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Making maintenance smarter and more predictable

Maintenance has always been a topical matter when it comes to asset management. Traditional maintenance strategies continue to lack real insight into the actual condition of an asset. Developments are, however, being made through condition-based monitoring (CBM) and other data capturing tools to help mitigate this.  

A slightly controversial statement but backed by real-world data that shows that time and money are often spent on unneeded maintenance as opposed to mitigating possible critical equipment failure. 

IDC’s Field, Digital, and Consulting Services Survey underscores this sentiment, highlighting that equipment performance is increasingly important, especially to mission-critical facilities. The researchers, however, point out that many organisations overestimate their ability to accomplish this. 

The good news is that thanks to the ability to capture, consolidate and analyse asset performance data and CBM – combined with advanced analytics – plant and facility managers can gain unprecedented insight into critical asset behaviour. 

To this end, we’re seeing more organisations moving beyond preventative maintenance, which allows them to identify an event long before it materialises.  That said, the move towards predictive and CBM does entail a number of critical steps to realise an optimised asset management posture. 

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 Steps towards optimised asset management 

The first step is to perform a self-assessment of your organisation’s position on the current maintenance spectrum.  Most industrial organisations practice one, or a combination of, the following asset maintenance methodologies: 

  • Reactive – maintenance resources are only deployed after a failure occurs.  This strategy can be extremely costly and disruptive; problem solving is often urgent and downtime costs can escalate in no time; 
  • Preventive is based on pre-established calendar dates, regardless of whether the equipment really needs servicing. This strategy has been in use for decades and has proven to be effective, although costly; 
  • CBM monitors the actual condition of an asset and identifies the nature of maintenance required. It dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure. This approach saves on cost and improves uptime – a win win so to speak; and 
  • predictive strategy utilises advanced analytics to mitigate accelerated aging due to usage and challenging environmental conditions.  It optimises both productivity and overall equipment effectiveness (OEE).  Furthermore, it utilises analytics to predict abnormal asset behaviour and enables corrective action long before a problem materialises. 

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Putting it to the test 

With Industry 4.0 and subsequent use of sophisticated plant data equipment, organisations are now seeing increased value in data performance and ultimately its contribution to advanced maintenance strategies. 

Digital technologies leverage machine learning methodologies to continuously refine asset performance insight and operational performance, thereby improving plant productivity and profitability. 

BASF, the world’s largest chemical producer, uses a cloud-based version of CBM at one of its manufacturing sites which monitors electrical equipment to verify the health of a portfolio of 56 electrical distribution assets.  

In addition to identifying equipment pre-failure abnormalities, the CBM service also generates a risk assessment criticality matrix which helps BASF to determine which assets are at a greater health risk relative to how critical it is to the process at hand. 

 “By using a partner that has a deep understanding of your industry and operations, it’s possible to speed time to value and avoid potential pitfalls by leveraging the collective learning of others,” underscores the IDC survey. 

By Quintin McCutcheon (Digital Transformation Leader) & Patrick Kazadi (Marketing & Business Development Director Field Services) at Schneider Electric 

Guest Contributor
The views expressed in this article by the author are not necessarily those of the publishers and/or association partners. While every effort is made to ensure accuracy, the publisher and editors cannot be held responsible for any inaccurate information supplied and/or published.