Predictive analytics with data from automation and process control
Posted: Tue Jan 21, 2025 10:56 am
Discover the benefits of using data from automation and process control in industrial and production environments.
Dec 22, 2017
The IIoT or Industrial Internet of Things is part of the broader concept known as the Internet of Things , which we have talked about on several occasions. The IIoT deals with the use of Internet of Things technologies in manufacturing.
In automation and process control applications, the big data generated by this IIoT can be used to perform predictive analysis. This is the holy grail of IIoT: the ability to predict the future through the vast viber database of data generated by automation and process control .
In a digital-first world, maturity is achieved with effective data management
Process automation and control, big data and predictive analysis
The production environment is changing as demand demands change and new technological advances appear. Modular production or the incorporation of 3D printers are just some examples of how big data is already present in the factory.
In fact, when you think about a typical industrial application today, you can see that a huge amount of data is already being collected and recorded. This information includes everything related to processes, their inputs and outputs, or the variables of the control system.
Process automation and control applications, when connected, are responsible for creating the IIoT big data base that will be accessed through the cloud to practice advanced analytics processes.
Predictions will gain in reliability by being the result of:
Time series data collection , a very common format in industrial applications that consists of a set of data points with associated time stamps and is basically a recording of a variable.
The preparation of this data, which can now be considered Big Data, to ensure its cleanliness and completeness.
Its analysis, which allows for extracting significant statistics from the data by identifying correlations between data from automation and process control , trends and isolated facts that can be classified as relevant.
However, it is necessary to ensure access to a large amount of information, since this is what will allow data research to be enriched by combining it with each other, complementing it with new perspectives or broadening the vision of the field being studied.
Dec 22, 2017
The IIoT or Industrial Internet of Things is part of the broader concept known as the Internet of Things , which we have talked about on several occasions. The IIoT deals with the use of Internet of Things technologies in manufacturing.
In automation and process control applications, the big data generated by this IIoT can be used to perform predictive analysis. This is the holy grail of IIoT: the ability to predict the future through the vast viber database of data generated by automation and process control .
In a digital-first world, maturity is achieved with effective data management
Process automation and control, big data and predictive analysis
The production environment is changing as demand demands change and new technological advances appear. Modular production or the incorporation of 3D printers are just some examples of how big data is already present in the factory.
In fact, when you think about a typical industrial application today, you can see that a huge amount of data is already being collected and recorded. This information includes everything related to processes, their inputs and outputs, or the variables of the control system.
Process automation and control applications, when connected, are responsible for creating the IIoT big data base that will be accessed through the cloud to practice advanced analytics processes.
Predictions will gain in reliability by being the result of:
Time series data collection , a very common format in industrial applications that consists of a set of data points with associated time stamps and is basically a recording of a variable.
The preparation of this data, which can now be considered Big Data, to ensure its cleanliness and completeness.
Its analysis, which allows for extracting significant statistics from the data by identifying correlations between data from automation and process control , trends and isolated facts that can be classified as relevant.
However, it is necessary to ensure access to a large amount of information, since this is what will allow data research to be enriched by combining it with each other, complementing it with new perspectives or broadening the vision of the field being studied.