Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
ICT Convergence Safety Research Center, University of Ulsan, Ulsan 44610, Korea.
Sensors (Basel). 2022 Jun 28;22(13):4881. doi: 10.3390/s22134881.
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.
制造业系统中的机械问题诊断对于维护安全和最小化支出至关重要。在本研究中，提出了一种结合信号到图像编码技术和卷积神经网络（CNN）的智能故障分类模型，用于对轴承故障进行分类。首先，我们考虑运行条件将数据集分为四部分。然后，将原始信号分段为多个样本，并对每个样本应用Gramian 角场（GAF）算法生成二维（2-D）图像，这也将时间序列信号转换为极坐标。图像转换技术消除了手动特征提取的要求，并为每个故障特征创建了独特的模式。最后，使用生成的图像数据集来设计和训练一个 2 层深度的 CNN 模型，该模型可以从多个图像中提取高级特征以对故障条件进行分类。在对不同运行条件进行的所有实验中，所提出的方法表现出超过 99%的高分类精度，并证明 GAF 可以有效地从电流信号中保留故障特征。还应用了三种内置的 CNN 结构对图像进行分类，但具有两层结构的简单 CNN 在分类结果和计算时间方面证明是足够的。最后，将所提出的诊断框架的实验结果与一些最先进的诊断技术和已发表的工作进行比较，以验证其在不一致的工作条件下的优越性。结果验证了基于电机电流信号分析的所提出的方法在分类准确性和其他评估参数方面是一种很好的轴承故障分类方法。
