Using Apache Spark for Predicting Degrading and Failing Parts in Aviation
Description
Throughout naval aviation, data lakes provide the raw material for generating insights into predictive maintenance and increasing readiness across many platforms. Successfully leveraging these data lakes can be technically challenging. However, the data they hold can inform maintenance decisions and help fleets improve readiness by revealing detectable conditions prior to component degradation and failure. Civilian and military aviation datasets are extremely large and heterogeneous. The authors are successfully using Spark to help overcome these challenges within ETL pipelines. Spark also facilitates ad-hoc and recurring reporting for aircraft component health checks at scale, which are created in collaboration with in-house engineering departments which flag recorded flights for known issues. Spark ML is used to flag anomalous data by fitting regression models to historical data and comparing model outputs to observed flights. Feature deviation from model output is measured for each new flight, and flights that appear to be anomalously out of expected ranges are flagged for human review. Apache Spark has enabled a small team to handle a large volume of data spanning hundreds of …
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