混合光虫群集优化与反复的深度学习用于工业物联网环境中的故障检测
G Anitha1, Hariprasath Manoharan2, Abirami Manoharan3
1Department of Electronics and Communication Engineering, RMD Engineering College, Chennai, Tamil Nadu, India. anirajkan@gmail.com.
Scientific reports
|November 7, 2025
概括
本研究介绍了一种新的混合灯群优化与反复深度学习 (HGSO-RDLFD) 用于工业设备的故障检测. HGSO-RDLFD方法达到99.7%的准确性,在工业物联网 (IIoT) 设置中明显优于现有模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业工程 工业工程 工业工程
背景情况:
- 在工业4.0中,故障检测 (FD) 对于降低成本和监测设备健康至关重要.
- 深度学习 (DL) 方法对于缺陷检测越来越受欢迎,但从丰富的传感器数据中提取特征是具有挑战性的.
- 工业物联网 (IIoT) 设备的在线故障检测至关重要.
研究的目的:
- 为IIoT环境中故障检测提出一种混合灯群优化与反复深度学习 (HGSO-RDLFD) 技术.
- 开发一种使用音频信号检测和分类故障的方法.
- 通过优化超参数调来提高故障分类性能.
主要方法:
- 使用Mel谱图技术从音频信号中提取特征.
- 使用循环深度学习模型 (HCNN-GRU) 进行故障分类.
- 使用Glowworm Swarm Optimization (GSO) 算法对深度学习模型进行超参数优化.
主要成果:
- 拟议的HGSO-RDLFD方法在故障检测数据集上实现了99.7%的准确性.
- 这种准确性比经典基线高5%,比先进的CNN-LSTM模型高2.4%.
- 该方法在各种措施中表现出比其他方法更好的表现.
结论:
- HGSO-RDLFD技术为IIoT环境中的故障检测提供了一个高度准确和有效的解决方案.
- 深度学习和群集优化的结合显著提高了分类性能.
- 这种方法解决了复杂工业系统中特征选择和超参数调节的挑战.
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