Precise Diagnosis of Unknown Fault of High-Speed Train Bogie Using Novel FBM-Net

Zhang, Yiming; Qin, Na; Huang, Deqing; Du, Jiahao

doi:10.1109/tim.2022.3180410

Cited by 6 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al [20] proposed an open fault semantic subspace-based open-set fault diagnosis and inference framework that relies on outlier scores to discriminate whether the samples are from an unknown fault type. Zhang et al [21] combined fractional Brownian motion with a one-dimensional convolutional neural network to enhance the decision uncertainty of the model for unknown samples. They verified the effectiveness of a high-speed train dataset.…”

Section: Introductionmentioning

confidence: 99%

A novel simulation-assisted transfer method for bearing unknown fault diagnosis

Huang,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%