Paving the way for factory digitization: cloud-based wireless sensing technology
Figure 1 Integrating data into the cloud can create an environment for cross-sectional analysis, enabling third-party consultants to perform high-precision analysis and provide recommendations to optimize production.Image source for this article: Yokogawa Electric
“Through extensive asset monitoring, cloud-based wireless sensing technology can help businesses achieve increased security, reliability and profitability.”
Many assets in oil and gas industry plants and facilities are often not directly or indirectly connected to distributed control systems (DCS) or other types of industrial control systems. While this reduces the number of assets that require DCS input and output (I/O), it doesn’t mean they don’t need monitoring. Many assets require regular data monitoring as part of improved maintenance efforts rather than real-time control. Examples include motors, pressure relief valves, safety sprinklers and steam traps.
In most factory facilities, many more assets are not connected to the DCS than are connected to the DCS, and many are inaccessible or difficult to access due to the distance from the access point. Using traditional wired sensors to connect these assets to a DCS or other control system for monitoring would be astronomical. Therefore, the status quo is that these assets cannot be monitored or minimally monitored by technicians to conduct routine inspections on them. However, increasingly stringent health, safety and environmental regulations (HSE) requirements are forcing factories to invest in better maintenance to improve safety, reliability and profitability.
As inevitable demographic changes bring more younger workers, who sometimes have less situational awareness and ability to troubleshoot these assets, there is an increasing urgency for businesses to implement Industrial Internet of Things (IIoT) solutions to deal with these issues. The proliferation of data and data-driven organizations has compressed decision-making time frames and brought more digital competitors.
Improve safety by reducing the number of field personnel working in hazardous locations; improve reliability by applying predictive analytics to big data generated by continuous plant monitoring; and employ the consulting needed to troubleshoot plant equipment and improve plant-wide improvements services to improve profitability. These 3 expected benefits are the catalysts for most IIoT implementations.
Wireless Monitoring and Predictive Analytics
Condition monitoring combined with predictive analytics can improve safety, reliability, and profitability to help enable digital transformation and prevent major asset failures. In the past, operators carried portable equipment to patrol the site, carried out condition monitoring and on-site decision-making, or completed monitoring by installing extremely expensive condition monitoring systems.
The former method produces inaccurate data that often cannot be analyzed. The latter method is very expensive and only the most critical assets are monitored. In a typical plant, a combination of these two conditions usually occurs. Inaccurate data is effectively useless, and a condition monitoring system that monitors only the most critical assets can miss less critical assets (those that only become critical after the failure occurs) failures.
Replacing on-site patrol monitoring with more cost-effective wireless sensors, this disruptive technology will change best practices and prove to be highly efficient. Safety is also improved as workers spend less time entering potentially hazardous areas.
Wireless monitoring can free up workers for other, more value-added activities. Due to the large number of sensors that can be installed, ubiquitous mass monitoring is possible throughout a factory or facility. Leveraging the data collected by these wireless sensors enables online condition monitoring diagnostics for a large number of assets, which when transformed into trusted, actionable insights, enables predictive analytics.
The best way to handle this kind of data is to use the cloud. As we all know, cloud computing uses remote servers hosted on a network rather than local servers or personal computers to store, manage and process data. Because plant data is stored in cyberspace, it can be queried from anywhere. The cloud combines accessibility and convenience with enhanced factory security.
In addition, through the cloud, anyone can use certified smart devices to view the data, and experts can also conduct remote monitoring and analysis to improve performance. Since the data resides in the cloud, data management is possible through a simple one-stop solution.
It’s not an efficient way to have workers go through equipment and assets to spot potential failures. If these operators don’t have to fill out inspection reports and post them on bulletin boards, they can do more value-added work. With manual inspections, it is easy to accidentally overlook obvious signs of anomalies, so failures often occur despite inspections.
For example, a factory outsourced vibration measurements for 200 items and collected data on a monthly basis. The annual cost is about $48,000, but failures still occur frequently because the data is not digitized, so customers cannot use the data for predictive maintenance.
Yokogawa deploys dozens of sensor devices throughout the factory for customers, and each sensor device transmits data to the cloud. Cloud-based data management tools provide visualization and trend monitoring to indicate unusual signs of incipient failures. Consultants provide information to factory personnel so they can take action. Factory gets real-time equipment status reports. They provide automatic alerts to plant technicians when failures are predicted. Since the data is already digitized, this method can help factories achieve digital transformation.
Another factory installed wireless sensing equipment on the pump and monitored pump acceleration for 6 months and found many signs of anomalies (see Figure 2). The most common cause of these potential failures is broken balls in the bearing assembly. Identifying these issues early allows predictive maintenance of pumps to keep them running and reduce costs due to unplanned downtime.
Figure 2 The sensor system monitors the acceleration of the pump and detects signs of anomalies before failure occurs.
Simple wireless sensors are easy to install, relocate, and connect to the cloud. Cloud data brings problem-finding insight to consultants, maintenance managers, operators and other plant personnel. For many oil and gas plants and facilities, this is the fastest way to start an IIoT implementation.
Failures and downtime can be prevented by analyzing and taking action on data sent from sensors to the cloud, providing ubiquitous, real-time field information. Integrating common equipment data not associated with DCS optimizes production. Data can be used as input to a digital twin that simulates factory operations in the cloud, making it a great help for factory personnel and consultants to adjust factory performance. Using an authorized smart device, experts anywhere in the world can analyze cloud-based data.
Wireless sensing technology with cloud-based data management is a smart choice, opening the way for the digitization of factories. Digitization can improve performance and optimize factories.