Organizations that depend on a fleet of vehicles for daily operations understand how crucial it can be to keep them running smoothly. Missed appointment for service providers result in customer churn and lost revenues from new installations. Traditional preventative maintenance processes require vehicles to be repaired at regular intervals based on time or usage. These methods, however, still result in instances of vehicle breakdown resulting in accidents, idle workers, lost revenues and angry customers. In addition, preventative maintenance strategies 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 vehicles, fleet data, weather data, and more to determine which components should be replaced before they break down or cause an accident. 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 and usage. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. AI systems can even warn drivers and fleet managers that components may fail soon, so that they can take proactive measures to change vehicles to keep scheduled appointments.
The mission at H2O.ai 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 telecommunications brands have partnered with H2O.ai to deliver the next generation of solutions powered by H2O technologies. 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 telecom companies including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.
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Bhavana Bhasker, et al.
Data Scientist, Comcast
"We reached 90% accuracy in the real-time deployment and our results were in-sync with the training phase, which was really good."Watch the Video