Data-Driven Trends

The tried and true industrial data collection methods of the last several decades will expand into the future as Big Data and IIoT technology developments lead to more data, analytics and related benefits.

Bill Dehner, Technical Marketing Engineer at AutomationDirect, wrote an article for the September-October issue of IEN titled Data-Driven Trends.

Dehner says many automation professionals are becoming more data-driven and are trying to stay at the crest of this new technology wave.

Data collection is common at most manufacturing facilities, along with at least some level of analysis. Ask any automation professional and most will say it’s very important or at least somewhat important. Data has always been important to the success of plants and facilities—so it has been collected, stored and examined for decades.

Data-Driven Trends

This AutomationDirect Productivity2000 controller offers significant data logging capabilities with 32 GB of storage space, filling the needs for many data collection applications.

The first step is data collection, starting with sensors, the “things” in the IIoT. Sensors abound in machine automation and discrete part manufacturing applications, and they are commonly connected to PLCs and other controllers, and sometimes directly to human machine interfaces (HMIs). It’s important that these PLCs have extensive data logging capabilities, as found in modern controllers.

Common data collected includes discrete on/off values such as motor status, valve status, position, presence, etc. Leading analog sensor measurements include temperature, pressure, flow, level, current, speed, weight, etc. Data collection and storage components often convert this raw data into summary information as KPIs, OEEs, etc.

The biggest area of advancement related to Big Data and the IIoT will be data analysis, Dehner says.

Much is being developed now, such as cloud analytics, but it has not fully matured. Currently what is popular is simply importing or copying and pasting data into Excel where it is computed, sorted and graphed. Specific data analysis software, such as Seeq and other advanced analytic applications, are relatively new but growing rapidly in popularity.

Not surprisingly, analysis is focused on process improvements through diagnostics, preventative maintenance, enhanced troubleshooting, optimization, etc. Data related to quality control, energy efficiency and production forecasting are also being analyzed to improve the bottom line.

Data collection tools are well developed, creating a large amount of data, and new smart sensors and wireless devices will add to the available data. Cloud-based storage of this data using IIoT methods will mature, along with better analysis tools.

Much of this advanced infrastructure is being developed now, but don’t wait to collect the data because it is valuable now, and will become more so as technologies to exploit it progress.\

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