Actuator Fault Detection for Autonomous Underwater Vehicles Using Unsupervised Learning

Matt Kemp and Ben Raanan
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_17_066.pdf3.96 MBSeptember 6, 2017 - 10:38am

Many Autonomous Underwater Vehicles (AUV) have high rates of false-alarms because their health management relies on user-generated rules. We suggest that the high false-alarm rate could be substantially lowered if fault-detection were based on actual actuator performance instead of heuristics. We collected performance data on a critical AUV actuator, a mass-shifter, in order to develop an unsupervised fault detector. We found that a small number of features were sufficient to detect known and novel faults with a high probability of detection and a low false alarm rate. We also found that n-point false-alarm reduction schemes performed poorly due to correlation during startup.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
066
Page Count: 
7
Submission Keywords: 
autonomous
autonomous robots
Subsystem Health Monitoring
Component-based systems
component-level PHM
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Systems and platform applications
Submitted by: 
  
 
 
 

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