Wisal Muhammad, Oh Ki-Yong
Departement of Mechanical Convergence Engineering, Hanyang University, 222, Wangsimni ro, Seongdong gu, Seoul 04763, Republic of Korea.
Sensors (Basel). 2023 Aug 12;23(16):7141. doi: 10.3390/s23167141.
Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem's escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.
转子不平衡是工业机器振动最常见的原因。不平衡会导致效率损失以及轴承和其他部件的使用寿命缩短，进而导致系统故障并带来重大安全风险。人们已经开发出许多复杂的分析技术和特定的分类算法来研究转子不平衡问题。这些分类算法虽然使用简单，但缺乏在处理少量和大量类别时都能高效使用的灵活性。因此，需要一种强大的多类预测算法来在运行时有效地对转子不平衡问题进行分类，并避免问题升级为故障。在这项工作中，开发了一种新的深度学习（DL）算法，用于检测旋转轴的不平衡，以进行二分类和多分类识别。该模型是通过利用ResNet的深度和有效性以及卷积神经网络（CNN）的特征提取特性开发的。新算法的性能优于ResNet和CNN。利用振动传感器收集的加速度计数据来训练该算法。对这些时间序列数据进行预处理，以提取重要的振动特征，如快速傅里叶变换（FFT）和短时傅里叶变换（STFT）。STFT作为一种特征丰富的特性，在我们的模型上表现更好。进行了两种类型的分析：（i）平衡与不平衡情况检测（两个输出类别）和（ii）不平衡程度检测（五个输出类别）。所开发的模型在二分类中的测试准确率为99.23%，在多级不平衡分类中的准确率为95.15%。结果表明，所提出的深度学习框架对于二分类和多分类问题都是稳健的。本研究提供了一个用于检测旋转机械轴不平衡的稳健框架，并可作为工业应用中的实时故障检测机制。
