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Predictive Maintenance

Fixing Machinery Before a Breakdown with AI


According the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. Traditional preventive maintenance processes require machines to be repaired at intervals based on time or usage. These methods, however, still result in significant instances of equipment failure resulting in idle workers, increased scrap rates, lost revenues and angry customers. In addition, preventive maintenance may replace parts that still have significant working life, which can be a waste of time and money.


AI based predictive maintenance uses a variety of data from IoT sensors imbedded in equipment, data from manufacturing operations, environmental data, and more to determine which components should be replaced before they break down. AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. McKinsey and Company found that AI based predictive maintenance typically generates a 10% reduction in annual maintenance costs, up to a 25% downtime reduction and a 25% reduction in inspections costs.


The mission at is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. Leading industrial brands like Stanley Black and Decker have partnered with to deliver the next generation of industrial manufacturing solutions powered by H2O Driverless AI. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale machine learning efforts by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to manufacturers including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.

Related Case Studies

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The platform’s feature engineering and scoring pipeline generation are better than anything we’ve seen out there right now.”

Dr. Robert Coop, Artificial Intelligence and Machine Learning Manager, Stanley Black & Decker

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H2O really shines in model training and scoring and we can do it all without sampling the data.”

Lou Carvalheira, Principal Data Scientist, Cisco


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