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Vibration Based Fuzzy-Neural System for Structural Health Monitoring

Vibration Based Fuzzy-Neural System for Structural Health Monitoring. Lakshmanan Meyyappan (Laks). 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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Vibration Based Fuzzy-Neural System for Structural Health Monitoring

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  1. Vibration Based Fuzzy-Neural System for Structural Health Monitoring Lakshmanan Meyyappan (Laks)

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

  3. Signal Processing, Feature Extraction and Data Cleansing Fuzzy Logic Detection System Vibration Data Possible Damage? YES Neural Network Prediction System NO Damaged? Bridge is perfect NO YES Small Damage Damage Value Medium Damage Large Damage 2. Overall System

  4. 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) 3.1.1 Vibration Signatures

  5. 3.3 Experiment Teardrop Bridge

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

  7. 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

  8. 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

  9. 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

  10. 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)

  11. 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

  12. Output Layer Hidden Layer Input p1 a1 IW 1,1 LW 1,1 a3 = y T A N S I G P U R E L I N 100 X 1 + + n1 15 X 1 n2 15 X 100 100 X 15 100 X 1 b1 b2 15 X 1 1 100 X 1 1 100 15 X 1 15 100 X 1 a1 = tansig (IW 1,1 p1 + b1) a2 = purelin (LW 2,1 a1 + b2) 6. Neural Network Prediction System

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