How to use automation technology for remote operations
Automation technology for remote operation and plant maintenance is becoming more prevalent.
As factories, distribution centers and other industrial environments become more automated, business leaders must be sure to invest in the proper technologies to maximize productivity and efficiency. Running an operation remotely could mean managing a production line from the other side of the plant or from the other side of the world, and the networking tools needed for each case are different. Cloud technology and 5G cellular networks show a lot of promise for remote work, but they have their challenges as well. Cybersecurity is the chief concern of many technology professionals in the industrial world, and many companies draw a line in the sand when it comes to connectivity and remote control. That line generally separates process automation control from the public internet via firewalls and other security measures.
Many industrial technology players address those challenges by using Industrial Internet of Things (IIoT) solutions for equipment and process monitoring exclusively and leaving controls out of the conversation. That method is producing a hybrid approach that could dominate the industry for years to come in which insights are discovered in the cloud from Big Data streaming out of the plant and actions are taken separately by personnel onsite after suggested changes are approved. The future of remote operations will be defined by the advances of today’s industrial equipment manufacturers as more data collection hardware and software are built into machinery. The proliferation of “smart” or “connected” devices in the industrial world will continue to enable more opportunity for remote operations as cybersecurity innovations solidify the efficacy of that future reality.
There are many reasons why remote operations have become the goal of industrial leaders, though they are predominantly centered on leveraging the decision-making power of Big Data and streamlining the headcount and expertise of human resources. Consider these two topics a little further. At this point in the fourth industrial revolution timeline, we have only begun to scratch the surface of the benefits promised by Big Data analytics. The potential is endless for optimizing decisions on the factory floor — from machine reliability, to product quality, to inventory control and process efficiency.
The truth is that human beings cannot compete with advanced analytics software when it comes to evaluating trends and correlations within data from multiple sources. We are even less capable of manually collecting applicable data from the floor in the first place. Sensors, cameras, scales and other data collection devices continually monitor their respective applications, which results in exponentially more data than technicians can chart during a walk-around evaluation. As industrial leaders continue to rank data collection and analysis among their top priorities, productivity and efficiency gains will be discovered around every corner. Imagine every decision from the factory floor having a clear reason backed by data instead of managing by tradition (see Figure 1). “That’s the way we’ve always done it” will no longer be a valid justification, and businesses will thrive because of it.
Managing headcount has always been a priority for managers from a budgetary perspective, but now it may be more important than ever for a different reason: a shrinking pool of skilled labor. The shortage of mechanics, electricians, machinists, equipment operators, engineers and other technical human resources is putting a squeeze on leaders’ ability to staff their facilities appropriately, especially at multiple sites. Existing skilled labor positions are being vacated at increasing rates due to the Baby Boomer generation moving into retirement and the trend among younger generations to change employers more frequently than prior norms. Capturing the knowledge of those experienced employees and quantifying their knowledge into algorithms for data-driven decisions is a key component of system-generated data-driven decisions.
There are many scenarios that could qualify as a remote operation. Imagine a generic example in which a corporate team is managing multiple production sites in different geographic locations. Consider three technology domains that will enable remote operation in our example and contribute to its proliferation in the real world: digital twins, predictive maintenance and augmented reality.
A “digital twin” of a production process is a graphical model of the line populated with metrics and key performance indicators (KPIs) from the actual process in real (or near-real) time. The digital twin may be created in 3D modeling software by modeling raw materials, conveyor belts, processing equipment, assembly robots, quality checkpoints, packaging stations and so on. Once a visual model exists, dynamic performance metrics are inserted within the appropriate portions of the digital production line. These KPIs may be variables such as times, weights, pressures, temperatures, vibration levels, speeds, part counts, reason codes for quality defects, etc. In addition to the information generated by sensors on the line, live video feeds may also supplement the digital twin for further enhancement of management capabilities. Digital twins may also be used for comparing “design” performance against actual performance. Any variation between the two outside a certain allowed band can be used as an early indicator of potential flaws in the line or equipment. It can also be used to fine-tune the process to get maximum efficiency or throughput.
“Predictive maintenance” (PdM) is a modern practice enabled by continual monitoring of machine health and performance via sensors and analytics software. The goal of collecting and analyzing machine health data (like temperature, vibration, energy use, etc.) is to be more effective with maintenance efforts. Rather than depending on traditional time-based maintenance procedures that may not be appropriate for a given application, PdM solutions allow leaders to focus their resources on assets that have the greatest risk of failure based on performance data. With PdM tools, leaders can schedule their maintenance efforts to be executed before the asset degrades to a critical failure state. The measurement, planning and evaluation of the maintenance can occur remotely, with only the actual maintenance having to occur onsite.
“Augmented reality” (AR) is an interactive technology that can be leveraged in multiple remote applications. If you’re not familiar with AR, think of a live, hands-free video conference in which the video feed interfaces with the real world around you. An AR solution may include a helmet-like wearable component that creates visual projections in the user’s immediate field of view while not obscuring their vision. One popular AR application is for a technical expert working remotely to guide a novice technician through a procedure on the factory floor. The guide, in this case, can see and hear everything in the field user’s environment and coach the novice through a procedure while populating the field of view with instructional graphics. In other AR applications, procedures can be recorded in a step-by-step fashion to create a digital instruction manual so users can educate themselves in real time while performing procedures. AR technology is actively being used in industry, particularly for service and repair.
There are two characteristics in common with each technology: collecting and analyzing massive amounts of data, and the connectivity to stream data to remote locations in real time. Cloud computing is a popular solution for the analysis and storage requirements of using Big Data. Though cloud use is commonplace in many industries, business leaders remain wary of security threats. These concerns are exponentially greater when a cloud or internet-based data source can affect a physical operation. While cybersecurity is at the top of the list of concerns for leaders, it isn’t the only challenge standing in the way of true remote operations. Network latency and reliability are also factors to consider in any case where a real-time input is needed for process controls. Does your internet service work flawlessly 100% of the time? Most of us have experienced service interruptions, and many leaders are not ready to depend on continuous cloud-based inputs to control their facilities.
A method used to alleviate some of the cybersecurity and network concerns is a hybrid cloud and edge architecture. In this approach, most of the continuous operation and analysis is carried out locally on servers or industrial computers (edge devices). The edge machines communicate to the cloud periodically and/or use predefined rules. The cloud platform receives these selected inputs, performs analysis and adjusts the algorithms that are sent down to the edge computers. This approach is like running antivirus software on a PC. The virus “definition files” are updated periodically, for example once a day, but the antivirus software runs continuously on the PC using the last available definition files even if there is no internet connection.
Automation technology for remote operation and plant maintenance is becoming more prevalent. While there are legitimate technical and practical concerns that have not yet allowed complete remote operation, the technology is evolving rapidly, and businesses are finding creative ways to realize some of the benefits afforded by remote operation.