ColdStart Windfarm Predictive Maintenance Solution

Sentient Science DigitalClone

Sentient Science is a provider of a digital platform connecting operators and suppliers to increase demand and lower the cost of doing business. Sentient Science is a trusted third party to the wind-farm operator providing system and component level predictions and life extension actions to reduce costs and improve reliability of rotating mechanical equipment. You can read more about Sentient Science at – //

Sentient DigitalClone platform connects windfarm operators and wind turbine component suppliers to increase demand and lower the cost of wind energy. DigitalClone platform leverages AWS Cloud and Sagemaker services to develop and deploy material and data science-based predictions and life extension actions that reduce cost and improve the reliability of wind turbines. One of the existing challenges of the platform is the lengthy on-boarding process of windfarm customers. Sentient’s current customer base is 40,000 wind turbines with 20,000 deployed live in the platform. Sentient science is working with TensorIoT to address these challenges to streamline the onboarding process.


The end to end solution will shorten the sales lifecycle of the Sentient DigitalClone platform and drive rapid customer value creation and growth. Cold Start looks at the energy and grid control intelligent edge from wind farm geolocation, install date, climate, terrain and MW capacity to provide a failure rate assessment. The Cold Start model can be applied to every wind turbine in the United States. The initial data analysis can be integrated with a full turbine model that can be trained using customer data.

About AWS IoT Analytics

AWS IoT Analytics is a fully-managed service that makes it easy to run sophisticated analytics on massive volumes of IoT data without having to worry about all the cost and complexity typically required to build your own IoT analytics solution. It is the easiest way to run analytics on IoT data and get insights to make better and more accurate decisions for IoT applications and machine learning use cases.

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