In most factory facilities, there are far more assets not connected to the DCS than those connected to it, and many are inaccessible or difficult to access due to their distance from the access point.
Figure 1: Integrating data into the cloud creates an environment for cross-sectional analysis, enabling third-party consultants to perform high-precision analysis and provide recommendations for production optimization. Image source: Yokogawa Electric.
Monitoring these assets using traditional wired sensors connected to a DCS or other control system would be astronomically expensive. Therefore, the current situation is that these assets cannot be monitored, or are only minimally monitored by technicians for routine checks. However, increasingly stringent health, safety, and environmental (HSE) regulations are forcing plants to invest in better maintenance to improve safety, reliability, and profitability.
As inevitable demographic shifts bring about a larger, younger workforce, who sometimes lack situational awareness and the ability to troubleshoot asset failures, businesses are increasingly in need of implementing Industrial Internet of Things (IIoT) solutions to address these challenges. The proliferation of data and data-driven organizations are compressing decision-making timeframes and creating more digital competitors.
Safety is improved by reducing the number of on-site workers in hazardous locations; reliability is enhanced by applying predictive analytics to the big data generated from continuous factory monitoring; and profitability is increased by employing consulting services that can troubleshoot factory equipment failures and facilitate plant-wide improvements. These three anticipated benefits are the catalysts for most IIoT implementations.
Wireless monitoring and predictive analytics
Combining condition monitoring with predictive analytics can improve security, reliability, and profitability, thereby facilitating digital transformation and preventing major asset failures. Previously, operations personnel conducted on-site inspections with portable devices to monitor conditions and make on-site decisions, or they relied on installing extremely expensive condition monitoring systems.
The former method produces inaccurate data that is often unanalyzable. The latter method is very expensive and only monitors the most critical assets. In a typical factory, a combination of these two situations often occurs. Inaccurate data is essentially useless, and a condition monitoring system that only monitors the most critical assets may miss failures of less critical assets (those that only become critical after a failure has occurred).
Replacing on-site inspections with more cost-effective wireless sensors is a disruptive technology that will change best practices and has proven highly efficient. It also improves safety by reducing the time workers spend in potentially hazardous areas.
Wireless monitoring can free up workers to engage in other, more value-added activities. The sheer number of sensors that can be installed enables ubiquitous, large-scale monitoring throughout a factory or facility. Data collected by these wireless sensors allows for online condition monitoring and diagnostics of a wide range of assets, and predictive analytics can be performed when this data is transformed into reliable, actionable insights.
The best way to handle this type of data is to use the cloud. Cloud computing is known to use remote servers hosted on a network, rather than local servers or personal computers, to store, manage, and process data. Because factory data is stored in cyberspace, it can be queried from anywhere (see Figure 1). The cloud combines accessibility and convenience with enhanced factory security.
Furthermore, anyone can view data using certified smart devices via the cloud, and experts can remotely monitor and analyze it to improve performance. Because the data resides in the cloud, data management can be achieved through a simple, one-stop solution.
Cloud-based Data as a Service
The sensor suite involved includes sensors used to monitor devices for vibration, temperature, and pressure. These sensors and the provided cloud services constitute a Data-as-a-Service (DaaS) product.
DaaS is attractive to operators of oil and gas facilities who do not want to manage and operate large-scale data collection, transformation, and sharing solutions. All of these operations require granting access to their internal OT and IT networks.
DaaS addresses these issues by providing dedicated access points and access from trusted companies behind the carrier's firewall. Therefore, the required multi-layered management and support for connectivity and visibility are handled by third parties, decoupling these services from the carrier's core business activities.
For DaaS with visualization capabilities, operators receive publicly available data from existing automation and asset management systems. This allows engineers, managers, and others to work using tools via a browser. Of course, this work can be done from anywhere, on any device with a browser installed, such as a PC, smartphone, or tablet.
Therefore, DaaS is an enabler for operators' digitalization activities. Digitalization drives good data practices, helps simplify the implementation of DaaS services, and provides operators with high-quality services to facilitate their digital transformation activities.
Digitize data
Having workers discover potential faults by conducting routine inspections of equipment and assets is not an efficient method. If these operators didn't have to fill out inspection reports and post them on bulletin boards, they could do something with higher value. With manual inspections, it's easy to accidentally miss obvious signs of abnormality, so faults still frequently occur despite inspections.
For example, a factory outsources vibration measurements for 200 projects, collecting data monthly. The annual cost is approximately $48,000, but because the data is not digitized, the customer cannot use it for predictive maintenance, and breakdowns still occur frequently.
Yokogawa Electric deploys dozens of sensor devices throughout its customers' factories, each transmitting data to the cloud. Cloud-based data management tools provide visualization and trend monitoring to indicate anomalies in the early stages of malfunctions.
Consultants provide information to factory personnel so they can take action. The factory receives real-time equipment status reports. When a failure is predicted, these reports automatically alert factory technicians. Because the data is already digitized, this method helps the factory achieve digital transformation.
Another factory installed wireless sensors on its pumps and monitored pump acceleration for six months, detecting numerous anomalies (see Figure 2). The most common cause of these potential failures was broken balls in the bearing assembly. Early detection of these problems allows for predictive maintenance of the pumps, keeping them operational and reducing costs associated with unexpected downtime.
Figure 2: The sensor system monitors the pump's acceleration and detects abnormal signs before a failure occurs.
Simple wireless sensors are easy to install and relocate, and also easy to connect to the cloud. Cloud data provides consultants, maintenance managers, operations personnel, and other plant staff with insights to identify problems. For many oil and gas plants and facilities, this is the fastest way to begin IIoT implementation.
Sending data from sensors to the cloud provides ubiquitous, real-time field information. Analyzing and acting on this data can prevent failures and downtime. Integrating data from utilities not linked to a DCS can optimize production. Data can serve as input for digital twins simulating factory operations in the cloud, empowering factory personnel and consultants to adjust factory performance. Using authorized smart devices, experts anywhere in the world can analyze cloud-based data.
Wireless sensing technology and cloud-based data management are a smart choice, paving the way for factory digitalization. Digitalization can improve performance and optimize the factory.
