In today’s industrial automation environment, the amount of data available is increasing so fast businesses are hard-pressed to keep up with it. Data comes from multiple sources now more than ever. In addition, the cost of storage space has decreased, making the increase in Big Data affordable. Under these circumstances, businesses run the risk of missing valuable data that is critical to success or, worse, storing mounds of data with little to no impact on the business.
In the past, engineers and instrumentation/electrical technicians tended to be the only ones to access and use the data, but now the entire organization uses data. So, how can businesses ensure the quality, type and quantity of stored historized data remains relevant year after year and the right people can access and use it?
With new advances in smart manufacturing and Industrial Internet of Things (IIoT)-enabled technologies on the market, businesses can capture, collect and store data from incoming raw materials to the final product to consumers. They also can discover which real-time data adds value to improve operational efficiencies and increase their competitive edge in the marketplace. The first step is understanding data acquisition systems and consider the eight essential best practices for data acquisition success.
Breaking data down bit by bit
In its simplest form, a data acquisition system (DAQ or DAS) samples signals that measure real-world physical conditions and converts the resulting samples into digital numeric values that a computer can manipulate. These systems convert analog waveforms into digital values for processing. Data acquisition components include:
- Sensors to convert physical parameters to electrical signals
- Signal-conditioning circuitry to convert sensor signals into a form that can be converted to digital values
- Analog-to-digital (A/D) converters to convert conditioned sensor signals to digital values.
Many companies have used all forms of programming languages to develop data acquisition software programs to help capture mission critical data. At the same time, numerous vendors have developed their own versions of data historians (e.g., OSIsoft PI, AspenTech IP21 and Rockwell Automation’s FactoryTalk Historian), which are used to acquire and store selected data from instrumentation and control system sources. For the first data acquisition best practice, it is essential businesses understand automation and control system sources to know which ones are right for your data acquisition needs.
1. Understand automation and control sources. Field instruments use a variety of sensors to convert physical properties, such as valve position, temperature, pressure, level, density, viscosity and more, to electrical signals interpreted via control systems. The control systems are the heart and brain of the automation process, so understanding how they fit in with data acquisition system requirements is key. The type of control system used depends on the complexity of the process being automated. The three most common control systems are:
- Supervisory control and data acquisition (SCADA): A SCADA software tool is used to view, monitor and control process variable data, while providing a graphical representation of the process via human-machine interface (HMI) displays.
- Programable logic controllers (PLCs): A PLC is an effective solution for handling data up to about 3,000 input/output (I/O) points.
- Distributed control systems (DCSs): A DCS becomes the most effective solution for handling data when the I/O point count is greater than 3,000.
Other common system platforms to consider when planning for a data acquisition system include:
- Manufacturing execution systems (MESs)/manufacturing operations management (MOM)
- Enterprise resource planning (ERP) systems
- Enterprise asset management (EAM)/Computerized management maintenance systems (CMMSs).
These platforms can collect, generate, organize and manage data that will be valuable to the business through data-analytic tools. Figure 1 is an example of how data acquisition systems and tools are networked.
2. Understand connectivity and interfaces. Getting process automation data from the control system sources and written to the data historians requires connectivity via interfaces. Understanding the different types of interfaces needed for collecting and storing the required data is the second data acquisition best practice. The interfaces often reside on a separate server and are commonly referred to as “interface nodes.”
Some of the most used interface types include:
- OLE for Process Control (OPC): OPC is an interoperable software interface standard that allows Windows programs to communicate with industrial hardware devices. OPC servers are implemented in a client/server architecture. A control system uses a hardware communication protocol the OPC server software program converts into an OPC protocol.
- OLE for Process Control-Data Access (OPC-DA): OPC-DA, developed by the OPC Foundation, was designed to eliminate the need for custom drivers/connectors to communicate with various sources. The OPC-DA standard has had multiple revisions to keep up with the changes in data sources.
- OLE for Process Control Historical Data Access (OPC-HDA): OPC-HDA is used to retrieve and analyze historical process data for multiple purposes, optimization, inventory control and regulatory compliance to name a few. OPC-HDA servers typically are used for retrieving data from a process data historian, relational database or a remote terminal unit (RTU).
- UFL: Universal file and stream loading, known as PI UFL, was developed by OSIsoft for reading ASCII data sources and writing the data to the PI data historian.
3. Properly set up buffering. “Buffering” is an interface node’s ability to access and temporarily store the collected interface data and forward it to the appropriate historian. Properly setting up buffering is the third data acquisition best practice.
To effectively perform data acquisition, it is recommended that buffering is enabled on the interface nodes. Otherwise, if the interface node stops communicating with a historian, the collected data is lost. Buffering application programming interfaces (APIs) (e.g., API Buffer Server [Bufserv] and PI Buffer Subsystem [PIBufss]) can read the data in shared memory. If a connection from a data source to the historian server exists, the buffering application also can send the data to the historian server. If there is no connection to the historian server, it continues to store the data in shared memory (if shared storage memory is available) or writes the data to disk (if shared memory storage is full). When the buffering application re-establishes connection to the historian server, it writes to the historian server the interface data contained in both the shared memory storage and the disk.
4. Effectively plan backup and archiving. Establishing efficient and effective backup and archiving plans is the fourth data acquisition best practice. It is also important to understand the difference between backing up data versus data archiving. Data backups are used to restore data in case it is lost, corrupted or destroyed. Data archives protect older/historical information that is not needed for everyday business operations but is sometimes needed for various business decisions.
Backup strategies are key for protecting current/immediate data. Most IT professionals already have established best practices for backing up all the networked systems. This applies to systems inside and outside of firewalls. Protocol documentation is critical to backing up and restoring data when things do not go as planned.
