Asset Management Automation

Attain Next-Level Asset Management

EP Editorial Staff | March 1, 2022

Consider these factors when you want to advance an established and effective digitized plant system.

By Jason Urso, Honeywell Process Solutions

While IIoT and cloud computing are considered new capabilities to support asset management for some operations, within processing and refining plants there is a mature and successful installed base of legacy digital industrial technology. That technology has served critical machine assets and reliability-centered maintenance/condition-based maintenance (RCM/CBM) strategies for years.

Technology in today’s processing and refining facilities has evolved tremendously in the past 30 years. Even two decades ago technology was patently remarkable. As the onset of the 21st century neared, fast microprocessor-based control and automation systems were relatively ubiquitous. Data historians had deep capacity and networked seamlessly across a plant. PCs and data servers were being commoditized and included well-evolved, configurable productivity and condition-monitoring software. Furthermore, on machinery upstream of this remarkable data engine was a sea of precision sensors. This only scratched the surface of machinery control, automation, and monitoring technology available to plant operators and reliability professionals in years prior.

Indeed, the continued evolution of industrial APM systems has advanced present-day systems significantly and, combined with RCM/CBM processes, the value provided from installed technology has risen even more. Therefore, today’s plant directors, managers, and reliability and maintenance personnel might ask: What does digital transformation look like for a modern hydrocarbon-processing plant or refinery that already appears to be digitally mature? Also, if greater maturity is obtainable, how does a plant get there?

Before we answer those questions, consider the four drivers of the current technological push:

New technology: Cloud computing and machine learning analytics are now quite mature following a decade of intense development, application, and demonstrable results. Akin to how the millennial internet boom and cellular advances previously augmented APM (asset performance management) systems, today’s big-data analytics can perform the same functions. This, among other advances, provides the foundation for a step change in predictive asset management. By aggregating all plant data sources into a single cloud service, today’s technology transforms asset monitoring from a collection of discrete and disjointed data-collection systems, into an integrated asset-performance and data-analysis engine that truly provides the actionable information long-promised—yet scarcely delivered—by previous solutions.

The Great Resignation: While not a new concern, what felt like an idle threat two decades ago is now a reality. The Great Resignation has arrived. In the current landscape, the availability of human talent and average employee tenure is declining. Meanwhile, the depth of expertise and the spectrum of skillsets required for downstream processing facilities is increasing. As decades of experience retires, and an inadequate supply of asset SMEs backfill their vacancies, today’s APM digital technologies are demonstrably adept at augmenting human analytical capabilities.

While not intended to replace humans, the analytic and performance models provided by cloud computing deliver vetted alerts to help SMEs pinpoint root causes sooner and more accurately. Likewise, the cloud offers a centralized view of an asset enterprise, easier configurability, and productivity tools that increase the volume of manageable assets by a single individual.

Maximizing digital assets: Clearly, the objective of any asset-management system is to maximize return on all assets. This includes the electronic systems controlling and monitoring traditional rotating assets. Within these electronic systems there are likely immeasurable terabytes of data that were collected yet never analyzed, configuration methods that are cumbersome and differ widely, and user training requirements that are very difficult or impossible to manage effectively. Additional value is derived by connecting these systems to a common cloud-user interface and, through use of the cloud data lake, to analyze data in its entirety, maximizing installed rotating and digital assets.

Considering those three factors, describing what digital transformation looks like in a plant with an established high level of digital maturity involves creating a system out of the existing systems by aggregating all data sources and applying modern analysis on the unified dataset. Using cloud computing, analysis is performed with first-principles performance models, machine-learning analytics, big-data mining, and statistical analysis. The output is focused results delivered to personnel across the reliability and maintenance value chain.

The Great Resignation should be a primary factor in driving you to keep your digital systems on the leading edge.

