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Overview
This past May, ARC Advisory Group hosted a meeting of the European Digital Transformation Council (DTC) in conjunction with the ARC Industry Forum in Barcelona. During the meeting, Peter Kwaspen from Shell gave a presentation on the company’s unique digitalization approach to asset management for its large installed base of instrumentation.
While these types of presentations typically remain confidential within the DTC (membership is restricted to qualified end users), Shell decided to make an exception for this story, since the company believes it holds valuable lessons for all industry stakeholders.
Asset Management Goals
To remain competitive, industrial organizations need to improve asset availability, ideally, while also reducing their maintenance costs. However, well-established companies like Shell typically have a large installed base of aging assets, which typically require more maintenance than newer ones to maintain operational performance and safety.
In a previous project at Shell, Mr. Kwaspen had developed a predictive maintenance approach for a large installed base of conventional (non-smart) instrumentation in process plants without using add-on IoT 
sensors. In this project, Shell had provided proof of concept that process information from historians could be used to train neural network models to determine most failure modes of conventional (non-smart) control valves, providing adequate lead times to allow the company to act before a failure occurs.
Following the development phase, an initial rollout showed that about 40 percent of the conventional control valves can be modeled satisfactorily and about eight out of ten failure modes (all mechanical root cause failures) can be detected with lead times varying between four and 120 days. However, the demands in terms of bandwidth, sizes of data sets, and computing power exceeded the capacity of the existing systems.
A dedicated cloud architecture was required to implement the solution. With the potential to reduce failures of a very large number of conventional control valves at Shell, top management decided to productize the solution to enable global rollout. In the follow-on project, the aim was to exploit another category of unused data to improve maintenance performance: the data available in smart instruments and actuators.
Shell’s Asset Management of Instrumentation
Shell’s overall asset management framework encompasses multiple processes for managing equipment, executing maintenance, and ensuring safe production. The overarching goal is to ensure that activities are performed consistently in a coordinated manner and improved systematically to deliver excellent and sustainable business outcomes.
One of the goals of the Manage Equipment Care process within Shell’s overall AMS (see chart on page 5) is to have a complete and up-to-date information repository to support risk-based decisions relative to maintenance planning and execution. This process also includes improving maintenance efficiency and effectiveness and the quality of the maintenance activities by continuous analysis of equipment performance history.
The company’s Perform Maintenance Execution AMS process, in turn, includes:
- Preparation for just-in-time maintenance
- Timely detection of asset degradation to avoid emergency and rush jobs
- Activity prioritization and administration
- Updating master data
- Access to maintenance history for learning and improvement, and
- Support for advanced maintenance solutions, such as predictive maintenance
Next, the Ensure Safe Production process requires an up-to-date overview of safety-critical and production-critical instruments and their health statuses. The goal here is to improve situational awareness and provide automated alerts related to abnormal situations. The process includes proactive technical monitoring and improving the management of cumulative risk from degradation of performance of multiple equipment entities.
Preventing Instrument Failure-related Downtime
A breakdown of maintenance tasks at Shell, according to Mr. Kwaspen, was very similar to the responses ARC received several years ago in a survey of 141 enterprise asset management (EAM) professionals:
- 10 percent of maintenance tasks are condition-based or predictive
- 30 percent are reactive upon failure or emergency
- 37 percent are proactive inspections and preventive maintenance
- 23 percent relate to project management, administration, and safety
Reactive maintenance typically results in at least some production downtime or degraded performance, resulting in unnecessary damage and opportunity cost. And since ARC estimates that more than 80 percent of equipment failures are random, it is likely that many preventive maintenance-based jobs are executed prematurely, also creating unnecessary cost. However, condition- or predictive-based maintenance (ideally performed just prior to the failure), could eliminate both unnecessary damage related to reactive maintenance and unnecessary maintenance costs related to preventive maintenance to help ensure non-stop production and uncompromised performance. This makes the Shell case study particularly relevant for the process industry.
