From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine Chuan Li a,1 , Diego Cabrera a,b,∗,1 , Fernando Sancho c , Mariela Cerrada b , René-Vinicio Sánchez b , Edgar Estupinan d a National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China b GIDTEC, Universidad Politécnica Salesiana, Ecuador c Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Spain d Department of Mechanical Engineering, University of Tarapaca, Arica, Chile Keywords: Fault detection Severity discrimination One-class support vector machine 3D printer Bidirectional generative adversarial network abstract 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. 1. Introduction Fault detection and severity assessment are two fundamen- tal tasks of system health management. In this context, fault (anomaly) detection is the determination of whether a system is in normal working condition or some component contains a fault. Anomaly detection has been extensively studied in such fields as abnormal activity recognition [1], communication networks [2], malicious file detection [3], fault detection in power transmission lines and distribution systems [4], and industrial condition mon- itoring [5]. In the last field, although model-based approaches have been proposed [6], the current trend is to use data-driven techniques for reasons of flexibility and accuracy [7]. In data-driven approaches, a detection model is built using data collected from the monitored system. Informative features ✩ Acknowledgments: The work was sponsored in part by GIDTEC Research Group of Universidad Politécnica Salesiana, the National Natural Science Founda- tion of China (51775112), the MoST Science and Technology Partnership Program (KY201802006), the Key Project of the Chongqing Natural Science Foundation (cstc2019jcyj-zdxmX0013), and the CTBU Open Project (KFJJ2019059) ∗ Corresponding author at: GIDTEC, Universidad Politécnica Salesiana, Ecuador. E-mail address: dcabrera@ups.edu.ec (D. Cabrera). 1 The two authors contributed equally to this paper. are extracted from raw signals. A classifier is then created us- ing supervised, semi-supervised [8], unsupervised, or one-class learning [9]. Although supervised learning is superior to one- class learning in diverse applications [10,11], it only applies when labeled data in both normal and abnormal conditions can be obtained from the system, and it is usually expensive. Semi- supervised and unsupervised learning typically require fewer la- beled examples [12], but still need data in every system condition. Evolving approaches [13] address the issue of data availability in the training stage by recognizing new patterns in the testing stage and including them in the base of knowledge. These approaches require retraining of classification models after identifying a new cluster, which could be computationally expensive. One-class learning only uses data in normal condition to build the classifier, showing clear advantages in the absence of abnormal data and requiring less computation. The popular OCSVM [14] is a one-class learning model that has shown versatility and success in combination with feature extraction techniques for different applications [15,16]. It aims to build a decision hyperplane in a projected space of the input features. The hyperplane has samples in normal condition on one side, and abnormal ones on the other. The hyperplane is optimized using only data in normal condition.