Data archiving is the practice of moving data that is no longer being used to a separate storage device. Data archives are indexed and have search capabilities to aid in locating and retrieving files. Several data backup software vendors (e.g., AWS Cloud Services, Rubrik and SolarWinds MSP) are addressing archiving in their current and future software releases. Several studies (e.g., SolarWinds MSP) are available online concerning backups versus archiving.
5. Properly set up scan classes. Understanding how to set up scan classes is the fifth data acquisition best practice. Historian interfaces use a code called a “scan class” to scan tags at different time intervals and schedule data collection. Scan classes determine a period of time in hours, minutes and seconds that tells the historian how often to collect the data. An interval and an offset define the scan class. The offset can be used to adjust specific time intervals. The offset helps avoid having two scan classes with the same frequency scanning at the same time.
The commands used for scan classes include:
- /f=SS (The frequency equals time in seconds)
- /f=SS;SS (The frequency equals time in seconds with an optional offset time)
- /f=HH:MM:SS (The frequency equals time in hours, minutes and seconds)
- /f=HH:MM:SS,hh:mm:ss (The frequency equals time in hours, minutes and seconds with an offset time)
- /f=00:01:00,00:00:15 /f=00:01:00,00:00:45 (Two scan classes with the same frequency but using offsets to avoid scanning at the same time).
Knowing the data to be collected is essential to setting up the scan class. For example, data for temperature, level, pressure and flow will need a faster scan rate. Data for starting a pump or opening a valve may only need to be written when the state changes. Properly setting up the scan classes will ensure your system runs as efficiently as possible.
6. Organizing data. A little over five years ago, the organization of collected data at the historian level was limited. As the amount of data being collected continued to grow, it became more difficult to find and group data in ways that made sense to the people consuming the data. Organizing data is the sixth data acquisition best practice.
Various software programs make it easier to organize data. The most commonly used PI Server and its asset framework (AF) component make the organization and sharing of data much easier. The AF component can integrate, contextualize, refine, reference and further analyze data from multiple sources and even external relational databases. Users can create a hierarchy of elements/assets and all their attributes including metadata.
For example, a major dog treat manufacturer has four facilities that manufacture chicken-, beef- and pork-flavored dog treats. All four facilities also have the same type of equipment, raw material storage, blenders, presses, ovens and packaging. Figure 2 gives a high-level view of the dog treat manufacturer’s element/asset hierarchy. Setting up an AF structure and performing the task properly requires individuals who have a high-level understanding of the elements/assets within the organization.
Attributes for the assets should be added at the asset detail level. For raw material storage tank CRM01, you may have the following:
- High level alarm
- Cooling on/off
- Tank capacity
- Inlet valve open/closed
- Discharge valve open/closed
- Product name.
Metadata from other sources can be set up as well:
- LOT number
- Date received.
7. Metadata use. Understanding the effects and use of metadata is the seventh data acquisition best practice. “Metadata” is defined as “a set of data that describes and gives information about other data.” Using software-coded connectors, access to data from all types of data sources is possible. Having the ability to link metadata to assets provides some unique ways to collect, analyze, visualize and report on process conditions.
Linking data from MES, ERPs, or even maintenance planning sources will make the available information even more relevant to users. Generating templatized displays will allow a user to visualize similar assets with just a single mouse-click. These content-rich displays have process-related assets and attributes, as well as various metadata details. Now the displays can show not only what is being monitored, but also other tasks, such as “time until next maintenance due” and “name, model, date of installation and runtime hours.”
Having such a high level of detail makes for better informed, data-driven business decisions. Businesses that think through their processes and identify every piece of data that can contribute to their success, and work toward acquiring that data, will be the most successful. It is important to approach the use of metadata as an extension of the live attributes being collected.
8. Obtain data from the edge. Understanding the advantage of obtaining data from the edge and knowing how to obtain it is the eighth data acquisition best practice. Collecting data from the edge is not a new concept, but it is more affordable than ever. Edge computing is a way to streamline the flow of traffic from IIoT devices and provide real-time local data analysis.
Edge devices provide an entry point into an enterprise core network. Some of the latest edge devices have the historian databases embedded for collecting data for synchronization via multiple ways of connecting. New small form-factor devices can be purchased for as little as $299. Data from sensors in the field is written to edge devices and then written to the edge infrastructure. From the edge infrastructure, the data is replicated to the centralized data center (typically in the cloud) at a low roundtrip speed of 5-10 ms. Data from the edge brings information from the most remote areas of the business to the heart of the data collection system at near real-time speed. Having as much real-time quality data available as possible for decision making will keep businesses competitive.
Valuable data leads to success
To ensure historized data remains relevant year after year and the right people can access it, consider these eight best practices as the most practical means to help determine data acquisition objectives and strategies. Also, consider consulting a third-party automation solutions provider to help implement a quality, high availability data acquisition system. They can provide a holistic view of data acquisition systems and software, while helping review the various vendor options on the market, including historians and data-analytic tools.
Today’s data acquisition technologies provide the opportunity to improve asset utilization and realize the benefits of Big Data and enhanced production processes. Achieve business gains and stay ahead of the competition with the most dependable data acquisition system and software in place.
Brian E. Bolton is a consultant for MAVERICK Technologies, a CFE Media content partner. He has more than 35 years of experience in chemical manufacturing, including more than 20 years involved with the OSIsoft PI Suite of applications, quality assurance, continuous improvement and data analysis.
Maverick Technologies is a member of the Control System Integrators Association (CSIA).
This article appears in the Applied Automation supplement for Control Engineering and Plant Engineering.