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Advancements in Predictive Maintenance & the IIoT

The next generation of condition monitoring uses innovations in edge computing, analytics and machine learning for you to best leverage your asset’s data.

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Middle Aged Engineer Monitoring and Analyzing Conditions at an Automated Modern Electronics Factory with Mobility Robots Working with the Help of Artificial Intelligence Software

By Chris Barnes, Senior Manager, Data Science & IoT Consulting, Kalypso

: A Rockwell Automation Business

When it comes to connecting, collecting and analyzing data, we’ve seen macrotrends among many industries that include the democratization of machine learning (ML), increased accessibility of edge computing resources and the proliferation of connected devices or sensors.

Manufacturers no longer need an internal data science team. They also now have access to all kinds of information about their assets. However, many still don’t know how to make the most use of this data, including only using reactive maintenance operations instead of predictive.

According to a U.S. Department of Energy Operations and Maintenance report, instituting a proper preventive maintenance program can result in energy savings of as much as 12%.

Asset Performance Management & Predictive Maintenance

Asset Performance Management (APM) encompasses the capabilities of data capture, integration, visualization and analytics tied together for the explicit purpose of improving the reliability and availability of physical assets.

Predictive maintenance (PdM) is a component of an APM system that uses artificial intelligence (AI) and ML techniques to try and predict asset health issues before they arise.

This includes the components of:

  • Condition-Based Monitoring.
  • Operations Management.
  • Reliability-Centered Maintenance (RCM).

PdM uses condition-based data from various sources to deliver real-time insights on asset performance. This type of solution is designed to improve the reliability of assets and maintenance strategies using a combination of first-principles analysis and data science.

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Our Approach to Maintenance

To have effective maintenance operations, industrial firms need to understand the fundamentals. At the most basic level, these include planning, scheduling and execution. How well an organization can perform these three activities establishes the baseline for how impactful every other maintenance program, technology choice or initiative can be.

Now let’s dive a little deeper into each of the concepts of predictive maintenance we listed above.

Condition Monitoring

How do I get my assets to tell me when they’re “unhealthy?”

This includes device-level analytics that use advanced sensing, edge computing and ML. For example, it can report on the mechanical health of an asset driven by variable-frequency drives.

Consider predictive maintenance driven by condition monitoring. Although a trendy term, its roots are decades old. Condition monitoring solutions, such as the Allen‑Bradley® Dynamix system,

work with monitors and portable data collectors to help establish and execute a condition-based predictive maintenance program. It helps a maintenance team detect oncoming failure earlier and more accurately.

Eventually, the condition monitoring software or team generates a maintenance request, and it enters nearly the same planning, scheduling and execution workstream as every other maintenance request. If the maintenance team is excellent at planning, scheduling and executing other maintenance requests, they’ll likely be excellent at doing the same for a request created by condition monitoring.

A solution like FactoryTalk® Analytics™ Guardian AI

™ from Rockwell Automation is a differentiator in the market because it incorporates signals from existing sensors to apply prebuilt ML models to the data source and close gaps.

Condition monitoring can give that team a longer time horizon to get all those pieces in place before failure occurs.

Operations Management

How do I manage the action side of things and make sure I get the right insights at the right time?

Data born from devices, orchestrated at the edge, and liberated in the cloud make data available, meaningful, useful and valuable.

This component includes data contextual like visualization for real-time asset tracking. Think about supervisory analytics with APM, computerized maintenance management system (CMMS) or the Industrial Internet of Things (IIoT).

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It’s never been enough to just detect failure. Fundamentals in action include getting technicians and contractors on site with the right training, information, tools, materials and safety permits in the optimal production window.

This is where technology such as FactoryTalk DataMosaix

™ comes in. It’s custom-designed for industrial data applications and provides context around the multitude of condition monitoring solutions existing at the sensing and control level. It also has the ability to add context and take actions against the supervisory or operations management level.

Reliability-Centered Maintenance Consulting

Where should I focus my efforts?

RCM consulting includes the strategy, delivery and support services that help you focus on the right opportunities. You should be able to orient the solution around a manufacturer’s key goals.

This includes focusing on questions such as, “Where are the critical assets that are bottlenecks?” or “What issues are safety critical?”

In addition to energy savings, we here at Kalypso: A Rockwell Automation Business have seen organizations that implement a predictive maintenance solution improve:

  • Long-term value and scalability with a common manufacturing data fabric .
  • Asset availability by 10-30%.
  • Plant productivity by 10-15%.
  • Total cost of ownership from 8-12% up to 40%.
  • Worker safety, all but eliminating any catastrophic failure.

Valuable Opportunities for Predictive Maintenance

Results in terms of throughput or maintenance costs depend on the business processes you have in place, the specific assets you're running and the product you're making. We’ve worked with organizations across many industries, including consumer packaged goods

, tire, oil & gas
, aerospace
and mining
.

For example, process and heavy industries working with remote assets are probably the most mature in terms of maintenance operations, but not in terms of connected operations. There’s still room to deploy ML algorithms and make use of the sensor equipment available.

In pharmaceutical manufacturing, the ambient environment is important. Meanwhile, an HVAC or cooling tower going down can lead to scrapping, reworking or loss of valuable product.

Why Now?

Everyone has a maintenance department with varying levels of maturity. When it comes to using condition monitoring solutions, companies often struggle with competency, talent retention and training challenges.

With major advancements in edge computing and analytics, there’s an opportunity to embed a layer of innovation into your intelligent devices that work with ML.

It’s hardware, software and services all under one roof. It’s the next generation of condition monitoring capabilities.

Kalypso

: A Rockwell Automation business, is a professional services firm that provides consulting, digital innovation, technology, business process management and managed services across the innovation value chain.

 

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