基于改进的卷积神经网络与贝叶斯优化的贝叶斯优化,研究电动潜水的故障诊断
1School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.
The Review of scientific instruments
|November 20, 2023
概括
本研究介绍了一种贝叶斯优化增强的一维卷积神经网络支持向量机 (BO-1DCNN-SVM) 用于电潜 (ESP) 故障诊断. 这种新的方法实现了96.64%的准确性,改进了传统方法.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电动潜水 (ESP) 的传统智能故障诊断严重依赖于手动功能提取.
- 标准卷积神经网络 (CNN) 需要广泛的参数和大数据集,这对实际的ESP故障诊断构成了挑战.
研究的目的:
- 为ESP开发一种先进的故障诊断算法,克服传统方法和标准CNN的局限性.
- 通过混合机器学习模型提高ESP故障分类的准确性和稳定性.
主要方法:
- 提出了一个贝叶斯优化-1DCNN-SVM (BO-1DCNN-SVM) 模型,将自动特征提取的1DCNN与用于分类的SVM集成在一起.
- SVM取代了CNN中的Softmax层,以改善有限数据的性能.
- 用贝叶斯优化 (BO) 来微调1DCNN-SVM模型的超参数.
主要成果:
- 拟议的BO-1DCNN-SVM算法展示了强大的自我提取功能能力,减少了对手工专业知识的依赖.
- 用SVM取代Softmax层,提高了ESP故障分类的准确性和概括能力,特别是使用更少的样本.
- 实验结果显示分类准确率为96.64%,比类似数据尺度的现有CNN方法提高了5%.
结论:
- 该BO-1DCNN-SVM模型为ESP故障诊断提供了一个高度准确和强大的解决方案.
- 这种方法有效地解决了手动特征提取和传统方法中有限数据的挑战.
- 贝叶斯优化的集成进一步提高了故障诊断模型的预测性能.
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