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The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Shen, Junxian / Ma, Tianchi / Song, Di / Xu, Feiyun: Quantitative blade damage detection based on multisource domain and multistage joint transfer. In: Structural Health Monitoring.

    https://doi.org/10.1177/14759217231226381

  2. Ma, Tianchi / Shen, Junxian / Song, Di / Xu, Feiyun: A two-level fusion model of vibro-acoustic signals for centrifugal fan blade crack detection. In: Structural Health Monitoring.

    https://doi.org/10.1177/14759217241229213

  3. Ding, Peng / Song, Di / Shen, Junxian / Zhao, Xiaoli / Jia, Minping: A novel graph structure data-driven crack damage identification for compressor blade based on vibro-acoustic signal. In: Structural Health Monitoring.

    https://doi.org/10.1177/14759217231222002

  4. Shen, Junxian / Ma, Tianchi / Song, Di / Xu, Feiyun (2023): Incremental learning BiLSTM based on dynamic proportional adjustment mechanism and experience replay for quantitative detection of blade crack propagation. In: Structural Health Monitoring, v. 23, n. 2 (July 2023).

    https://doi.org/10.1177/14759217231170723

  5. Shen, Junxian / Song, Di / Ma, Tianchi / Xu, Feiyun (2023): Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion. In: Structural Health Monitoring, v. 22, n. 5 (February 2023).

    https://doi.org/10.1177/14759217221139134

  6. Song, Di / Shen, Junxian / Ma, Tianchi / Xu, Feiyun (2022): Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism. In: Structural Health Monitoring, v. 22, n. 3 (September 2022).

    https://doi.org/10.1177/14759217221116599

  7. Ma, Tianchi / Song, Di / Shen, Junxian / Xu, Feiyun (2022): Blade crack detection using variational model decomposition and time-delayed feedback nonlinear tri-stable stochastic resonance. In: Structural Health Monitoring, v. 22, n. 2 (May 2022).

    https://doi.org/10.1177/14759217221109882

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