A neural network approach to failure diagnostics for underwater vehicles
MetadataShow full item record
This paper addresses the proposed use of Kalman filters and Artificial Neural Networks to provide the detection, and isolation of impending system failures. Such system health diagnosis is necessary to the overall success of mission controllers for AUVs. Two examples of network designs are given. The first addresses the identification of anomalous changes to the vehicle's acceleration behavior resulting from possible propulsion system changes of loss of propulsion efficiency from fouling. The second example relates to the identification of excessive frictional loads in the propulsion drive train that may cause motor failure. In each case, the training method and the resulting decision surface characterization of the networks so designed are given.
Showing items related by title, author, creator and subject.
Clarke, William M. (Monterey, California. Naval Postgraduate School, 2009-12);The 9/11 Commission report described how driver's licenses, identification cards and travel documents are as important as weapons to terrorists. Vulnerabilities in existing identification systems provide the opportunity ...
Moore, Todd M. (Monterey, California. Naval Postgraduate School, 2010-12);This thesis investigates the ties between biometrics and state security by analyzing biometric identification and screening programs, their structural elements, and ultimately their effectiveness. Although biometric ...
Yeo, Jiunn Wah. (Monterey California. Naval Postgraduate School, 2006-12);Target recognition and identification in battlefields has been a crucial determinant to the ultimate success or failure of modern military campaigns. Since World War II, the Identification of Friend or Foe (IFF) systems ...