国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护
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| 标 题： |
A hybrid deep learning-based approach for rolling bearing fault prognostics
基于混合深度学习的滚动轴承故障预测方法 |
| 作 者： |
Domicio Neto
Lorena Petrella
Jorge Henriques
Paulo Gil
Alberto Cardoso |
| 关键词： |
Predictive maintenance [预测性维护]
Circular manufacturing [ 循环制造]
Remaining useful life [ 剩余使用寿命]
Rolling bearings [ 滚动轴承]
Deep learning [ 深度学习] |
| 出版年： |
2023 |
| 文集名： |
22nd IFAC World Congress
第22届国际应用数学联合会世界大会 |
| 会议名： |
22nd IFAC World Congress
第22届国际应用数学联合会世界大会 |
| 届 次： |
22nd |
| 摘 要： |
Predictive Maintenance (PdM) has the potential to revolutionize the industry by providing advanced techniques to assess the condition of an industrial system and yield key information that can help optimize maintenance planning and prevent unexpected faults and breakdowns. Nevertheless, PdM is far from being universally applied and it is still the subject of increasing research. Thus, developing new approaches has great relevance to help PdM become a practical reality for the industry. PdM can also bring benefits in terms of sustainability, by reducing human and material resources waste, which is one of the main objectives of Circular Manufacturing initiatives. In this context, rolling bearings are one of the most studied components, as most industrial systems with rotating mechanisms contain bearings, which are prone to a number of faults caused by natural and unnatural wear. In this work, an hybrid Deep Learning (DL) approach is proposed, combining a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network to predict Remaining Useful Life (RUL) using rolling bearing vibration data preprocessed with the Short-Time Fourier Transform (STFT). This model was trained and validated using the PRONOSTIA public dataset, which is a popular benchmark for rolling bearing prognostics. The obtained results are satisfactory, providing RUL estimates close to the true values in most test cases, proving the competitiveness of the approach and its potential.
预测性维护（PdM）通过提供先进的技术来评估工业系统的状态，从而获取关键信息，有助于优化维护计划，并预防意外故障和停机。然而，PdM尚未被普遍应用，仍处于不断研究之中。因此，开发新的方法对于推动PdM在工业中的实际应用具有重要意义。此外，PdM还能在可持续性方面带来益处，通过减少人力和资源浪费，这正是循环制造倡议的主要目标之一。在此背景下，滚动轴承是研究最为广泛的部件之一，因为大多数带有旋转机构的工业系统都包含轴承，而轴承容易因自然或人为磨损导致多种故障。本研究提出了一种混合深度学习（DL）方法，将卷积神经网络（CNN）与门控循环单元（GRU）网络相结合，利用经过短时傅里叶变换（STFT）预处理的滚动轴承振动数据，预测其剩余使用寿命（RUL）。该模型使用PRONOSTIA公共数据集进行训练和验证，该数据集是滚动轴承预测的常用基准。所得结果令人满意，在大多数测试案例中，寿命剩余（RUL）估计值接近真实值，证明了该方法的竞争力及其应用潜力。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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