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国外文献 > 1.轴承 > 5. 监测与诊断 > 状态监测

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标 题： A Data-Driven Approach Based on Artificial Neural Networks for the Detection and Classification of Bearing Anomalies in Power Generation Plants
基于人工神经网络的数据驱动方法用于发电厂轴承异常的检测与分类
作 者： S. I. Senarathna
Dhammike Wimalarathne
L. A. U. Prasanshi
S. Kumarawadu
C. Wanigasekara
S. D. W. Senanayake
V. Logeeshan
关键词： Artificial neural networks  [人工神经网络]
Machine learning  [ 机器学习]
Condition monitoring  [ 状态监测]
SuperTML  [ 超级TML]
Tilted pad journal bearing  [ 倾斜垫片滚动轴承]
出版年： 2023
文集名： 2023 IEEE World AI IoT Congress (AIIoT 2023)
2023年IEEE世界人工智能物联网大会（AIIoT 2023）
会议名： 2023 IEEE World AI IoT Congress (AIIoT 2023)
2023年IEEE世界人工智能物联网大会（AIIoT 2023）
届 次：
摘 要： Power generation plants play a crucial role in modern societies, but they are vulnerable to different types of anomalies and faults that can have serious economic and environmental consequences. Bearing anomaly detection is an effective approach to recognize potential failures beforehand and avoid their occurrence. Recently, artificial neural networks (ANNs) have emerged as a promising approach for detecting anomalies in power generation plants, owing to their capability of acquiring intricate patterns and adapting to diverse operating circumstances. The presented study proposes a novel method to detect bearing anomalies in power generation plants using artificial neural networks. The approach aims to enhance the precision and dependability of anomaly detection by incorporating diverse features extracted from bearing data signals. Experimental validation was carried out on vibration data obtained from a real-world power generation plant to demonstrate the effectiveness of the proposed approach for detecting bearing anomalies. The results indicate that the proposed approach surpasses conventional methods, emphasizing the potential of ANNs for detecting vibration anomalies in power generation plants with higher accuracy and reliability.

发电厂在现代社会中发挥着至关重要的作用，但其运行容易受到各类异常和故障的影响，这些异常可能带来严重的经济和环境后果。轴承异常检测是一种有效的方法，可提前识别潜在的故障并避免其发生。近年来，人工神经网络（ANNs）因其能够提取复杂模式并适应多种运行条件，已成为发电厂异常检测的有前景的技术手段。本研究提出了一种基于人工神经网络的新型方法，用于检测发电厂中的轴承异常。该方法通过融合从轴承数据信号中提取的各种特征，旨在提高异常检测的精确性和可靠性。实验验证在真实发电厂采集的振动数据上进行，以证明所提方法在检测轴承异常方面的有效性。结果表明，所提出的方法优于传统方法，凸显了人工神经网络在发电厂检测振动异常方面的高精度和高可靠性潜力。
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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