Publications

2024

1. 

   A Brain-Inspired Energy-Efficient Wide Spiking Residual Attention Framework for Intelligent Fault Diagnosis

   Jiale Liu, and Huan Wang^*

   Reliability Engineering & System Safety, Mar 2024

   Abs DOI Bib

   Intelligent fault diagnosis functions as a necessary tool to prevent substantial damage to industrial products and enhance system reliability. While Artificial Neural Networks (ANNs) have been extensively studied in this context, they still face substantial challenges in resource consumption, robustness, and generalizability. To overcome these limitations, researchers have developed the third-generation neural network based on brain’s structure, namely the Spiking Neural Network (SNN), which leverages the concept of time steps and spiking signals for enhanced spatiotemporal feature processing and energy efficiency. This paper proposes the Wide Spiking Residual Grouping Attention Framework (WSRGA-FW), which incorporates the advantages of both ANN and SNN. The WSRGA-FW employs Extended Gramian Representation for signal encoding to reduce noise impact, followed by a tailored ANN with wide convolutional kernels, optimized residual structures, and Grouped Perception Generation (GPG) Layers. These augmentations increase the network’s representation and robustness, particularly in noisy environments. The backbone ANN is transformed into an SNN model, allowing deployment in portable and miniaturized devices with improved application prospects. Performance evaluations across various noisy scenarios using bearing fault datasets demonstrate that WSRGA-FW surpasses existing networks. Visualization of firing rates and energy consumption calculation contribute to the interpretability and intrinsic energy efficiency advantages of SNNs.

   @article{liu2024braininspired,
     title = {A Brain-Inspired Energy-Efficient {{Wide Spiking Residual Attention Framework}} for Intelligent Fault Diagnosis},
     author = {Liu, Jiale and Wang, Huan},
     year = {2024},
     month = mar,
     journal = {Reliability Engineering \& System Safety},
     volume = {243},
     pages = {109873},
     issn = {09518320},
     doi = {10.1016/j.ress.2023.109873},
     urldate = {2024-12-02},
     langid = {english},
   }

2. 

   An Integrated Framework of Fourier Transform and Transformer for Rotating Machinery Fault Diagnosis

   Xiaopeng Liu, Jiale Liu, Bingxiang Sun^*, and 1 more author

   In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), Jun 2024

   Abs DOI Bib

   Diagnosing faults in rotating machinery is crucial for maintaining operational efficiency and safety. However, existing diagnostic methods, while sometimes effective, often struggle with the complexity of data and noise interference, creating a need for more advanced solutions. In light of this, this paper proposes CFF-Trans, a novel deep learning framework that combines Fourier and Transformer-based techniques for enhanced fault diagnosis in rotating machinery. CFF-Trans excels in extracting frequency domain features from vibration signals, enabling it to effectively differentiate between noise and fault-related components and accurately identify faults even in high-noise environments. Specifically, CFF-Trans integrates convolutional layers for initial feature extraction, Fourier transforms for detailed frequency domain encoding, and Transformer encoders for global dependency mapping. This approach not only improves the noise robustness of the diagnostic model but also enhances its capability to extract relevant features from complex vibration signals. Validated on a dataset simulating various faults in rotating machinery under different load conditions. The experimental results clearly demonstrate the superiority of CFF-Trans over existing models in terms of accuracy and F1 scores across different noise levels, demonstrating its promising practical value in this field.

   @inproceedings{liu2024integrated,
     title = {An {{Integrated Framework}} of {{Fourier Transform}} and {{Transformer}} for {{Rotating Machinery Fault Diagnosis}}},
     booktitle = {2024 {{IEEE International Conference}} on {{Prognostics}} and {{Health Management}} ({{ICPHM}})},
     author = {Liu, Xiaopeng and Liu, Jiale and Sun, Bingxiang and Zhang, Weige},
     year = {2024},
     month = jun,
     pages = {161--166},
     publisher = {IEEE},
     address = {Spokane, WA, USA},
     doi = {10.1109/ICPHM61352.2024.10627117},
     urldate = {2024-12-02},
     copyright = {https://doi.org/10.15223/policy-029},
     isbn = {9798350374476},
   }

2023

1. 

   QGFORMER: Quantum-Classical Hybrid Transformer Architecture for Gravitational Wave Detection

   Hu Jiaxiang, and Liu Jiale^*

   In 2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Dec 2023

   Abs DOI Bib

   Gravitational Wave (GW) detection is a pivotal field in astrophysics, gaining prominence for its role in deciphering cosmic phenomena. Traditional machine learning algorithms in GW detection, while effective, grapple with challenges in parallelism, scalability, efficiency in handling high-dimensional data and noise interference. To mitigate these limitations, this paper proposes the Quantum-Gravitational Transformer (QGFormer), a novel quantum-classical hybrid transformer for gravitational wave detection. The QGFormer consists of a vision transformer encoder and a quantum classifier following the autoencoder architecture, which first segments the original input signal into small patches and maps them to higher dimensions, and then inputs the higher-dimensional feature map into the quantum classifier for prediction. The excellent parallelism inherent in the multi-head self-attention mechanism and quantum computing makes QGFormer expected to achieve high computational efficiency and scalability on quantum computing devices. Additionally, the super-density coding and quantum state compression techniques that could be used in these devices help mitigate the effects of noise. In experiments on the Gravitational Spy dataset, which utilizes data collected by the LIGO telescope, our results show that QGFormer outperforms the comparison models in accuracy and F1-score, demonstrating the promising future of quantum machine learning approaches in this field.

   @inproceedings{jiaxiang2023qgformera,
     title = {{{QGFORMER}}: {{Quantum-Classical Hybrid Transformer Architecture}} for {{Gravitational Wave Detection}}},
     shorttitle = {{{QGFORMER}}},
     booktitle = {2023 20th {{International Computer Conference}} on {{Wavelet Active Media Technology}} and {{Information Processing}} ({{ICCWAMTIP}})},
     author = {Jiaxiang, Hu and Jiale, Liu},
     year = {2023},
     month = dec,
     pages = {1--5},
     publisher = {IEEE},
     address = {Chengdu, China},
     doi = {10.1109/ICCWAMTIP60502.2023.10387052},
     urldate = {2024-12-02},
     copyright = {https://doi.org/10.15223/policy-029},
     isbn = {9798350318982},
   }