The Path Forward

Recognizing that greater maturity is obtainable, the real challenge is seeing the path forward. There are several aspects to consider in realizing greater effectiveness of an existing APM system and augmenting the digital systems underlying it. This includes an in-depth strategy developed between operations, maintenance, and IT departments. They must consider the strengths and opportunities of the current APM system and asset-management processes, as well as linking APM with CMMS and ERP systems. Such depth regarding strategy is better discussed separately, but keeping with the theme of leveraging existing digital assets to greater levels of functionality, that will hinge heavily on which platform is selected to supply the cloud-based APM system.

The following additional key factors should also be considered when selecting a platform:

Time to value: Platform selection should give considerable attention to the platform’s ability to address integration complexity and aggregate data in an efficient, time-effective manner. Vendors who can demonstrate their techniques and prove expeditious aggregation capability will ensure a smooth transformation to the cloud.

A plant may easily have tens of thousands of points and control tags that must be mapped to the cloud digital twin. It is not a trivial task and can be the most time-consuming element of a transformation. Whether contracting the platform vendor to perform aggregation and tag mapping, or choosing to keep it in-house, it is critical to ensure that aggregation activities do not overly consume resources or jeopardize the targeted timeframe. The platform’s effectiveness in data aggregation will dictate this.

Likewise, model building is time consuming and template libraries, cloud connectors, and context-aware aggregators available from a platform will improve the initial engineering productivity and aid in model sustainability and evolution. The maturity and depth of libraries and connectors will usually dictate a platform’s ability to address a wide variety of digital platforms, software, and connected machines within the plant.

Likewise, the depth of libraries and connectors will likely be in proportion to the platform vendor’s experience within the refining and processing industry, as many models are evolutionary from past implementation of automation, control, and condition monitoring systems, as well as previous modeling and system simulation.

Analytics: Platform vendors may incorporate their own analytic capabilities and machine-learning engines, have integrated third-party solutions, or some combination. Analytics should address three areas:

• Descriptive analytics or, “What has happened?” through insight derived from aggregating multiple plant data sources and mining the data to provide insight into past performance.

• Predictive analytics or, “What could happen?” through statistical models and forecast techniques to gauge the future.

• Prescriptive analytics or, “What should we do?” through optimization and simulation algorithms to advise on possible outcomes, given theoretical inputs.

Performance modeling: While cloud analytics provide a newer means for deriving greater asset insight through statistical analysis, the traditional first-principles performance models remain a core element of a comprehensive APM system. First principles, i.e., formulas and equations derived from the laws of physics, have and will continue to be successful in machinery performance assessment. However, such modeling techniques require extensive experience and skills with appropriate calibration techniques. 

A vendor’s experience with process-industry assets such as compressors, turbines, and pumps, and associated processes, is imperative for successfully calculating machine performance efficiency, its energy usage relative to optimal, and the timeframe to next maintenance or overhaul.

Cybersecurity: Industrial machine network hacks are growing in frequency. With the increased connectivity, network breadth, and direct connection to machine-control systems, a cloud APM solution is allowed zero cybersecurity weakness and must demonstrate an imperviousness to a breadth of cyberattack methods from global sources.

Conventional security fails to protect proliferating cyber threats to OT and IT systems. ICS on OT networks have different operational requirements that affect the ability to adapt and respond to new cyberthreats and reveal new avenues for cyberattack. The APM digital platform must be specifically designed with asset and operational requirements in mind, while protecting critical processes without.

It’s clear that new and value-adding functionality is here to stay, regardless of the level of digitization and RCM/CBM existing in a plant. Indeed, the current stage of APM evolution is following its traditional, remarkable path. However, currently it happens to be intersecting with the evolution of cloud computing. This is significant. To harness the benefits, a plant should choose its digital-transformation partner carefully. They should be experienced in the digital systems, refining processes, and rotating equipment. In addition, the chosen platform must leverage the best of cloud-based analytics, productivity, and connectivity capabilities, while offering implementation in an efficient and minimally invasive manner. EP

Jason Urso is Vice President and Chief Technology Officer of Honeywell Process Solutions (HPS), Houston (, a leading provider of industrial automation and digitization technology and software.


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