Strategy and Standards Determine the Value of Your Big Data | Automation.com

Strategy and Standards Determine the Value of Your Big Data

Strategy and Standards Determine the Value of Your Big Data

By Daymon Thompson, Automation Product Manager – North America, Beckhoff Automation

Unlocking and harnessing real value in data remains foremost on engineering minds at companies seeking to capitalize on Industrial Internet of Things (IIoT) technologies and Big Data. However, “one man’s trash is another man’s treasure,” as the old saying goes. This holds true in terms of machine and production data as the priorities of a machine builder may differ wildly from the information most desired by the end user manufacturer.

The first steps required to determine the data types most necessary for each application are in the identification of key business and production challenges that IIoT technology should address. With that in mind, the company must then create a plan to store and analyze data in order to generate insights that support continuous improvement efforts within company operations.

On the end user side, this may include a wide range of information types — including categories such as direct, indirect or derived data — depending on the established goals. Direct or “raw” data may include data from sensors in the field. This data is unfiltered, and often has not even been converted into engineering units. Indirect data would include values such as motor temperature or vibration in engineering units, and would perhaps be filtered. Derived data, or figures that come from data acquisition and subsequent calculations, could include such metrics as Overall Equipment Effectiveness (OEE).

 

Data priorities for end users, machine builders

Most often, the end user generally seeks optimization of throughput and overall plant effectiveness by comparing production lines across the enterprise or by streamlining operations via supply chain management (SCM) initiatives. These efforts enable the company to make positive changes such as shifting production runs simply to maximize throughput and product variety. Another strategy could be to implement future-oriented concepts such as dynamic production allocation and object-oriented manufacturing where the IoT-enabled plant can autonomously move production steps based on machine line/module availability or other factors.

Machine builders, on the other hand, may have a different agenda regarding the types of data required and how to best implement IIoT in their operations. In our experience, machine builders use data to enhance machine performance or offer predictive maintenance services. These companies dive deep into the details of an individual machine’s operation through utilization of robust analytics software, such as TwinCAT IoT from Beckhoff. Analysis tools assist in the creation of data models that help engineers determine ideal machine operation variables and identify possible areas for improvement in terms of mechanical, electronic and software components.

 

Data analytics benefits for industrial automation

New analytics solutions also provide a wealth of new features that can bring about the perfect mix of high-performance and cost-effective operation. For example, high-level analytics software running on a PC-based control platform logs data cyclically, leveraging the speed of modern industrial Ethernet protocols to gather machine data in real-time, every PLC cycle. This may include such data as motion system performance, maximum torque, motor temperature and machine state timing, among other sources.

The collected data can then be used to drive decisions on machine component specifications to remedy any perceived areas of weakness. In addition, machine builders often require aggregated metadata to accompany the “regular” data, as a means to correlate the many variables in machine optimization. For example, machine operating temperatures may be tracked over a long period of time, but the machine builder may also use the accompanying metadata to reveal the overall state of the machine when temperature spikes or dips occur.

Analytics tools in PC-based automation software assist in the creation of data models that help engineers determine ideal machine operation variables and identify possible areas for improvement.

Established Big Data, IoT standards and protocols

Data structures and standards are important to assure industry conformance, and they represent an important first step toward regulated methods of data acquisition and transmission. Ultimately, as IoT and cloud technologies continue to make inroads into industrial markets, data and protocol standardization efforts will become increasingly important forces behind conformance and interoperability. Working groups comprised of companies and organizations such as the OPC Foundation and Microsoft are leading the charge toward IoT protocol standardization, highlighting the demand for improved data acquisition and transmission in higher-level systems while maintaining defined data structures and data access rights.

More companies are also beginning to use established IoT protocols such as MQTT to transmit data to the dashboards of plant engineering staff and decision makers. These protocols define the data transport mechanism, a channel through which the data can be moved from place to place, whether in a local database or in a public or private cloud. The format of the recorded data contained inside is not defined by the protocol, allowing the data to be packaged in a neutral format such as JSON (Java String Object Notation) or in a more compact format such as binary. Despite its origins, this lightweight data-interchange format is easy for industrial controls to create and understand, and it enables interoperability for many cloud platforms, middle layer software and analytics packages on the market.

Many companies now use established IoT protocols such as MQTT and AMQP to transmit data to the dashboards of plant engineering staff and decision makers.

This standardization of data formats represents another important step toward the increasing convergence of IT and automation. It also assures that industrial devices present all data in a widely useable format so it can be parsed out to find real value wherever it hides. Hardware and software tools are available today that can help generate more actionable data, regardless of a company’s individual priorities and focus areas.

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