation is also applicable to the geographic dimension.
But the date must also be correlated in line with the team at the controls, the equipment in operation, the alarms that appeared, plus the time taken to acknowledge them, etc. The concepts of timestamping and performance are therefore central to running a data historian.įor a data Historian, the primary requirement is to be able to correlate the data along a time-axis, according to the relevant base of each day, week, season or process followed. The dataset is stored chronologically on appearance or execution such as to reduce retrieval times to a minimum and to maximize data reliability. Furthermore, these recordings have to be picked up from a very different types of data sources, such as PLCs, DCSs, RTUs, proprietary interfaces on machines or measuring instruments and third-party computer systems as well as manually entered data. Any types of data can be involved, including digital and analog values, information about alarms, aggregations and statistical calculations, as well as information about equipment and the quality of the running of the processes themselves. The datasets put into play are at once functionally coherent and technically different, which means they must be very precisely captured and recorded.
Yet with perfectly organized and accessible operational data, operators and managers can be much more well informed, and therefore in a position to make decisions quickly that will improve productivity, quality and efficiency.Ī data Historian is a software solution that allows the user to replay entire process sequences, for instance to analyze a particular behavior.
So, to scale up whilst contextualizing the data and at the same time addressing volume constraints, means a standard database just won’t do the job. Space requirements to store this data can be very large. Saving data related to the operation of a process and its environment is of course essential for post-event analysis and for optimizing plant operations.