Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis

Abstract

This paper presents the application of recurrence plots (RPs)and recurrence quantification analysis (RQA) in model-based diagnostics of nonlinear systems. A detailed nonlinear mathematical model of a servo electro-hydraulic system has been used to demonstrate the procedure. Two faults have been considered associated with the servo valve including the increased friction between spool and sleeve and the degradation of the permanent magnet of the valve armature. The faults have been simulated in the system by the variation of the corresponding parameters in the model and the effect of these faults on the RPs and RQA parameters has been investigated.A regression-based artificial neural network has been finally developed and trained using the RQA parameters to estimate the original values of the faulty parameters and identify the severity of the faults in the system.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002749

Entities

People

  • C. Nataraj
  • Cedrick A. Kwuimy
  • M. Samadani

Organizations

  • Villanova University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Armatures
  • Complex Systems
  • Computational Science
  • Control Systems
  • Control Systems Engineering
  • Engineering
  • Engineers
  • Flow Rate
  • Friction
  • Lepidoptera
  • Machine Learning
  • Mathematical Models
  • Mechanical Engineering
  • Neural Networks
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Permanent Magnets

Fields of Study

  • Engineering

Readers

  • Electrical Engineering
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference