Making Industrial IoT a Reality for the Energy Industry
Steve Sponseller
Market Insight & Solutions, Oil & Gas
PTC

The industrial Internet of Things (IoT) has arrived and is here to stay. Today's IoT is perhaps best defined by interconnectivity. It enables better monitoring, information gathering, role-based information presentation, and operator situational awareness—and ultimately leads to improved decision-making, increased optimization, improved safety, and lowered costs.

With its abundance of remote assets, complex interconnectivity across SCADA devices and enterprise systems, and focus on safe and efficient processes, today's Energy Industry is one sector that is particularly well-positioned to take advantage of the many benefits that the industrial IoT has to offer. However, despite the potential benefits, concerns with IoT solutions' implementation, cost, and reliability remain.

Why Industrial IoT?

The industrialized world needs energy, and the demand for hydrocarbons will not diminish in our lifetimes. With the price of oil hovering around $50/barrel, companies need to reduce costs to be profitable. An unproductive day in a liquefied natural gas (LNG) facility could cost millions of dollars in unplanned downtime. The IoT could very well alleviate these kinds of problems, as it enables enterprise-wide visibility into operations. By implementing analytics and machine learning technologies, companies can detect and alert of process and equipment anomalies to proactively prevent unexpected downtime or out-of -spec products.

In addition, more producers are trying to streamline data sharing in real-time across many disparate enterprise systems from geoscience to accounting, marketing, and compliance as well as improve visibility of SCADA and measurement data from the field. All areas of the industry want to increase efficiencies and switch from preventative maintenance to less costly predictive and condition-based maintenance.

Common Challenges of the Industrial IoT

Despite its potential benefits, concerns with IoT implementation are still often top-of-mind. In today's world of cyber-attacks, security is always at the forefront of any new technology discussion. Given the volatile nature of its products and the catastrophic impact that security breaches could have on critical infrastructure—and society at large the Energy Industry is already a target for exploitation. Industrial equipment is typically designed to last for decades and the majority of connected devices today (and for the foreseeable future) will be from legacy equipment already operating in the plant or field. Many of the protocols for industrial communications to this equipment are not secure by today's standards; some were specifically stripped down and designed for low bandwidth networks back in a time of simpler security threats.

Additionally, many of today's energy companies are already struggling to support the number of connected SCADA devices, because existing telemetry networks often have bandwidth limitations for these remote assets. For example, an industrial oil and gas operator could have 20 ,000 legacy devices located remotely across multiple production sites with limited connectivity, power, and network bandwidth. Depending on the asset, there could be gigabytes of data being produced and updated in milliseconds. As more devices come online, data production will only increase exponentially and consume new levels of required bandwidth.

The data scientist that uses the analytics and machine learning applications requires high frequency data to truly understand a process or machine. Not sampling the data at a high enough frequency could result in a completely different analysis or modeled behavior—and potentially miss peaks that could be of significance. As such, the bandwidth and latency of the industry's communication networks is also a concern when implementing IoT solutions. Data is typically most valuable when it can be accessed and acted upon quickly, efficiently, and safely. For example, Cisco estimates that an offshore oil platform generates between 1 TB and 2 TB of time-sensitive data per day. With satellite, the most common offshore communication link, the data speeds range from 64 kbps to 2 Mbps. This results in 12 days to transmit one day's worth of data back to a central site for processing and 12 days is far from "real-time." This type of latency could have significant operational and safety implications.

Allieviating These Challenges with Edge Computing

In the traditional SCADA data collection architecture, all data sources in the field are polled from a centralized host. This requires all raw data to be requested and provided across the network so that it can be stored, monitored, and analyzed back in the enterprise (SCADA, Historian , analytics, and so forth). However, industry leaders are increasingly moving IoT data collection— and some analytics—to “the edge” as a potential solution to alleviate network bandwidth limitations and security concerns. The edge is the network entry points or data sources that are in the field on the opposite end of the network from the centralized host. Edge devices are the remote equipment in the field, machinery on a plant floor across the world, or any other asset that provides data in a location far from where data is acted upon. This includes routers, routing switches, integrated access devices, multiplexers, and a variety of local area network (LAN) and wide area network (WAN) access devices. Devices and sensors built for the industrial IoT with access to the network are also considered edge devices.

Here are three benefits of implementing edge computing as part of an IoT initiative.
  • Generates cost-savings and alleviates network bandwidth limitations A typical architecture for an analytics solution is to transfer the required large data sets from the field and back to the enterprise for analysis. The issue with this is that it often results in service degradation, data latency, and security concerns—on top of new expensive levels of required bandwidth for the needed high frequency data. Edge computing reduces the need for costly bandwidth additions. Low-cost edge gateways, with their ever-increasing computing capacity, are designed to keep computing and data storage on the edge while hosting localized and task-specific actions to analyze edge data in near real-time. This means much less data needs to be transmitted back to the host server—where enterprise-level applications reside—saving on bandwidth requirements. On top of that, data transmission is not free, so reducing the amount of data transmitted across the network is a potential cost savings benefit overall.
  • Increases security Collecting and analyzing data at the edge increases security because information is kept within a local network. As price and form factors of processors keep decreasing, unnecessary computing and data storage can be moved away from the centralized server where enterprise-level applications reside. This enables companies to distribute their computing to the edge of the network through gateways and industrial PCs that can host localized and task-specific actions in near real-time and transmit much less required data back to the enterprise. This data can be transmitted with more modern protocols— such as MQTT, OPC UA, AMQP, and CoAP- which are specifically designed for secure and efficient network communications and can deploy encryption and security certificates to strengthen access controls and prevent man-in-the-middle attacks. Locating the edge gateway in the field and on-site and connecting it directly to the data sources helps alleviate many security concerns of communicating directly with the data sources in the field over a wide area network with an unsecure protocol.
  • More precise and predictive analytics By storing the data locally, models can be developed and patterns recognized by analytic applications running on the local gateway or industrial PC. Descriptive analytics can turn data into more meaningful information. The key performance indicators (KPI) used to determine how a machine is running might be just a few data points that are computed from several different sensor readings, which could be done locally with just the KPI information transmitted from the edge to the enterprise.
Even more advanced are predictive analytics on the edge, where machine learning techniques and applications can be applied to predict certain outcomes before they happen. This could be done locally at the edge with periodic updates on predicted outcomes, or through alerts if undesirable trends are predicted. The ability to make decisions locally and quickly based on collected and analyzed data can significantly improve an organization’s efficiency and safety. Instead of performing centralized data analytics to determine what caused downtime after it happens, the Energy Industry could see an undesired state or accident before it happens, and prevent it entirely with real-time predictive analysis.

Conclusion

Edge-based data collection and analytics are solving some of the challenges of implementing the industrial IoT in the Energy Industry . Collecting data locally and directly from a data source in the field can reduce security concerns while improving data reliability. Performing analytics ranging from data reduction and consolidation to machine learning and condition-based monitoring at the edge can reduce the required volume of data transmission across limited bandwidth networks and enable real-time analysis and decision-making for optimal operational excellence and safety. With vendors across the industry coming together to provide comprehensive solutions that make edge-based data collection and analysis possible, the Energy Industry will benefit from improved safety, efficiency, and productivity at every level across the enterprise.