Convolutional neural network-based data anomaly detection considering class imbalance with limited data

Yao Du, Ling Fang Li, Rong Rong Hou, Xiao You Wang, Wei Tian, Yong Xia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

Original languageEnglish
Pages (from-to)63-75
Number of pages13
JournalSmart Structures and Systems
Issue number1
Publication statusPublished - Jan 2022


  • Class imbalance
  • Data anomaly detection
  • Focal loss
  • Limited labelled data
  • Transfer learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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