Supporting AMS With Digital Information from Smart Assets
Mr. Kwaspen described a next possible step for improving the effectiveness of Shell’s AMS that, ultimately, could further improve reliability and uptime. While data from smart instruments have been used for decades to support process automation system engineering and configuration, even today these data are rarely used in the operate-and-maintain phase. A survey that ARC performed jointly with NAMUR found similar practices in other process industry companies.
Mr. Kwaspen believes that consistent use of performance and health data from smart instruments could improve the performance of the AMS by helping populate the asset registry. This could help the company detect mismatches between instrument and DCS and/or SIS configurations and enable the instrument history to be recorded. He anticipates that up-to-date and accurate instrument information will also improve the accuracy of maintenance instructions and other support materials and, on a larger scale, improve turnaround planning. Timely detection of asset performance degradation will reduce emergency and urgent interventions. An accurate instrument history will improve the results of analyses by maintenance experts. Improved asset information and availability of safety- and process- critical instruments will improve situational awareness. It would also enable instrument health alerts to the asset team to be automated and facilitate incident analysis to help manage cumulative risk.
Mr. Kwaspen is confident the instrumentation information will be able to be used for next-generation predictive maintenance solutions based on new data analytics and machine learning tools available in industrial cloud platforms. In addition to improving asset reliability and uptime, this should help reduce maintenance cost by enabling remote access to information for analysis and remote intervention for certain types of anomalies. It should also reduce the risk of HSE incidents that, potentially, could be harmful to personnel and/or the environment.
Linking Business Processes to a Technical Implementation
Mr. Kwaspen sees the company’s instrument asset management system (IAMS) as a combination of infrastructure, applications, services, and adaptations of business processes. He envisions connecting smart instruments and actuators to a dedicated IAMS application.
The plan calls for a phased approach for technical installation to help manage the impact on the current business processes. In a first phase, the basic functionality of remote and smart configuration and commissioning should be applied consistently (if not already the case). Shell plans to do this using leading commercial IAMS solutions.
In a second stage, and for a second level of usage, IAMS will be used to support project engineering and turnaround activities to improve plant integrity. This will use an advanced maintenance support application that will combine an instrument configuration repository, asset performance monitoring and trending capability, and alarming and reporting. This builds on layer 1 and may also use data from the DCS and safety systems.
In a third phase, the IAMS will be used to support maintenance processes. This should improve maintenance execution and simplify maintenance management. More advanced functions of the maintenance support application will be used to combine process and asset information from layers 1 and 2 to enhance decision support for maintenance and reliability. This stage could impact maintenance processes, competences, skills, and habits.
In the final stage, instrument asset information will be processed automatically and integrated seamlessly into the AMS processes to enhance maintenance- and operations-related decisions. The application will be integrated with the CMMS, scheduling, and other relevant enterprise applications.
Upcoming challenges will include harmonizing the many different versions and types of device descriptions required for integration. For example, text-based device descriptions (DDs) currently coexist with electronic device descriptions (EDDs) and the Windows-based, device type managers (DTMs). These device descriptions must be managed rigorously. This will be time consuming, since while the organizations that manage the EDD and DTM standards (the FieldComm Group and the FDT Group, respectively) continue to update the technologies, they have yet to combine the two into a single FDI standard. ARC believes that broad adoption of FDI would create major savings for technology suppliers and end users alike.
The NAMUR NE 107 standard, broadly adopted by device suppliers, provides standardized diagnostic status information for smart devices. This provides plant operators and maintenance personnel with a quick overview of their priorities to help manage their time more efficiently. Mr. Kwaspen believes that just integrating the standard diagnostics standards into IAMS and AMS could bring benefits. If more diagnostic information could be made accessible, this would help the company realize the maintenance and safety benefits mentioned above.
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Keywords: Digital Transformation, Smart Devices, Asset Management System, Instrumentation, Actuators, Valves, Reliability, Safety, ARC Advisory Group.