Towards Performance Prognostics of a Launch Valve

Abstract

Due to its criticality in aircraft carrier steam catapult operations, the performance of the Launch Valve is monitored using timer components to determine the elapsed time for the valve to achieve a set opening distance. Significant degradation in performance can lead to loss in end speed of the catapult and result in loss of aircraft / lives. This paper presents a method of using existing timing data for anomaly detection and predicting when maintenance is required (MIR) for a Launch Valve. Features such as mean and standard deviation of timing values are extracted from clock time data to detect anomalies. Neyman-Pearson Criterion and Sequential Probability Ratio Testing are used to formulate a decision on the degraded state. Once an anomaly is detected, an observation window of the previous N filtered samples are used in a risk sensitive particle filter framework. The resulting distribution is used in the prediction of shots until MIR. Performance degradation is extracted from training data and modeled as a third order polynomial. The algorithm was tested on two test sets and validated by Subject Matter Experts (SMEs) supplying the data. An Alpha-Lambda performance metric shows the time predictions until MIR fall inside an acceptable performance cone of 20% error.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002262

Entities

People

  • Everard Martin
  • Glenn Shevach
  • James Hing
  • John Wheelock
  • Larry Venetsky
  • Mark R. Blair

Organizations

  • Naval Air Warfare Center

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Anomaly Detection
  • Catapults
  • Change Detection
  • Data Sets
  • Detection
  • Electrical Engineering
  • Engineers
  • Information Science
  • Mechanical Engineering
  • Particles
  • Probability
  • Reliability
  • Sequential Monte Carlo Methods
  • Standards
  • Test Sets

Fields of Study

  • Engineering

Readers

  • Internal Combustion Engine (ICE) Technology.
  • Neural Network Machine Learning.
  • Statistical inference.