Department of Information & Communication Engineering, Graduate School, Dongguk University, Gyeongju 38066, Republic of Korea.
Department of Electronics, Information & Communication Engineering, Dongguk University, Gyeongju 38066, Republic of Korea.
Sensors (Basel). 2023 Mar 15;23(6):3157. doi: 10.3390/s23063157.
Bearing defects are a common problem in rotating machines and equipment that can lead to unexpected downtime, costly repairs, and even safety hazards. Diagnosing bearing defects is crucial for preventative maintenance, and deep learning models have shown promising results in this field. On the other hand, the high complexity of these models can lead to high computational and data processing costs, making their practical implementation challenging. Recent studies have focused on optimizing these models by reducing their size and complexity, but these methods often compromise classification performance. This paper proposes a new approach that reduces the dimensionality of input data and optimizes the model structure simultaneously. A much lower input data dimension than that of existing deep learning models was achieved by downsampling the vibration sensor signals used for bearing defect diagnosis and constructing spectrograms. This paper introduces a lite convolutional neural network (CNN) model with fixed feature map dimensions that achieve high classification accuracy with low-dimensional input data. The vibration sensor signals used for bearing defect diagnosis were first downsampled to reduce the dimensionality of the input data. Next, spectrograms were constructed using the signals of the minimum interval. Experiments were conducted using the vibration sensor signals from the Case Western Reserve University (CWRU) dataset. The experimental results show that the proposed method could be highly efficient in terms of computation while maintaining outstanding classification performance. The results show that the proposed method outperformed a state-of-the-art model for bearing defect diagnosis under different conditions. This approach is not limited to the field of bearing failure diagnosis, but could be applied potentially to other fields that require the analysis of high-dimensional time series data.
轴承缺陷是旋转机械和设备中常见的问题，可能导致意外停机、昂贵的维修费用，甚至安全隐患。诊断轴承缺陷对于预防性维护至关重要，深度学习模型在这一领域已经显示出了有前景的结果。另一方面，这些模型的高复杂性可能导致高计算和数据处理成本，使得它们的实际实施具有挑战性。最近的研究集中在通过减小模型的大小和复杂性来优化这些模型，但这些方法往往会牺牲分类性能。本文提出了一种新的方法，通过对用于轴承缺陷诊断的振动传感器信号进行下采样和构建频谱图，同时降低输入数据的维度和优化模型结构。与现有的深度学习模型相比，本文提出的方法使用较少的输入数据维度，实现了更高的分类精度。首先，通过对用于轴承缺陷诊断的振动传感器信号进行下采样来降低输入数据的维度。接下来，使用最小间隔的信号构建频谱图。实验使用了凯斯西储大学（CWRU）数据集的振动传感器信号。实验结果表明，该方法在保持出色分类性能的同时，在计算方面具有高效性。实验结果表明，该方法在不同条件下优于轴承缺陷诊断的最先进模型。这种方法不仅限于轴承故障诊断领域，还可能应用于需要分析高维时间序列数据的其他领域。
