Fault　detection　in　3D　printers　is　crucial　for　safety　and　quality　assurance,　emphasizing　proactive　prediction　over　reactive　rectification　based　on　manufacturing　factors.　Presently,　most　detection　techniques　rely　on　shallow　models　with　limited　representational　capabilities,　necessitating　manual　feature　extraction　from　the　captured　signals.　This　manual　process　is　not　only　cumbersome　and　potentially　costly　but　often　requires　intricate　domain　specific　knowledge.　Additionally,　these　handcrafted　features　might　not　optimally　distinguish　between　normal　and　faulty　samples,　potentially　reducing　prediction　accuracy.　In　this　study,　we　introduce　an　end-to-end　approach　using　the　Deep　Support　Vector　Data　Description　model　for　fault　detection　in　3D　printers.　This　design　inherently　facilitates　automatic　feature　learning,　where　the　features　are　synergistically　optimized　for　fault　detection.　Our　experiments　leverage　magnetic　field　signals　for　fault　detection　in　3D　printers,　using　1D　convolutional　layers　to　discern　temporal　signal　patterns　and　wide　kernels　in　the　initial　layer　to　mitigate　high-frequency　noise.　Furthermore,　our　model　can　be　easily　adapted　to　integrate　multi-channel　signals　for　enhanced　accuracy.　Evaluations　on　real-world　data　from　a　delta　3D　printer　underscore　the　superiority　of　our　method　compared　to　existing　alternatives.