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.

缺少故障状态数据会降低在新制造技术中进行故障检测或故障严重性判别的有监督学习的可行性。为了解决这个问题，仅使用健康状况数据就可以建立二进制判别模型，这是一类的学习方法。但是，这些模型尚未推断出严重程度歧视。本文建议将通常用于故障检测的OCSVM扩展到3D打印机故障严重性判别。首先，从一组正常信号中提取一组特征。通过调整内核和模型超参数可以获得优化的OCSVM模型。使用提出的性能评估方法评估所得模型的故障检测和故障严重性。3D打印机中基于皮带的故障的实验比较表明，到超平面的距离具有区分严重性级别的信息，并且其使用是可行的。与其他一些方法相比，提出的超参数优化技术改善了OCSVM的故障检测和严重性判别能力。