Introduction and challenges
Maintenance operations are on the cusp of a paradigm shift with the growth and accessibility of breakthrough technology, such as the Internet of Things and machine learning. These developments will bring about significant improvement in our ability to predict changes in machine operating performance.
The commercialisation of sensors, which can record all types of data, combined with the power of data analytics, provides maintenance teams with formidable tools with which to make decisions and optimise maintenance activity.
Perhaps the most exciting development is the emerging concept of predictive maintenance which resulted from the joining of preventive maintenance and curative maintenance which focuses on repairs to improving the lifetime of equipment.
For any new innovations to be successfully embedded the basics need to be in place – this is perhaps the biggest challenge for effective predictive maintenance. Collecting the right data, understanding the operating modes and optimising failure prevention are all critical to creating a powerful predictive solution.
The rapid growth of network bandwidth and coverage, paired with dematerialized supports and augmented reality tools and digital twins allows the renewal of traditional maintenance processes towards a more efficient and remote maintenance.
The implementation of these solutions is complex; the requirements need to be carefully defined and the pre-requisites understood, therefore businesses need the right partners to be able to technically assist their operational teams.
How we can help
Argon & Co supports its clients though all aspects of predictive maintenance projects, including:
- The diagnostic phase, which informs the business case, the level of maturity of the organisation and defines the prerequisites
The implementation phase, which involves selection of the solution, piloting and roll-out
To do this, we rely on strong functional expertise and in-depth knowledge of the technological ecosystem.