Li Yuqing, Zhu Linghui, Xu Minqiang, Jia Yunzhao
Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2024 Nov 21;24(23):7435. doi: 10.3390/s24237435.
A harmonic reducer is an important component of industrial robots. In practical applications, it is difficult to obtain enough anomaly data from error cases for the supervised training of models. Whether the information contained in regular features is sensitive to anomaly detection is unknown. In this paper, we propose an anomaly detection frame for a harmonic reducer with only healthy data. We considered an auto-encoder trained using only healthy features, such as feature mapping, in which the difference between the output and the input constitutes a new high-dimensional feature space that retained information relevant only to anomalies. Compared to the original feature space, this space was more sensitive to abnormal data. The mapped features were then fed into the OCSVM to preserve the feature details of the abnormal information. The effectiveness of this method was validated by multiple sets of data collecting from harmonic reducers. Three different residual calculations and four different AE models were used, showing that the method outperforms an AE or an OCSVM alone. It is also verified that the method outperforms other typical anomaly detection methods.
谐波减速器是工业机器人的重要组成部分。在实际应用中，很难从错误案例中获取足够的异常数据用于模型的监督训练。常规特征中包含的信息对异常检测是否敏感尚不清楚。在本文中，我们提出了一种仅使用健康数据的谐波减速器异常检测框架。我们考虑了一种仅使用健康特征（如特征映射）训练的自动编码器，其中输出与输入之间的差异构成了一个新的高维特征空间，该空间仅保留与异常相关的信息。与原始特征空间相比，该空间对异常数据更敏感。然后将映射后的特征输入到OCSVM中，以保留异常信息的特征细节。通过从谐波减速器收集的多组数据验证了该方法的有效性。使用了三种不同的残差计算和四种不同的AE模型，结果表明该方法优于单独的AE或OCSVM。还验证了该方法优于其他典型的异常检测方法。
