average % of unplanned downtime using predictive maintenance
average % unplanned downtime using planned maintenance
average % of unplanned downtime using reactive maintenance
TensorIoT consultants designed and developed the AWS infrastructure necessary to allowed the client to utilize Amazon Lookout for Equipment to improve their asset monitoring and management.
How can we created the framework for scaling IoT device connectivity that supports cutting edge AWS features including Amazon Lookout for Equipment?
TensorIoT architected the technical infrastructure that the client required in order to utilize the benefits of Amazon Lookout for Equipment with the ability to scale to thousands of assets.
The client is a large scale energy company focused on serving the agricultural, chemical, and energy sectors. Given their considerable scale, improvements to operational efficiency produce substantial savings, incentivizing the client to continue to invest in refining their internal systems and procedures.
The client wanted to automate and improve their asset health monitoring process to reap a number of benefits, including reducing the cost of maintenance on their assets (repair work, time on tools, work performed on assets, HC based on asset need). They also wanted a faster process for building and improving anomaly detection models, and to set the groundwork for scaling their anomaly detection to cover thousands of assets. The anomaly detection feature would ideally have a low-friction user experience, to allow workers who aren’t trained in machine learning to be able to leverage anomaly detection. With an improved anomaly detection process, the client will be able to keep their assets running longer before they fail. Lastly the client wanted to be able to optimize the costs for scaling, so that managing resources, training, and models would continue to be affordable, even as the application grows.
TensorIoT built the framework to enable the client to utilize Amazon Lookout for Equipment, giving the customer the ability to decrease the impact of unplanned maintenance, increase uptime, and improve their operational efficiency through anomaly detection.