Li Chuan, Cabrera Diego, Sancho Fernando, Cerrada Mariela, Sánchez René-Vinicio, Estupinan Edgar
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China.
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China; GIDTEC, Universidad Politécnica Salesiana, Ecuador.
ISA Trans. 2021 Apr;110:357-367. doi: 10.1016/j.isatra.2020.10.036. Epub 2020 Oct 15.
The lack of faulty condition data reduces the feasibility of supervised learning for fault detection or fault severity discrimination in new manufacturing technologies. To deal with this issue, one-class learning arises for building binary discriminative models using only healthy condition data. However, these models have not been extrapolated to severity discrimination. This paper proposes to extend OCSVM, which is typically used for fault detection, to 3D printer fault severity discrimination. First, a set of features is extracted from a set of normal signals. An optimized OCSVM model is obtained by tuning the kernel and model hyperparameters. The resulting models are evaluated for fault detection and fault severity discrimination using a proposed performance evaluation approach. Experimental comparisons for belt-based faults in 3D printers show that the distance to the hyperplane has the information to discriminate the severity level, and its use is feasible. The proposed hyperparameter optimization technique improves the OCSVM for fault detection and severity discrimination compared to some other methods.
缺乏故障状态数据降低了在新制造技术中进行故障检测或故障严重程度判别的监督学习的可行性。为了解决这个问题，出现了一类学习方法，即仅使用健康状态数据来构建二元判别模型。然而，这些模型尚未扩展到严重程度判别。本文提出将通常用于故障检测的支持向量数据描述（OCSVM）扩展到3D打印机故障严重程度判别。首先，从一组正常信号中提取一组特征。通过调整核函数和模型超参数获得优化的OCSVM模型。使用提出的性能评估方法对所得模型进行故障检测和故障严重程度判别的评估。对3D打印机中基于皮带的故障进行的实验比较表明，到超平面的距离具有区分严重程度级别的信息，并且其使用是可行的。与其他一些方法相比，所提出的超参数优化技术改进了用于故障检测和严重程度判别的OCSVM。
