Abstract Details
75 / 2025-05-14 19:02:28
A CNN-BiLSTM-Based Evaluation Model for Steady-State Bearing Fault Diagnosis Method
CNN-BiLSTM; Rolling Bearings; Envelope Analysis; Fault Feature Coefficient; Evaluation Model.
Final Paper
The examination of rolling bearing fault characteristics is crucial for guaranteeing the state of rotating machinery. This paper presents an evaluation model based on CNN-BiLSTM to assess the effectiveness of fault frequency diagnostic methods under steady-state conditions. The model focuses on envelope spectrum analysis, It employs fault feature coefficients (FFC) to label data, The CNN extracts time-frequency features, while the BiLSTM learns sequence features, enabling a binary classification of the diagnosis success. The experimental outcomes means that the model have a high accuracy in effectiveness evaluation.Comparisons show CNN-BiLSTM outperforms CNN-LSTM in precision, recall and F1-score. despite longer training times. This research offers a reliable approach for assessing fault diagnosis methods, facilitating the choice of suitable diagnostic techniques for analyzing bearing vibration signals.
Important Date
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Conference Date
Aug 01
2025
toAug 04
2025
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Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
