GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Long Wen , Shaoquan Su , Xinyu Li , Weiping Ding , Ke Feng
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引用次数: 0

Abstract

Bearings play a pivotal role in various mechanical systems, and their health directly impacts the reliability and safety of these systems. Consequently, extensive research has been dedicated to the estimation of Bearing Remaining Useful Life (RUL) through the lens of information fusion theory. The absence of comprehensive life-cycle degradation data for bearings, a common challenge within the information fusion domain, can hinder the accuracy and reliability of RUL prediction models. A novel hybrid data and model approach named GRU-AE-Wiener has been developed to address this limitation. This approach combines the power of Gated Recurrent Unit (GRU) and Wiener process models within the information fusion framework. Firstly, a Loop Generative Adversarial Network (Loop-GAN) is introduced to synthesize pseudo data to enhance the quality of synthetic data. Next, a bidirectional GRU model is structurally integrated with the Wiener process. In this design, the GRU model is configured in an Auto-Encoder-like structure, with the Wiener process serving as the hidden layer within this Auto-Encoder. Importantly, both the GRU and Wiener processes are jointly optimized with the assistance of Loop-GAN, emphasizing the collaborative nature of information fusion in this approach. The effectiveness of the proposed GRU-AE-Wiener is validated using the PHM 2012 dataset and XJTU-SY dataset. Experimental results underscore its superior RUL predictive performance compared to other deep learning models, highlighting the practical application of information fusion principles in bearing health assessment.

GRU-AE-Wiener:用于轴承剩余使用寿命估算的生成对抗网络辅助混合门控递归单元与维纳模型
轴承在各种机械系统中起着举足轻重的作用,其健康状况直接影响着这些系统的可靠性和安全性。因此,大量研究致力于通过信息融合理论来估算轴承的剩余使用寿命(RUL)。缺乏全面的轴承生命周期降解数据是信息融合领域面临的共同挑战,这可能会妨碍 RUL 预测模型的准确性和可靠性。为解决这一问题,我们开发了一种名为 GRU-AE-Wiener 的新型混合数据和模型方法。这种方法在信息融合框架内结合了门控递归单元(GRU)和维纳过程模型的功能。首先,引入循环生成对抗网络(Loop-GAN)来合成伪数据,以提高合成数据的质量。其次,双向 GRU 模型与维纳过程进行了结构整合。在这一设计中,GRU 模型被配置为类似于自动编码器的结构,而维纳过程则作为该自动编码器的隐藏层。重要的是,在 Loop-GAN 的协助下,GRU 和 Wiener 流程都得到了联合优化,从而强调了这种方法中信息融合的协作性质。利用 PHM 2012 数据集和 XJTU-SY 数据集验证了所提出的 GRU-AE-Wiener 的有效性。实验结果表明,与其他深度学习模型相比,GRU-AE-Wiener 的 RUL 预测性能更优越,凸显了信息融合原理在轴承健康评估中的实际应用。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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