Moving on from maintenance modelling

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Maintenance, as defined by the dictionary, is the process of keeping something in good condition. It is part of the engineer’s daily role and, through the years, has evolved from old clipboard-based data collection and maintenance processes to initiating performance models and digital workflows.  However with the emergence of the industrial internet of things (IIoT) and growing demand for smart devices and consumer-style applications at work, the world of maintenance is once again evolving and entering a new era.

Technologies, such as machine learning and Asset Performance Management (APM), empower industry to take control and optimise their processes at unprecedented levels. They are shifting maintenance away from error prone, legacy models and utilising old and new data generated by a company's assets (such as its machines and supply chains) to identify potential signs of downtime or failure and conduct effective predictive maintenance before the issues impact the business as a whole.   

Although not new, Asset Performance Management (APM) has blossomed thanks to  low-touch machine learning. The widespread integration of machine learning in APM marks a transition from estimated engineering and statistical models towards measuring asset behaviour patterns. Manufacturing facilities staff can now readily extract value from existing design and operations data to optimise asset performance.

Today, there’s a growing realisation that maintenance alone cannot solve the unexpected asset breakdown problem. Market-leading companies understand that they have gone as far as possible with traditional preventative maintenance techniques. Predictive maintenance represents the next frontier.

The emergence of low-touch machine learning

Data-intensive and complex environments in industries, such as manufacturing and energy, are prime candidates to deploy the new advances in reliability management. Deployed coherently, with appropriate automation, machine learning enables greater agility and flexibility to incorporate current, historical and projected conditions from process sensors, as well as mechanical and process events. Systems become automatic and agile, flexible models emerge that learn and adapt to real data conditions, and incorporate all the nuances of real asset behaviour.

Data capacities and computational capabilities are so great that internal staff can now perform active, accurate management of individual processes and mechanical assets. This management capability can now be applied to combinations of assets — plant-wide, system-wide or across multiple locations.

This pivot in APM’s capabilities is timely for these industries.  Process manufacturers are under economic pressure, and razor-thin operational margins are pushing process industry executives to look to APM for additional return on investment.

With that in mind, here are five machine learning best practices that command state-of-the-art reliability management, across multiple process industries.

Over the last two decades, every attempt at massive data analysis from diverse sources of plant data collected from sensors has run into issues around collection, timeliness, validation, cleansing, normalisation, synchronisation and structure issues.

Often such data preparation can consume 50–80 percent of the time to execute and repeat data mining and analysis. However, that process is essential to ensure appropriate and accurate data that allow end-users to trust the analytical results. New advances in APM have automated most of the data preparation process to assure trust and reveal previously undiscovered opportunities with minimal user preparation.

2. Condition-Based Monitoring

Once data is trustworthy, condition-based monitoring (CBM) can be applied. The plant conditions vary constantly, according to mechanical performance of assets, feedstock variations in quality, weather conditions and production timeline and demand changes. Static models cannot work

under such duress. In addition, focusing CBM on mechanical equipment behaviour can reveal only a small fraction of the true issues causing degradation and failure.1

To address the shortcomings of legacy CBM, new advances in APM deliver comprehensive monitoring of the mechanical and upstream and downstream process conditions that can lead to failure.

3. Work Management History

Problem identification, coding and a standard approach of problem resolution provide an important baseline for the exact failure point in the lifecycle of an asset. OEM data that may live in a big data solution can provide insight into process issues and outliers specific to the configuration and engineering within the plant process.

4. Predictive and Prescriptive Analytics

Clean data and CBM enable in-place predictive analytics: a process to interpret past behaviour and predict future outcomes. In contrast, using engineering and statistical models to estimate the future readings of sensors, and interpret variances from actual readings, is an error-prone technique. Top performers use inline, real-time analysis of the patterns of normal and failure behaviours of process equipment and machines.

When performed correctly, predictive analytics can accurately portray asset lifecycle and asset reliability and focus on the early root cause of degradation. The insights available provide

accurate, critical lead times. This allows time for decisions that can eliminate damage and maintenance or, at least, provide preparation time to reduce the time-to-repair and mitigate the consequences.

Best-in-class APM provides prescriptive advice based on root cause analysis and presents information on the approach that will proactively avoid process conditions that cause damage, and/or advise on the precise maintenance required.

5. Pool and Fleet Analytics

The next level of analytics allows patterns discovered on one asset in a pool or fleet to be shared, enabling the same safety and shutdown protection for all equipment. Once deployed, companies can rapidly scale solutions from a unit to multiple sites. From all local systems, information roll-up from disparate sites into one larger model provides asset performance comparisons across sites and plants, creating common baselines that highlight areas for improvement.

 Conclusion

Maintaining the norm for industry is an increasingly complex affair, particularly as we enter the fourth industrial revolution - which sees those within the industry further integrate the physical and digital worlds.  As technology further drives how the world of manufacturing, the models we use to maintain the assets at the heart of a business must also be smarter. To lean upon predictive maintenance tools, such as APM,  means business can remain 99.9% efficient, increase operating savings and improve profitability.

John Hague, senior vice president and general manager, Asset Performance Management, AspenTech
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