Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
Sensors (Basel). 2021 Dec 18;21(24):8453. doi: 10.3390/s21248453.
Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.
机器的故障诊断和分类是工业领域状态监测的重要组成部分。然而，近年来，随着传感器技术和人工智能的发展，基于数据驱动的故障诊断和分类得到了更广泛的研究。数据驱动方法需要高质量的特征来获得良好的故障分类精度，但领域专业知识和大量的标记数据对于更好的特征也很重要。本文提出了一种基于深度自动编码器（DAE）和卷积神经网络（CNN）的轴承故障分类模型，该模型使用感应电动机（IM）的电机电流信号。电机电流信号可以很容易地从电机中采集，并且是非侵入式的。然而，从工业源采集的电流信号受到严重的噪声污染；因此，特征计算变得非常具有挑战性。DAE 用于用正常状态数据估计系统的非线性函数，然后获取残差信号。随后的 CNN 模型成功地对残差信号中的故障类型进行分类。我们提出的半监督方法实现了非常高的分类精度（超过 99%）。事实证明，包含 DAE 不仅可以显著提高准确性，而且在标记数据量少时也很有用。将实验结果与同一数据集上的一些现有工作进行比较，发现该组合方法的性能可与之相媲美。就分类准确性和其他评估参数而言，该综合方法可以被认为是使用电机电流信号进行轴承故障分类的有效方法。
