A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification

Recently, a variety of intelligent structural damage identification algorithms have been developed and have obtained considerable attention worldwide due to the advantages of reliable analysis and high efficiency. However, the performances of existing intelligent damage identification methods are heavily dependent on the extracted signatures from raw signals. This will lead to the intelligent damage identification method becoming the optimal solution for actual application. Furthermore, the feature extraction and neural network training are time-consuming tasks, which affect the real-time performance in identification results directly. To address these problems, this paper proposes a new intelligent data fusion system for damage detection, combining the probabilistic neural network (PNN), data fusion technology with correlation fractal dimension (CFD). The intelligent system consists of three modules (models): the eigen-level fusion model, the decision-level fusion model and a PNN classifier model. The highlight points of this system are these three intelligent models specialized in certain situations. The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice. The single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed. Numerical simulations of a two-span concrete-filled steel tubular arch bridge in service and a seven-storey steel frame in laboratory were used to validate the hybrid system by identifying both single- and multi-damage patterns. The results show that the hybrid data-fusion system has excellent performance of damage identification, and also has superior capability of anti-noise and robustness.

[1]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[2]  Belal Al-Khateeb,et al.  Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images , 2018, Comput. Electr. Eng..

[3]  Jian-Da Wu,et al.  An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network , 2009, Expert Syst. Appl..

[4]  Konstantina S. Nikita,et al.  A Power Differentiation Method of Fractal Dimension Estimation for 2-D Signals , 1998, J. Vis. Commun. Image Represent..

[5]  Yang Yu,et al.  A novel deep learning-based method for damage identification of smart building structures , 2018, Structural Health Monitoring.

[6]  R. Bai,et al.  Fractal mechanism for characterizing singularity of mode shape for damage detection , 2013 .

[7]  Oral Büyüköztürk,et al.  Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization , 2015, Comput. Aided Civ. Infrastructure Eng..

[8]  Hui Li,et al.  Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section , 2011, Comput. Aided Civ. Infrastructure Eng..

[9]  Nan Wang,et al.  Fault diagnosis model of adaptive miniature circuit breaker based on fractal theory and probabilistic neural network , 2020 .

[10]  Maosen Cao,et al.  Crack detection in beams in noisy conditions using scale fractal dimension analysis of mode shapes , 2014 .

[11]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[12]  Yang Yu,et al.  Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework , 2020 .

[13]  Lei Jia,et al.  Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network , 2018, Photonic Sensors.

[14]  Robertas Damaševičius,et al.  Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features , 2020, Applied Sciences.

[15]  Weifeng Liu,et al.  Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data , 2020 .

[16]  Wei Fan,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[17]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Shantanu Chakrabartty,et al.  Structural damage identification using image‐based pattern recognition on event‐based binary data generated from self‐powered sensor networks , 2018 .

[19]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[20]  Nicola Chieffo,et al.  Induced Seismic-Site Effects on the Vulnerability Assessment of a Historical Centre in the Molise Region of Italy: Analysis Method and Real Behaviour Calibration Based on 2002 Earthquake , 2020, Geosciences.

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[23]  Chunming Zhang,et al.  A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection , 2013, Int. J. Distributed Sens. Networks.

[24]  Yang Yu,et al.  Novel Hybrid Method Based on Advanced Signal Processing and Soft Computing Techniques for Condition Assessment of Timber Utility Poles , 2019, Journal of Aerospace Engineering.

[25]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[26]  Luc Chouinard,et al.  Defect detection in concrete plates with impulse-response test and statistical pattern recognition , 2021 .