On Optimizing Anomaly Detection Rules for Gas Turbine Health Monitoring

Weizhong Yan, Lijie Yu, Jim Sherbahn, and Umang Brahmakshatriya
Submission Type: 
Full Paper
phmc_13_020.pdf733.84 KBSeptember 11, 2013 - 10:16am

Gas turbine health monitoring is a critical process in
preventing costly unplanned maintenance and secondary
damage. To monitor gas turbine health, control signals are
typically collected and analyzed using anomaly detection
rules and models to assess failure likelihood based on
observed data patterns. An analytic designer will often deal
with rule optimization tasks in order to maximize failure
detection and reduce false alarms. Manual tradeoff analysis
is typically time consuming and suboptimal. In this paper,
we attempt to address this issue by introducing a strategy for
automatic and efficient rule optimization. By focusing on
optimizing rule parameters while keeping rule structure
intact, we maximize the rule performance by integrating
domain knowledge with data driven optimization
techniques. Realizing that automated rule tuning can be
computationally expensive and infeasible to complete in
reasonable time, we will leverage our recently-developed
scalable learning framework - iScale that allows for
automatically distributing rule tuning tasks to a large
number of cloud computers, which not only dramatically
speeds up tuning process, but also enables us to handle big
size of historical data for tuning. We also explore different
search methods to make rule tuning more efficient and
effective and finally demonstrate our rule optimization
strategy by a real-world application.

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Submission Keywords: 
anomaly detection; gas turbine; health monitoring; optimization
Submission Topic Areas: 
Industrial applications
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