利用旋转频谱和规模感知鲁棒网络推进航空发动机轴承故障诊断

IF 2.1 3区 工程技术 Q2 ENGINEERING, AEROSPACE Aerospace Pub Date : 2024-07-26 DOI:10.3390/aerospace11080613
Jin Li, Zhengbing Yang, Xiang Zhou, Chenchen Song, Yafeng Wu
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引用次数: 0

摘要

精确监测轴承对于及时发现旋转机械系统中的问题至关重要。然而,由于结构的高度复杂性,振动信号的传输路径错综复杂,给航空发动机轴承故障诊断带来了巨大挑战。因此,本研究提出了一种以旋转频谱为基础的规模感知鲁棒性(RSSR)神经网络,以解决错综复杂的故障特征和严重的噪声干扰问题。RSSR 算法融合了规模感知特征提取模块、非激活卷积网络和创新的通道注意模块,在简单性和有效性之间取得了平衡。我们通过比较传统的 CNN、变换器及其各自的变体进行了全面分析。我们的策略不仅提高了诊断精度,还明智地调节了网络的参数数量和计算强度,减轻了过度拟合的倾向。为了评估我们提出的网络的功效,我们使用两个复杂的公开数据集进行了严格的测试,并引入了额外的人工噪音来模拟具有挑战性的操作环境。在无噪声数据集上,与目前的主流方法相比,我们的技术在航空发动机数据集上的准确率提高了 5.11%。即使在最大噪声条件下,与其他当代方法相比,我们的平均准确率也提高了 4.49%。结果表明,我们的方法在诊断性能和泛化能力方面优于其他技术。
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Advancing the Diagnosis of Aero-Engine Bearing Faults with Rotational Spectrum and Scale-Aware Robust Network
The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a Rotational-Spectrum-informed Scale-aware Robustness (RSSR) neural network is proposed in this study to address intricate fault characteristics and significant noise interference. The RSSR algorithm amalgamates a scale-aware feature extraction block, a non-activation convolutional network, and an innovative channel attention block, striking a balance between simplicity and efficacy. We provide a comprehensive analysis by comparing traditional CNNs, transformers, and their respective variants. Our strategy not only elevates diagnostic precision but also judiciously moderates the network’s parameter count and computational intensity, mitigating the propensity for overfitting. To assess the efficacy of our proposed network, we performed rigorous testing using two complex, publicly available datasets, with additional artificial noise introductions to simulate challenging operational environments. On the noise-free dataset, our technique increased the accuracy by 5.11% on the aero-engine dataset compared with the current mainstream methods. Even under maximal noise conditions, it enhances the average accuracy by 4.49% compared with other contemporary approaches. The results demonstrate that our approach outperforms other techniques in terms of diagnostic performance and generalization ability.
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来源期刊
Aerospace
Aerospace ENGINEERING, AEROSPACE-
CiteScore
3.40
自引率
23.10%
发文量
661
审稿时长
6 weeks
期刊介绍: Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.
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