Predictive maintenance is a method of detecting anomalies in your operation and potential problems in equipment and processes using data analysis tools and methodologies, so you can remedy them before they fail. Predictive maintenance, in theory, provides for the lowest possible maintenance frequency to avoid unforeseen reactive repair while avoiding the costs associated with performing too much preventative maintenance.
Inference Labs is helping a global Renewable Energy leader to develop an intelligent framework to enable model-based predictive maintenance.
The client is a leader in renewable energy principally involved in the fabrication and construction of wind farms. They currently uses a heuristic model-based framework for enabling predictive maintenance. They would like to move towards a fully automated and analytical framework for establishing predictive maintenance across all its wind farms.
Inference need to identify the relationship between parameters and turbine faults and establish a comprehensive escalation matrix for issue severity identification.
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
