Industry 4.0 and the Industrial Internet of Things are allowing increased use of adaptive analytics in running our plants. But how did we get to the point where we are depending on this new expanded world of data and what does it mean?
When I began my career in 1973 as a process control engineer, the pneumatic era was ending and the digital age was just beginning—a time that became known as the Third Industrial Revolution. Pneumatic controllers were built out of an assembly of bellows, baffles, orifices and springs. Pneumatic air lines were fed under physical parameters such as pressure, temperature and flow from the production line to the control room and the responding output from the controller back to a diaphragm on the regulating control valve.
Orifices in valves were characterized in a way that best represented the nature of the loop under control. Response times were slow. Data that was collected was charted on large, round green and white charts, with perhaps three or four variables per chart. Analytics in those days consisted of an engineer sitting at his desk days or weeks after gathering data, studying the big round charts filled with squiggly blurred lines, making an interpretation, adjusting the set points and taking more data to be reviewed once again.
In the late 70s and early 80s, digital control came of age. Early in that period, Modicon introduced the programmable logic controller (PLC) for discrete control, which was soon followed by the distributed control system (DCS). Each of the major process control manufacturers introduced a DCS to the market. While control systems went digital early on, the sensor and the control valves did not. 4 to 20 mA loops were used to convey sensor data. There was no choice yet between electric and pneumatic valve actuators. The output from the DCS was sent to a current-to-pressure converter that then sent the traditional 3 to 15 psi output to the valve actuator.
Over the next decade, the architecture of the DCS system changed radically. Many proprietary and industry standard fieldbuses and communication protocols were introduced. These process-related networks included FOUNDATION Fieldbus, PROFIBUS and HART. One major advantage of this digital communication was that a large amount of information could be communicated on a single cable. Instead of one, hardwired cable for each variable, thousands of pieces of information were sent over a single cable. This meant that, in addition to the many variables a single device transmitted, multidrop systems allowed a single, two-wire cable to be connected to many transmitters and many control valves or other control devices.
Installation and maintenance costs were radically reduced. In addition, accuracy was improved through introduction of digital signal transmission. Small variations in the current loop, introduced by electrical signal interference or grounding problems would commonly cause large errors in the signal received. With digital fieldbuses, this was eliminated.
Through the 1990s and into the 2000s, DCS became the solution of choice. At the same time, PLC-based control systems evolved to include functionality that acted more like traditional DCS systems. While functionality differences between the DCS and the PLC became smaller, the input devices and technology also merged. In both cases, many of the inputs for variables in the process plant were connected using fieldbuses.
During the past decade, the Internet of Things gained strength in commercial and home applications. At the same time, the Industrial Internet of Things (IIoT) was under development. This evolution took a step forward in 2012 when a German consortium released a standard for what was introduced as Industry 4.0; the fourth industrial revolution had begun.
THE NEXT WAVE
Industry 4.0 introduced the concepts of interoperability, information transparency and decentralized decision making as standards for the future. The cloud, big data, artificial intelligence and process analytics were coming of age. Manufacturers were no longer satisfied with historians and data for a single plant or a single process. Processes and plants were being connected and analytics could identify variations in processes from production run to production run, or differences from plant to plant.
In many ways, implementing IIoT and Industry 4.0 are just beginning. Clearly, manufacturers want to gain the advantages of an IIoT system without scrapping their installed infrastructure or building new plants. This is entirely possible. Existing fieldbus devices are smart devices connected to each other and to an intelligent platform. They send data through digital protocols to a host for analysis. Using IIoT and cloud connectivity, this data can be available to monitor process conditions and learn from what’s going on with them. Data can be stored remotely and reviewed, using process analytics to improve performance.
Where fieldbus or process data connections are not yet in place, wireless connectivity can be used to expand field networks rather than a multiplexed fieldbus. This is particularly useful when islands of older, analog-based field transmitters are in place. Little cost or rework is required to make the changes.
To this point, we’ve focused on the fieldbus and input devices. However, process control for DCS or Industry 4.0-based systems need to have final control elements as well. While an increasing number of these elements are digitally driven pumps, the control valve still dominates—it continues to be a critical asset for quality control.
In the pneumatic control era, we had analog valves, and some portion of them were equipped with positioners. In many cases, the control valve of 2018 is equipped with its own “computer.” The equipment can monitor, control and transmit important status data, including air supply, electrical supply, travel stroke and other important parameters. While this data has been available for some time, the variables were seldom well understood, and local plant technicians did not have the ability or time to analyze and use the information provided. Only in the age of IIoT, artificial intelligence and process analytics can this data be used to correct valve operation, maintenance or operational performance.
Many valve manufactures have developed control valve analytics systems to deal with the complexity and overcome the local plant expertise gap. These systems identify maintenance tasks and improve performance using the information available from the onboard valve computer. When used appropriately, these systems can predict valve health and determine necessary maintenance.
In the past, data that was available was underutilized. IIoT and the functionality of Industry 4.0 are changing this reality. Leveraging IIoT technologies to process control insight is becoming a must in today’s digitized manufacturing environment.
IIoT, using the cloud and functionality defined by Industry 4.0, is changing the way process plants operate and providing radical improvements in performance and consistency. Suppliers are providing sensors, transmitters and valves that can provide and integrate with IIoT industrial process analytics. These analytics systems can read vast quantities of streaming time-series data, historical data, unstructured and unconventional data and addressing what they find.
Suppliers are leveraging cloud infrastructure services to deploy the data and connect it to process manufacturing sites. By combining data from smart devices with the process historian data such as flows, pressures and temperatures, a better outcome and more reliable predictions can be achieved. Data aggregation and contextualization can create efficiencies not previously possible.
When we view how adaptive analytics are applied to manufacturing plants in 2018, there isn’t really a way to compare it to DCS systems of the past. In the past half century, we have gone from stone to space age (or in a 1960s analogy, from the Flintstones to the Jetsons).
Today, we have big data. We have cloud computing and apps on mobile devices so we can watch production from almost anywhere. Manufacturing plants have sensors that are faster, less expensive and more comprehensive; the software is better, and more data is available.
It’s easy to see why real-time analytics are increasingly a part of our operation. The true value of these analytics is allowing functionality for decision-making to be carried out by online computing using artificial intelligence; rapid decision-making done in real-time and only monitored by a human rather than controlled by one. The biggest challenge—using all the data available from in-plant sensors and valves—is now being overcome. Industry 4.0 is changing the world of manufacturing.