Smart Structures and Systems

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CNN based data anomaly detection using multi-channel imagery for structural health monitoring

  • Shajihan, Shaik Althaf V. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Wang, Shuo (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Zhai, Guanghao (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Spencer, Billie F. Jr. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • Received : 2021.04.28
  • Accepted : 2021.09.24
  • Published : 2022.01.25
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Abstract

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

Keywords

Acknowledgement

The authors would like to thank the organizers of the International Project Competition for SHM (IPC-SHM 2020), ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for generously providing the data used in this study. We gratefully acknowledge the guidance and constructive criticism offered by Dr. Yasutaka Narazaki, Zhejiang University-UIUC Institute throughout this study. Additionally, the second and third authors acknowledge the partial support of this research by the China Scholarship Council.

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