Moving towards data-driven maintenance

Exploring the roadmap of preventive to prescriptive maintenance,  this blog presents a brief explanation of involved technologies and their potential, showing how, in Stork’s vision, collaboration between humans and machines can evolve in equal relationship to the benefit of asset owners.

By investing in such digital solutions, modern executives can assess hundreds of parameters to predict asset health, allowing them to proactively take actions that result in lower maintenance costs, improved uptime and enchanced operational safety.

Is this possible today? Yes. Why isn’t everyone doing this? New strategies require new mindset and skill sets to bring valuable technology to the workforce. All in all, plants are running, data is being collected and there are huge opportunities to drive down costs and increase revenues.

In the maintenance industry, efficiency improvements are traditionally achieved by using highly experienced workers to do the right things. However, advances in digital technologies now make it possible to detect different anomalies than usual by capturing the asset-health status in the form of physical data. Data-driven maintenance is within industries’ reach and Stork supports its clients to move from reactive to predictive maintenance, a journey driven by data.

Waiting for an asset to fail might sometimes do the trick. Often however, reactive maintenance leads to unnecessary time pressure and high costs involved. By learning from failure patterns, organizations then tend to move towards maintenance models which predict when certain assets require attention.

As preventive models do not identify earlier-than-expected failures, a focus on understanding the links between equipment-failure causes and effects paves the path to condition-based maintenance. This approach looks at an asset’s actual state as the deciding factor on its need for maintenance.

Real-time insight into the asset’s status allows asset owners to focus their maintenance strategies on improving uptime. Predictive maintenance is a data-driven decision-making methodology where, via the application of algorithms, it is possible to extract an insight of an asset’s performance.

The following building blocks are key in achieving data-driven predictive maintenance:

  • Sensors: the eyes and ears of predictive maintenance.
  • Connectivity: nervous system of operations.
  • Cybersecurity: ensuring the absolute security of the physical plant.
  • Data analytics: extracting insight from all sorts of data.
  • Mobile devices: taking data visualization out of the control room.

 

By investing in digital solutions, executives can assess hundreds of parameters to predict, with reasonable accuracy, what will happen to their assets.

The only step to be taken is an integrated approach – both in terms of partnership and technology-  towards shaping optimal asset management strategies. By having the right people bringing it all together.

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