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Transforming Predictive Maintenance with AI

When it comes to AI + predictive maintenance, manufacturing and many other sectors have a lot to gain. Rather than correcting problems once they occur, predictive maintenance prevents problems from ever happening in the first place.

With run-to-failure methods, low investment in maintenance leads to prohibitively high repair costs and downtimes once equipment does fail. With preventative strategies, constant, potentially unnecessary maintenance has very high costs without corresponding payoffs. With predictive maintenance, both high repair costs and time spent unnecessarily doing maintenance are minimized.  

While algorithms are not perfect, they offer the opportunity to make maintenance choices that are based on past trends and real-time data; it offers an entirely new cost-saving dimension.


Second-Order AI for Predictive Maintenance

Predictive maintenance generally requires another level of AI to optimize subsequent decisions about a high-value asset’s upkeep. Once it’s clear repair of a high-value asset is necessary via predictive maintenance techniques using data from all kinds of sources, including Internet of Things (IoT) sensors, initial perhaps automated first steps or processes will be kicked off (things like filing a work order or notifying maintenance staff).

From there, that’s where second-order maintenance come in. Because taking high-capital assets out of service can be extremely costly in and of itself (even when compared to the benefits of identifying necessary maintenance before run to failure), the next questions are when and how?

Take, for example, a truck from a large fleet with a part identified by your predictive maintenance system as being N days away from failure. Once identified, a member of the data team should be ready to send a secondary follow-up report to the maintenance team detailing the best possible options for time and place of service.

The Dataiku Advantage

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. More than 250 customers across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku to massively scale AI efforts.

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Customers building predictive maintenance solutions with Dataiku benefit from:

  • The ability to centrally and seamlessly connect to data, wherever it’s stored.
  • A simple and fast interface for ETL, including interactive data cleaning and integrated advanced processors.
  • AutoML features, including the ability to compare dozens of algorithms directly from the  Dataiku interface ( both for supervised and unsupervised tasks).
  • One-click model deployment on the cloud with Kubernetes.
  • Robust model monitoring features to prevent model drift.

Improving Manufacturing Processes with Essilor

See how one manufacturing company, Essilor, uses Dataiku to harness large, heterogeneous datasets and develop a robust predictive maintenance solution.

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Go Further

Go Beyond with Second-Order Analytics

Value in AI-based predictive maintenance systems really comes in the decisions that follow that prediction, known as second-order analytics.

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