Digital transformation is a top-of-mind topic for executives across the process industries, and this has been the case for quite some time. Emerson sees digital transformation as a strategy for improving performance using digital technologies, representing a tangible way for end users to attain top-quartile performance in their respective industries.
But while digital transformation is enabled by various technologies, business improvement only occurs when these technologies enable the workforce to create more value. Digital technologies have existed for decades and have primarily been used to help manufacturers automate and optimize their core production processes. But digital technologies can also be extended to enable the same sort of improvements in other operational areas like reliability, safety, energy and emissions.
This article explores two leading process industry examples to show how extended digital transformation works in practice, through valve diagnostics and connected services using augmented reality (AR), and with pressure relief valve (PRV) monitoring—both enhanced through the use of Industrial Internet of things (IIoT) technologies.
Predict and solve valve problems
Valves can be monitored remotely to predict problems before they occur, allowing issues to be addressed proactively. This method of monitoring and service is a vast improvement compared with a run-to-failure approach because it increases uptime, reduces maintenance costs and improves safety.
Valve diagnostics begin with data collection enabled by digital valve controllers, which provide extensive information for use by asset management, distributed control and other host systems. These controllers communicate using the HART protocol, WirelessHART with the addition of an adapter module, or a digital fieldbus. Once data is communicated to host systems, it must be analyzed to provide actionable insights.
Offline diagnostic testing can be used to characterize nominal performance, creating a valve signature with high-resolution samples of actuator pressure and travel. Offline diagnostics use a microprocessor to control the valve, ramping the input signal up and down at a slow enough rate to allow repeatable testing without excess speed influencing test results.
Once the baseline signature has been created, information collected from the valve while in operation is compared to established thresholds to indicate when valve performance is being compromised, for example, a temperature exceeding the limits of electronics or elastomers, or pressure outside of recommended range. Key performance indicators, such as travel deviation, can be analyzed to ascertain if an abnormal condition exists or if high friction is being caused by an overtightened packing.
Condensing the data into packets at the valve allows high-resolution information to be sent to host systems for trending and analysis. Subject matter experts (SMEs) can review these and other data to identify problems such as stick-slip (excessive friction), stem damage and seat damage.
SMEs can use data interpretation tools powered by anomaly detection algorithms. Further, SME knowledge and expertise can be used to determine if a problem has occurred or is predicted, and direct corrective actions needed to remedy the issue. Algorithms are continually updated as new leading indicators are identified through analyst findings, and via artificial intelligence and machine learning algorithms.
Remote assistance services empower local technicians by giving them mobile device connectivity to securely share their field of view through AR software, enabling remote experts to help them troubleshoot and solve valve problems. The specific valve installation is automatically identified, along with its maintenance history and repair instructions. Step-by-step instructions are overlaid in the field-user’s application to support installation, calibration or repair actions (Figure 1).
Real-time video communication enables users to resolve issues faster and minimize instruction errors that often occur with audio-only support, while eliminating travel time and the cost of getting technicians to the work site. In addition, operators can expand their in-house knowledge base and staff skillsets through on-the-job troubleshooting guidance and recommendations to remediate issues, up to and including oversight of final repairs.
PRV monitoring
PRVs only activate in an emergency but must perform when needed. They're mechanically self-operating, without the need for any electronic components or external support to function, so they usually have no built-in mechanism capable of reporting their condition or activity. If operators want to know what's happening with a particular PRV, they typically rely on local inspection, or monitor process pressure measurements for indication of operation near the PRV’s setpoint. Neither method is ideal, driving the need for continuous monitoring.
Acoustic monitoring devices equipped with wired or WirelessHART transmitters can be mounted directly to pipes adjacent to PRVs. Wireless devices are particularly effective in this application because many PRVs are mounted in areas not readily accessible, making installation of wired infrastructure difficult and expensive.
These devices sense vibrations in the discharge pipe due to turbulences generated by fluid flowing through the valve and transmitted directly through the pipe wall. A fully closed valve produces no vibration due to turbulence because nothing is flowing through it. When system pressure exceeds the setpoint, the valve opens and releases liquid, gas or both. This creates turbulence, generating mechanical vibrations that an acoustic monitor can detect and report to host systems, either locally via wired or wireless networks, or remotely via IIoT.
If the process recovers and system pressure returns to normal, or if operators reduce pressure sufficiently, the PRV should close again automatically. If everything is working correctly, it will seal, and the mechanical vibration will cease. Data from the acoustic transmitter can verify the action, reporting the time the discharge began and ended, while giving some approximate indication of how serious the discharge was based on the amplitude of the sound.
But sometimes things go wrong and a small particle of debris from the process can lodge on the valve seat, causing leakage. Like a full overpressure-driven release, small leaks also generate turbulence inside the discharge pipe, causing mechanical vibration detectable by the acoustic transmitter.
The importance of detecting leakage as soon as it starts is driven by a compounding effect over time. A mere 0.1% leak, if left unaddressed for a year, equals a full release from a PRV for six hours. The effect is multiplied into a huge problem when considering the total population of PRVs in a process unit or overall plant. A study from 10,000 PRV service records presented an astonishing result, indicating 20% of installed PRVs leaked below 50% of set pressure, meaning many of these valves were constantly leaking. Even worse, 8% of valves surveyed leaked so excessively they didn't operate correctly when tested.
Digital technologies have clearly led to improved monitoring and control for operational facilities over the years. These same technologies also generate great quantities of raw data and enable remote connectivity. Both of these characteristics are fundamental to digital transformation efforts extending beyond basic automation to provide enhanced reliability and safety, while reducing energy use and emissions.
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