Case study: Identifying vane failure in turbine data

In late 2015, a deployed combustion turbine experienced a row two vane failure, which caused massive secondary damage to the compressor, resulting in nearly two months of downtime and up to $30M in repairs costs and lost opportunity. This failure, though rare, is representative of typical catastrophic events that are very difficult to catch.

SparkCognition developed, trained, and validated high-performance machine learning models for each steady and transient operating mode, using two years’ worth of data from the combustion turbine in question. Read this case study to find out how the solution provided one month’s advance notice of a failure and an expected cost reduction of 30%.

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