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by | Aug 11, 2020

Moving to Predictive Maintenance Today – For a Better Tomorrow

Maintenance Strategy – Uptime and Reliability Focused

For all asset intensive companies, regardless of industry vertical, maintenance strategy is key to achieving uptime and reliability targets.  While there are a number of methods of measuring the maturity of your Asset Management strategy, probably the best-known and most commonly used method is the Asset Maintenance Maturity Model. 

The Asset Maintenance Maturity Model has been around for many years now and while it has several variations, the fundamentals are the same.  It is also utilized in a number of best practice standards. In essence, the levels of maturity are defined as the following: 

Asset Maintenance Maturity Model

A high-level description of each level of the Asset Maintenance Maturity Model is as follows: 

Stage 1 – Reactive Maintenance 

No maintenance plan, equipment is basically ‘ran to failure’ and then repaired.  Often chaotic with significant time spent ‘firefighting’ issues in an ad-hoc manner. 

Stage 2 – Planned Maintenance 

Maintenance is performed on a fixed time interval in a repeatable way, rather than simply leaving it to fail.  Bad Actor data is collected, and spares/tools required identified and held. 

Stage 3 – Preventative Maintenance 

Maintenance process has been well defined in a formalized framework (TPM, RCM etc).  Criticality is ranked, together with failure modes and classifications.   

Stage 4 – Predictive Maintenance 

IoT/Sensor Data used to better understand asset health, time-based maintenance replaced with connection-based maintenance, using sensor data to predict when failures will occur, rather than assuming it will after a set time interval.

Stage 5 – Advanced Optimized Predictive Maintenance 

Plant is monitored in real-time with integrated sources of data measured from multiple sources.  AI and Machine Learning tools integrate data to provide better insight and understanding. 

Predictive Maintenance and Beyond COVID-19

While most companies have had robust and well defined Reactive, Planned and Preventative Maintenance routines in place for years, few have moved further up the model into Predictive Maintenance and beyond.  There may have been a number of good reasons for this, for example: 

  • Investment 
  • Perceived risk of change 
  • Impact on performance 

However, this is now beginning to change. With the effects of COVID-19 continuing to be felt around the globe, and with no return to what was previously considered ‘normal’ in sight, the initial plan to cancel all non-essential maintenance is beginning to fail. Instead, companies are having to find new ways of working, often with reduced workers, reduced direct interaction between teams and reduced tool/wrench time.

Technology is now being relied on more than ever in the workplace, with products like Microsoft Teams and Zoom flourishing, replacing in person meetings and teams with video calls and online collaboration. 

Does the same increased dependence on Technology translate to the Maintenance World?

In reality – Yes and No. While sensor technology exists for almost any industry environment, the stakes are much higher.  If a team of people miss a meeting, it is an inconvenience.  If maintenance is missed because a sensor does not measure correctly, it could be catastrophic.  Due to this, confidence in the technology is needed before it is relied upon. 

So, how do you gain confidence in something before it is relied upon? That one is simple. With testing.

Predict Maintenance with Asset Health Insights from Sensor Data

Sensors now are relatively cheap and can be installed either in an evasive or non-evasive capacity. Furthermore, sensor data can be utilized, reported on, and analyzed in under 24 hours using LoveYourAssetBy subscribing critical assets, they can be monitored with minimal invasion and cost overhead (many current assets have sensors pre-installedand they can be retro-fitted to suit requirements). Perhaps most importantly, without any change to existing maintenance routines or procedures.  If the asset health data suggests it will fail before the asset is next maintained, you have the opportunity to bring maintenance forward, or instead, keep everything as-is and test the Asset Health data.   

Traditional Maintenance alongside Predictive Maintenance?

This option to run sensor data and traditional maintenance routines in parallel enables companies to fully test sensor data before any reliance is placed upon it.  This enables companies to begin moving towards Predictive Maintenance with minimal risk and without effecting their existing routines.  Companies can then decide how best to use the new Asset Health Insight, which may be to simply supplement their existing routines, to moving away from standard time-based preventative maintenance, to predictive and even reliability-based maintenance.  The key point is that making that initial step does not have to be a move away from what you are currently doing, technology can supplement just as well as replace.

Unsure on what Sensors are available or how they can be installed and set up? Or do you have sensors available and are not making use of them? 

Get in touch with us at BPD Zenith and we will be happy to talk you though next steps, what options are available and help you move forward on your maintenance maturity journey today. 

Richard Donaldson

Richard Donaldson

Before joining BPD Zenith (UK), Richard was engaged with the Nuclear and Utilities industries, where he worked on the UK's first ever large scale biomass-only power station. Richard has been the Technical Lead and Project Manager on many Maintenance and Supply Chain Business Improvement projects, as well as Asset Transitions and Maintenance Management system upgrades. Since joining BPD in 2009, Richard has predominantly focused on the Oil and Gas sector, completing projects both in the UK and World Wide. When Richard isn’t delivering successful Maximo implementations, he enjoys spending time with family and has a keen interest in the world of VR.

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