{"wysiwyg":[{"title":"Overview","content":"

We use predictive modelling to make asset maintenance preventive rather than reactive, and to help clients plan with confidence.

Our monitoring systems do a great job of identifying faults in systems accurately and early, so they can be fixed before they trigger chain reactions that lead to system-wide failures.

But what if we could do more? What if we knew ahead of time that a fault was likely to occur? Then, asset maintenance could be preventive rather than reactive, and clients could plan their asset management programs with much greater confidence and efficiency.

This is not a future scenario – it’s already happening, thanks to our advances in machine learning. By feeding large amounts of data with many parameters into complex algorithms, we can identify patterns associated with the lead-up to equipment failures.

Importantly, our approach is not simply based on blind faith in the predictive power of data. We use a true engineering approach, asking how and why the pattern is occurring before accepting it as genuinely predictive.

Our sophisticated algorithms are able to adjust their future predictions based on previous interventions, essentially learning from past actions.

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