Asset management’s new normal

Oct. 21, 2020
Modern prescriptive maintenance strategies rely on artificial intelligence and machine learning—as well as remote access and requisite cybersecurity measures

Asset management continues to evolve to make better use of the abundance of information available from today’s intelligent devices—from field sensors, actuators and drives to the interconnecting networking infrastructure including power supplies.

The current COVID-19 pandemic has also increased the willingness of organizations to expand their use of remote diagnostics and remote support for their operations, which typically involves either directly connecting to the system remotely, navigating through associated support tools together (i.e., shared desktop and/or mobile collaboration tools), or sending the information to a remote support team by some other means.

All these developments raise cybersecurity concerns. Many teams, especially within ISA and the FieldComm Group's FDI initiative, are spending significant effort to—at minimum—develop guidelines for protecting those devices for which adding cybersecurity would be prohibitive, and identifying how to incorporate native support for cybersecurity in next-generation field devices.

These developments also point to the growing importance of asset management overall and to the migration from traditional reactive and preventive maintenance practices to ones that accurately forecast device failure. Of course, for the many devices that do not impact facility operations or safety, “run to failure” may still be the most logical maintenance strategy. On the other hand, for high impact devices such as those on safety systems and many automated valves, intelligent asset management featuring prescriptive approaches to maintenance is the best choice.

Intelligent asset management takes advantage of all information available from a device to drive to the next level of operational excellence, which includes prescriptive maintenance strategies. Prescriptive maintenance incorporates all the features of a predictive maintenance program, plus machine learning (ML) and artificial intelligence (AI) algorithms that can prescribe mitigation solutions for optimal results.

The real-time collection and analysis of data necessary for prescriptive maintenance often has implications beyond maintenance. In many cases, it can offer suggestions on how to better run equipment to extend the time between maintenance cycles and the overall life of the machine.

This is only the latest logical step in the increasing use of AI and ML to convert all the data available in today’s control systems and equipment monitoring systems, as well as associated process models, to equip personnel with the information they need to run facilities more reliably, safely and at lower cost.

Connectedness yields cybersecurity risks

Implicit in being able to implement this next level of asset management and maintenance practices is integration of the information from these operational technology systems to Level 3 tools and software, such as for enterprise resource planning (ERP) and computerized maintenance management, as well as for commodity pricing, warehouse inventory (spare parts) and shutdown planning, to name only a few.

Again, these activities bring cybersecurity to the fore, along with the standards that describe how data can be securely transferred between such systems. The recently published IEC standard TR 63082-1:2020, “Intelligent Device Management–Part 1: Concepts and terminology,” provides guidance on how to set up a program consisting of practices, procedures and systems to access intelligent device data to feed into your next-generation asset management system, or to at least make better use of the information you already have in your intelligent devices.

If you're interested in participating in any of the development efforts described above, please let me know, and I'll put you in touch with the right individuals and organizations.

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