
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The application supports the operator to improve the maintenance of machinery through a reports and notifications system based on machine learning ...learn more
Project status: Under Development
Intel Technologies
Intel Opt ML/DL Framework,
Movidius NCS
The impact of unscheduled equipment downtime can be detrimental for any business. It's critical to keep field equipment running to maximize utilization and performance, and to minimize costly, unscheduled downtime. Early identification of issues can help allocate limited maintenance resources in a cost-effective way and enhance quality and supply chain processes. This predictive maintenance project provide a data science project from data ingestion, feature engineering, model building, and model operationalization and deployment. Most businesses are interested in predicting when these problems might arise to proactively prevent them before they occur. The goal is to reduce the costs by reducing downtime and possibly increase safety. The business problem is to predict issues that are caused by component failures. The business question is "What's the probability that a machine goes down due to failure of a component?"
A machine learning algorithm is used to create the predictive model. The model is trained on historical data that's collected from machines.
Azure ML Studio, PowerBI, TensorFlow, Movidius