在本文中,对轮盘异常检测进行了研究。在铁路领域,由于乘客的安全问题和维护问题,对实际铁路车辆进行测试是一项挑战,因为这是一个公共行业。因此,利用动力学软件。接下来,进行STFT(短时傅里叶变换)以产生谱图图像。在铁路车辆的情况下,control,监测,通过TCMS进行通信,但是复杂的分析和数据处理是困难的,因为没有像GPU这样的设备。此外,有记忆限制。因此,在本文中,选择了相对轻量级的LeNet-5、ResNet-20和MobileNet-V3模型进行深度学习实验。此时,LeNet-5和MobileNet-V3模型从基本架构进行了修改。由于对铁路车辆进行了预防性维护,很难获得故障数据。因此,还进行了半监督学习。此时,引用了DeepOneClass分类论文。评估结果表明,改进的LeNet-5和MobileNet-V3模型获得了大约97%和96%的准确率,分别。在这一点上,LeNet-5模型的训练时间比MobileNet-V3模型快12分钟。此外,半监督学习结果显示,当考虑铁路维护环境时,准确率约为94%。总之,考虑到铁路车辆维修环境和设备规格,推断,相对简单和轻量级的LeNet-5模型可以在使用小图像时有效地利用。
In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and
MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and
MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and
MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the
MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images.