Vibration Based Fuzzy-Neural System for Structural Health Monitoring

Lakshmanan Meyyappan (Laks)

1. Objectives

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

2. Overall System

Vibration Data Signal Processing, Feature Extraction and Data Cleansing

Fuzzy Logic Detection System

Possible Damage?

Bridge is perfect

Neural Network Prediction System

Damaged?

Damage Value

Small Damage

Medium Damage

Large Damage

YES

NO

YES

NO

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)

3.3 Experiment

Teardrop

Bridge

4. Damage Detection

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

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

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

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

abcFTI )1(2

1)(

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)

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

6. Neural Network Prediction System

T A N S I G

b1

+

IW 1,1

P U R E L I N

b2

+

LW 1,1

1 1

Output Layer

a1 = tansig (IW 1,1 p1 + b1) a2 = purelin (LW 2,1 a1 + b2)

100 15 X 1

15 X 100

100 X 1

15 X 1

15 100 X 1

100 X 15

15 X 1

100 X 1

p1

n1

a1

n2

a3 = y

100 X 1

Hidden Layer Input