Probabilistic and Reliability-Based Health Monitoring Strategies for High-Speed Naval Vessels

Abstract

In this project, a strategy is proposed for fatigue life estimation of a ship hull using a wireless sensor network installed in the hull for autonomous health monitoring. First, a rainflow counting algorithm is implemented as a continuous time-domain approach to fatigue estimation. Cycles are defined by a rainflow counting procedure and are kept on-board the wireless sensing unit in a histogram structure for long-term storage. Fatigue damage is accumulated by the Palmgren-Miner linear summation method. Second, a statistical approach called the Dirlik procedure is used. It relies on spectral moments of the stress-time history power spectral density (PSD) function. The Dirlik procedure outputs a probability density function (PDF) of stress ranges tailored to resemble the rainflow counting results. The PDF is converted to damage using an S-N curve and again accumulated by the Palmgren-Miner method. Experimental tests are conducted on an aluminum hull stiffened element specimen as part of the Monitored Aluminum Hull Integrity (MAHI) test program to verify the embedded fatigue life estimation procedures.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA556760

Entities

People

  • Jerome Lynch
  • Kincho H. Law
  • Sean P O'Connor

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Data Acquisition
  • Detectors
  • Engineers
  • Fatigue Life
  • Littoral Combat Ships
  • Materials
  • Mathematical Analysis
  • Naval Architecture
  • Naval Vessels
  • Probability
  • Probability Density Functions
  • Sensor Networks
  • Ships
  • Structural Health Monitoring
  • Time Domain
  • Wireless Sensor Networks

Fields of Study

  • Engineering

Readers

  • Naval Architecture and Marine Engineering.
  • Statistical inference.
  • Structural Health Monitoring of Composite Structures.