{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-and-its-applications-to-machine","title":"Deep Learning and Its Applications to Machine Health Monitoring: A Survey","arxiv_id":"1612.07640","date":"2016-12-16","proceeding":null,"authors":["Rui Zhao","Ruqiang Yan","Zhenghua Chen","Kezhi Mao","Peng Wang","Robert X. Gao"],"abstract":"Since 2006, deep learning (DL) has become a rapidly growing research\ndirection, redefining state-of-the-art performances in a wide range of areas\nsuch as object recognition, image segmentation, speech recognition and machine\ntranslation. In modern manufacturing systems, data-driven machine health\nmonitoring is gaining in popularity due to the widespread deployment of\nlow-cost sensors and their connection to the Internet. Meanwhile, deep learning\nprovides useful tools for processing and analyzing these big machinery data.\nThe main purpose of this paper is to review and summarize the emerging research\nwork of deep learning on machine health monitoring. After the brief\nintroduction of deep learning techniques, the applications of deep learning in\nmachine health monitoring systems are reviewed mainly from the following\naspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and\nits variants including Deep Belief Network (DBN) and Deep Boltzmann Machines\n(DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).\nFinally, some new trends of DL-based machine health monitoring methods are\ndiscussed.","url_abs":"http://arxiv.org/abs/1612.07640v1","url_pdf":"http://arxiv.org/pdf/1612.07640v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-and-its-applications-to-machine","repo_url":"https://github.com/lifesailor/data-driven-predictive-maintenance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"deep-belief-network","method_name":"Deep Belief Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}