Current State

• The client is a leader in renewable energy  principally involved in the fabrication and construction of wind farms​

• Client’s team currently uses a heuristic model-based framework for enabling predictive maintenance​

• The team would like to move towards a fully automated and analytical framework for establishing predictive maintenance across all its wind farms

Gap

• Need to identify the relationship between parameters and turbine faults

• Need to establish a comprehensive escalation matrix for issue severity identification​

Future State

Outcome: The team observes decreased downtime due to appropriate predictive maintenance of wind turbines and does not need to constantly monitor the framework​

Behavior: The team is able to free up time from manual maintenance of the predictive maintenance framework​

Insights: Inference Labs has developed an intelligent and automated framework which identifies turbines at risk of fault, creates relevant work orders and recommends appropriate corrective actions​

An ensemble model will enable identification of time and reasons for device failure​

Time series prediction of device health and performance parameters will answer when the device would encounter an issue​

Appropriate data exploration will yield necessary parameters for model creation, resulting in a robust issue identification framework ​

Machine learning will be used to identify the severity of an issue based on results of previous models i.e. time to failure, the reason for failure and associated cost

Past resolutions for similar issues will be used to generate the best possible resolution recommendation for an predicted problem