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AD-STLN: A Neural-Symbolic Framework with Dictionary Learning for Transparent Bearing Fault Classification
ID：60 View Protection：ATTENDEE Updated Time：2025-11-10 11:31:24 Hits：181 Oral Presentation

Start Time：2025-11-23 08:50（Asia/Shanghai）

Duration：20min

Session：S2 Parallel Session 2 » S2-2Parallel Session 2-23 AM

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Abstract
This paper tackles the interpretability challenge in rolling bearing fault diagnosis by introducing the Adaptive Dictionary-based Sparse Temporal Logic Network (AD-STLN). Unlike traditional approaches using fixed wavelet kernels, AD-STLN employs data-driven dictionary learning to adaptively extract fault-specific features. The framework comprises three key modules: an adaptive dictionary convolution layer for customized feature extraction, a sparse attention transformer encoder for highlighting salient signal regions, and a temporal logic reasoning layer that constructs weighted Signal Temporal Logic (wSTL) expressions to explain diagnostic decisions. Experiments on the CWRU dataset show that AD-STLN delivers competitive accuracy while offering clear, logic-based interpretability, supporting more transparent and trustworthy fault diagnosis.
 
Keywords
Adaptive-dictionary-based Sparse Temporal Logic Network (AD-STLN）,signal temporal logic (STL),dictionary learning,interpretable fault diagnosis
Speaker
Peixi Yang
Student South China University of Technology

Submission Author
Peixi Yang South China University of Technology
Gang Chen South China University of Technology
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Important Date

  • Conference Date

    Nov 21

    2025

    to

    Nov 23

    2025

  • Oct 20 2025

    Draft paper submission deadline

  • Dec 08 2025

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
Organized By
South China University of Technology
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