基于深度可分离卷积和 Bi-LSTM 的热轧配电箱故障智能诊断系统

IF 2.8 4区 工程技术 Q2 ENGINEERING, CHEMICAL Processes Pub Date : 2024-09-17 DOI:10.3390/pr12091999
Yonglin Guo, Di Zhou, Huimin Chen, Xiaoli Yue, Yuyu Cheng
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引用次数: 0

摘要

精轧机是热轧工艺中的关键环节,影响着最终产品的质量,甚至经济效益。精轧机的配电箱在电力传输和分配中起着至关重要的作用。然而,恶劣的运行条件经常会导致配电箱损坏甚至故障。为了及时诊断配电箱故障,本文构建了一个故障诊断网络模型。该模型结合了深度可分离卷积和双 LSTM。深度可分离卷积和 Bi-LSTM 可以从信号中提取空间和时间特征。这种结构可以实现全面的特征提取,并充分利用信号信息。为了验证模型的诊断能力,收集并使用了五种数据:齿面点蚀、平头套筒齿裂、齿轮表面裂纹、齿轮齿面剥落和正常情况。该模型的精度达到 97.46%,并采用了轻量化设计,提高了计算效率。此外,该模型在三种噪声条件下均保持了约 90% 的精度。基于这些结果,所提出的模型可以有效诊断配电箱故障,减少工程停机时间。
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Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi−LSTM
The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi−LSTM. Depthwise separable convolution and Bi−LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat−headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering.
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来源期刊
Processes
Processes Chemical Engineering-Bioengineering
CiteScore
5.10
自引率
11.40%
发文量
2239
审稿时长
14.11 days
期刊介绍: Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.
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