利用自适应噪声的完全集合经验模式分解,通过深度信念网络提取汽轮机轴承故障的深层信息

Q4 Engineering
Yashun Wang, Wei Xu
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引用次数: 0

摘要

为了提高汽轮机轴承故障诊断的准确性,我们引入了一种从振动信号中提取关键故障特征的新方法。考虑到轴承振动信号的复杂性、非线性和非稳态性,我们的策略包括利用多变量诊断算法进行敏感性分析。首先通过 TMI 系统收集故障轴承的振动数据。然后对这些数据进行带有自适应噪声的完全集合经验模式分解(CEEMDAN),从而整合自适应噪声,提取深度信息。随后,对分解后的信号进行时域和频域分析--后快速傅立叶变换(FFT)--形成诊断特征数据库的基础。为了简化数据分析并提高模型的计算效率,采用了极梯度提升(XGBoost)和互信息标准(MIC)相结合的降维方法。此外,还采用了深度信念网络(DBN)来开发旋转机械轴承的精确故障诊断模型。通过结合灵敏度分析,建立了诊断矩阵,为高精度故障识别提供了便利。通过对真实现场数据和基准数据库的测试,证实了该诊断算法的优越性,证明了它相对于其他特征选择技术的更强诊断能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Extract Deep Information of Bearing Fault in Steam Turbines via Deep Belief Network
Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.
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来源期刊
International Journal of High Speed Electronics and Systems
International Journal of High Speed Electronics and Systems Engineering-Electrical and Electronic Engineering
CiteScore
0.60
自引率
0.00%
发文量
22
期刊介绍: Launched in 1990, the International Journal of High Speed Electronics and Systems (IJHSES) has served graduate students and those in R&D, managerial and marketing positions by giving state-of-the-art data, and the latest research trends. Its main charter is to promote engineering education by advancing interdisciplinary science between electronics and systems and to explore high speed technology in photonics and electronics. IJHSES, a quarterly journal, continues to feature a broad coverage of topics relating to high speed or high performance devices, circuits and systems.
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