<br>使用自适应 Superlet 进行早期智能轴承故障检测,IEEE Sensors Journal

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使用自适应 Superlet 进行早期智能轴承故障检测
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2-22-2023 , DOI: 10.1109/jsen.2023.3245186
Sukanya Mitra ₁ , Chiranjib Koley ₁

Affiliation

轴承故障会中断三相感应电机（3Φ3\Phi IM）的运行并扰乱工厂生产。振动信号分析、特征提取和人工智能的结合可以增强轴承故障检测的能力。然而，由于恶劣的环境条件和巨大的计算能力，在工业中实施这些技术具有挑战性。针对这个问题，本文提出了一种通过小波的超分辨率特性和二维卷积神经网络（2-D-CNN）考虑振动信号时频内容的融合方法。首先，考虑使用变频驱动 (VFD) 驱动的感应电机 (IM) 进行基于实验室的实时过程，该电机也暴露在环境振动中。在目前的工作中，压电传感器用于收集 IM 在运行过程中的振动。然后，收集到的时间序列信号通过数据采集系统（DAS）存储在计算机中。此外，使用连续小波变换（CWT）、斯托克韦尔变换（ST）和自适应超变换（ASLT）在小窗口中对信号进行分割以提取各自的特征图像。此后，图像分辨率被优化调整大小并馈送到 2-D-CNN 模型以进行轴承的多标签分类。据观察，ASLT 与优化设计的 32×32×32\times 32\times 3 分辨率的 2-D-CNN (ASLT-2-D-CNN) 相结合优于其他两种融合方法，因为它报告了高精度和减少运行时间（训练和测试）以及内存使用。 ASLT-2-D-CNN 的稳健性还通过两个开源轴承数据集进行了验证。因此，结果表明，所提出的轴承故障检测方法可以在现场部署，以确保IM的安全，从而降低行业的维护成本。

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Early and Intelligent Bearing Fault Detection Using Adaptive Superlets

Faults in bearings interrupt the operation of three-phase induction motors ( 3Φ3\Phi IMs) and disturb factory production. Vibration signal analysis, feature extraction, and incorporation of artificial intelligence can enhance the ability of fault detection in bearings. However, implementing these techniques is challenging in industries due to harsh environmental conditions and massive computational power. Prioritizing the issue, this article presents a fusion method considering the time–frequency contents of vibration signals via the superresolution property of wavelets and a two-dimensional convolutional neural network (2-D-CNN). First, a laboratory-based real-time process is considered with variable frequency drive (VFD)-driven induction motor (IM), which is also exposed to environmental vibration. In the present work, a piezoelectric sensor is used to collect the vibration of IM during operation. Then, the collected time-series signals are stored in a computer through a data acquisition system (DAS). Furthermore, signals are segmented in small windows to extract respective feature images using continuous wavelet transform (CWT), Stockwell transform (ST), and adaptive superlet transform (ASLT). Thereafter, the image resolution is optimally resized and fed to a 2-D-CNN model for multilabel classification of the bearings. It is observed that ASLT in combination with an optimally designed 2-D-CNN (ASLT-2-D-CNN) with 32×32×32\times 32\times 3 resolution outperforms the other two fusion methods as it reports high accuracy and reduces runtime (training and testing) along with memory usage. The robustness of ASLT-2-D-CNN is also validated with two open-source bearing datasets. Thus, the result indicates that the proposed method for bearing fault detection can be deployed in the field to make IMs safe, which reduces the maintenance cost of the industry.

更新日期：2024-08-28

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