Big Data is the buzzword as industries realize its growing importance and benefits. Many sectors are investing in data analytics to bundle intelligent information from data generated by connected machines across various pocket in the plant infrastructure. Such different forms of data come from sensors, edge devices, networks, industrial protocols in the form of signals, discrete numbers, logs etc.
The amount of data generated by industries is enormous and is constantly increasing. Some industries generate up to 8 gigabytes of data per day. Heterogenous industrial data must be strategically managed to support intelligent and timely business decisions. This is where the role of a data historian becomes critical for smooth integration, storage, and access to various industrial nodes across the entire infrastructure.
Historian and its Use in Industry 4.0 / IIoT
Data historian is a part of industrial automation solutions that helps end-to-end data management. Digital transformation processes help industries make data-driven decisions for maximizing operational excellence and profit. Some advantages of deploying data historians are:
- Data accessibility: Data historians can collect data from multiple sources and store it in a structured and secure format. Object linking, OPC UA, etc., are some protocols that collect, analyze, and process to produce customized data output.
- Cost reduction: Data compression algorithms used by data historians help store large data volumes efficiently for extended periods. The maintenance costs are reduced significantly by data compression. Moreover, databases can be accessed by systems like MRP, ERP, SCM, etc, which reduces data loss and integration costs.
- Easy access: Compared to relational databases, data historians are faster in storing or retrieving data in real time. Thus, data is available 24X7 for visualizations or analysis.
Evolution of HISTORIAN with IIoT and Big Data
Data historians had supported product design and development teams in industries since the 1970s, when the first general-purpose computers were introduced in the market.
The older data systems were time-series databases that were deployed on the industry’s premiss. As a result, very little data was clocked, and the focus was on data visualizations only.
With the advancement in technologies and the onset of the digital world, the focus has shifted to cloud computing, artificial intelligence, and IIoT platforms. Due to these changes, the industrial engineering teams expect data historians to have enhanced data wrangling capabilities.
This includes data identification, metadata addition, data relationship mapping, and dataset mobilization to various servers.
The old and standard data aggregation process has become obsolete. Product engineering services teams are looking for end-to-end data management and digital transformation services.
How HISTORIAN Improve the OEE
OEE, (Overall Equipment Effectiveness) is a benchmark to quantify manufacturing productivity. A 100% OEE score points to the fact that an industry produces high-quality products without any downtime.
Once the industrial processes are automated, the OEE benchmark becomes even more critical. Data historian improves OEE scores in many ways like:
- Bidirectional communication is possible with advanced data historians.
- Data storage, processing, and analysis can be achieved in real-time. Thus, building and integrating machine learning models with batch analytics becomes easy.
- 24X7 data access helps in monitoring the industrial equipment and create real-time alerts.
- Data encryption technology makes the system safe and compliance ready
How HISTORIAN is Dominating the NextGen Industrial Data
The data historian is evolving with technology innovations and industry requirements. Simple data storing in the 1970s has changed to data architecture and network infrastructure.
As per the Industry 4.0 requirements, features like data integration, asset modeling, visualization, analysis, etc., should be part of industrial automation solutions.
The future of data historians has much more data crunching and analysis in store for it. In addition, operations data historians are challenging to work with and expensive to implement.
Moreover, they have limited visualization and analysis capabilities. These data historians are not scalable across multiple platforms also. Thus, it becomes difficult for the system to process large volumes of data.
The key technologies that future data historians need to incorporate are:
- Data wrangling: Data is the new gold for industries. If data quality is poor, extracting insights from it will be a painful task. Thus, data historians should have capabilities like data aggregation, data cleansing, data enrichment, etc.
- Digital twin: The digital twin concept is to replicate the industry’s processes and products virtually. The virtual world provides the capability to model a product’s attributes based on the data associated with it.
- Blockchain: It is a record-keeping technology that facilitates transactions through decentralized networks. No central authority can control the data in the blockchain ecosystem. Thus, the data remains safe and secure.
- OPC UA: It is the primary communications protocol for Industry 4.0. OPC UA enables hassle-free communication between heterogeneous machines. This open industry framework with global standards and specifications saves a lot of time, effort, and cost in collecting and sharing data for analysis.
The takeaway
The automation journey for industries isn’t a straight path. Every organization must constantly upgrade its automation footprint and get into the next level to survive the competition in around Industry 4.0 . Utthunga takes pride in introducing its highly skilled resources to execute niche and advance automation for industrial engineering services.Utthunga’s industrial experts are fully equipped to provide digital transformation consulting and Testing as a service for various industrial automation applications. For any specialized solution and services, reach out to us at [email protected]
