Vibration Based Fuzzy-Neural System for Structural Health Monitoring

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Vibration Based Fuzzy-Neural System for
Structural Health Monitoring

• Lakshmanan Meyyappan (Laks)

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Objectives

• The main goal is to develop a practical
  real-time structural health monitoring system
  using smart systems engineering concepts and
  tools.

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2. Overall System
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3.1.1 Vibration Signatures

• Advantages
• NDE Technique
• Global Analysis
• Normal Operation of the Structure
• Small
• Reliable
• Less Expensive (both initial and operating costs)
• Sensitive

• Disadvantages
• Unsupervised Learning Mode
• Data Accuracy (Potential problem with any type of
  data)

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3.3 Experiment

• Teardrop
• Bridge

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4. Damage Detection

• For simplicity of explanation the data collected
  with the sensors attached to the above five
  locations are used.

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4. Damage Detection

• Relationship between the members remains
  the same that is member 3 has the highest power
  spectrum value in all of the above cases followed
  by member 1, 5, 4 and 2 respectively

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5. Fuzzy Logic Decision System

• Goal To take power spectrum values of various
  members as input and predict a possible damage
• Method Fuzzy Ranking System

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5.1 Fuzzy Ranking System

• Fuzzy Ranking based on Fuzzy Integral values
  calculated using the formula
• where a, b, c are the vertices of the triangular
  membership functions
• Alpha is the index of optimism and it varies
  between 0 and 1

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6. Neural Network Prediction System

• Goal To make the final prediction on the
  condition of the bridge
• Inputs
• Fuzzy logic system output
• Speed of the vehicle ( Speed Gun output)

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6. Neural Network Prediction System

• Input 100 Data Points (speed)
• Target 100 Data Points (Power
  Spectrum Peak Value)
• Algorithm Back Propagation (LM Method)
• Layers 2 Layers 15 1
• Transfer
• Functions Tansig Purelin
• Error Rate 1e-8
• Max Epochs 1500

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6. Neural Network Prediction System