Maintenance and Reliability Engineers Gain Control of their Destiny with IIoT

With IIoT, maintenance and reliability engineers can gain control of their destiny.  Let me explain.

Control System Access by Maintenance and Reliability Engineering

Equipment, automation, and the control systems typically execute specific manufacturing steps that are critical to producing products, and obtaining the associated revenue to fund the business.  When this equipment doesn’t function properly, it gets a lot of attention fast – which can include executive management.  As a result, the control engineers responsible for this equipment are usually very protective and limit access by others to make changes (as the author confesses doing when he was a control engineer). 

Past Difficulties with Predictive Maintenance Projects

However, reliability and maintenance engineering groups need access to the real-time data for analysis and alerts when the equipment health deteriorates.  The historian has been a type of gateway in that it provides others with access to data without affecting the control system.  Predictive maintenance applications typically use the historian’s process data to assess the equipment health.  The data would be imported into a custom application that performed engineered analytics, i.e., first-order mathematical formulas and/or decision trees.  This was problematic for a few reasons:

  • Expensive custom engineering: Understanding the process well enough to develop the algorithms required deep engineering, math and programming skills – which are expensive and rare in most plants.
  • Costly to add I/O points: If the application required additional I/O, this was often impractical to accommodate.  Changes to the control system require rigor across many dimensions; industrial-strength devices, hard wiring, fault tolerance, testing, and more. 
  • Brittle: If any of the systems around the application changed due to software upgrades, new tags, etc., the application often breaks.  Since the original project team likely moved on to other activities, the resources would not be available to fix it quickly.
  • Inflexible: Deployment of a predictive maintenance application soon brings new knowledge, learnings, and an opportunity to continuously improve the application.  But, the project deployed a unique set of technologies requiring another engineering project for improvements. 
  • User Adoption: The initial too many false positives cause users tend to ignore the alerts which contributes to project sustainability issues.

Because of these problems, predictive maintenance implementations were difficult to sustain, and had a high decay rate.  Per end-user surveys conducted by ARC, they were found to be few and far between, even for critical assets.

IIoTIIoT for Equipment
Maintenance and Reliability

IIoT Improves Sustainability and Cost

Today’s IIoT platforms help take much of the drudgery out of developing a predictive maintenance application.  They provide the infrastructure so the user can focus on the application.  These systems typically function as a platform as a service (PaaS), a category of cloud computing services for users to develop, run, and manage applications without the complexity of building and maintaining the associated IT infrastructure such as database, programing tools, services like analytics, and more.  Infrastructure as a service (IaaS) focuses more on the datacenter with virtual machines.  PaaS and IaaS are two of the three main categories of cloud computing services alongside software as a service (SaaS).

Benefits of Using an IIoT Platform

The historian gets its data from the process control system which means it has only process data (like the temperature and pressures around a pump).  Adding equipment data (like the electric current draw of the pump’s motor) greatly improves the assessment of the equipment’s health and future maintenance needs.  Using an IIoT platform for predictive maintenance provides several benefits that can help mitigate many issues of the past:
  • Lower cost of entry: Suppliers of cloud computing, including PaaS, utilize huge data centers that provide economies of scale.  The business model means that the needed resources can be scaled up and down as required by the application.  In most cases, the user can create a proof of concept within the limits allowed for free resources.  Periodic charges increase (along with the benefits) as the application scales up.
  • Lower cost of I/O and tags: Most start by importing data from the historian into the IIoT platform.  As the application grows, new I/O tags will likely be needed.  These additions can be made external to the control system using products suited for monitoring, rather than control (wireless networks, commercial I/O, etc.).  Going directly into the platform also avoids the licensing costs for tags in the control system and historian.
  • Development flexibility: Being separate and external to the control system alleviates the need for rigorous development and testing prior to deployment.  Developers can take a more rapid development approach with testing of alternatives.
  • Robust: The PaaS avoids the need to understand a complex IT infrastructure so that the application becomes easier to support.  When something breaks, debugging and isolating problems is more focused and can be resolved with fewer technical resources.

Who Owns the IIoT Program?

The most common application of IIoT in industry has been for predictive maintenance.  With IIoT, maintenance and reliability engineers can gain control of their KPIs by implementing predictive maintenance programs, and continuously improving them for more accurate alerts and fewer false positives.  Maintenance and reliability engineering departments should take ownership of IIoT to fulfill their needs for asset health assessment.

To learn what your peer organizations are doing in this area and participate in the discussion about Maintenance and Reliability Engineering; and other IIoT-enabled solutions for industry, infrastructure, and smart cities, ARC invites you to join us at our upcoming ARC Industry Forum in Orlando, Florida, Feb. 11-15, 2018.

“Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

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 About the Author:

Ralph Rio

Vice President Enterprise Software

Ralph’s focus areas include asset performance management (APM), enterprise asset management (EAM), field service management (FSM), and global service providers (GSP).

Ralph’s focus involves Asset Performance Management including Enterprise Asset Management (EAM), Field Service Management (FSM) and the impact of Industrial IoT (IIoT).  He also researches and advises Global Service Providers.  Ralph is an advocate of continuous improvement programs (Lean Manufacturing & Six Sigma).  He has written well over a hundred reports. 

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