高效洁净机械制造实验室是 2009 年教育部批准立项建设的重点实验室。实验室秉承“突出特色、创新发展“的宗旨，以求真务实的态度认真做好各项工作。 实验室主任为黄传真教授，实验室副主任为刘战强教授和李方义教授。学术委员会主任为中国工程院院士卢秉恒教授。实验室固定人员中，有中国工程院院士艾兴教授，教育部 “ 长江学者 ” 特聘教授 2 人，国家杰出青年科学基金获得者 2 人， 新世纪百千万人才工程国家级人选 2人， 山东省 “ 泰山学者 ” 特聘教授4人， 教育部新世纪优秀人才 7 人，山东省有突出贡献中青年专家 4 人， 山东省杰出青年科学基金获得者 2 人。 博士生导师 24 人，教授 38 人，副教授 14 人，讲师 6 人，具有博士学位的比例达 91% ，现有在校博士生 80 人、硕士生 200 人、博士后 20 人。形成了一支基础较强、梯队健全、知识和年龄结构合理教师队伍和研究生科研队伍。 实验室所依托的 “ 机械制造及其自动化 ” 学科是国家重点学科、国家 “211 工程 ” 和 “985 工程 ” 重点建设学科。 实验室拥有价值百万元以上的先进精密加工设备、大型高精度电子测量仪器、各种实验台等，这些仪器设备在教学、科研及高层次人才培养等方面发挥着重要作用。 实验室非常重视科研工作，近五年共获得科研经费近亿元，在基础理论、应用技术、高新技术的研究与开发中，完成了包括国家自然科学基金、科技部、教育部、农业部、 “863” 计划、军工项目以及省科技厅等资助的科研项目 450 多项。 取得了多项重要研究成果，出版专著 10 部，在国内外著名学术刊物和国际学术会议上发表论 文共计 2000 多篇，被 SCI 、 EI 和 ISTP 收录约 450 篇，获得 全国优秀 博士论文 2 篇，获得全国优秀博士论文提名 2 篇，山东省优秀博士论文奖 2 篇，山东省优秀硕士论文奖 3 篇， 获得省部级奖励 50 多项。科技成果转化和产业化成效显著。 实验室以应用基础研究为主，面向国际前沿，面向国家重大需求， 围绕环保、高效加工制造理论方法及其应用，凝练高效洁净加工理论与方法、绿色设计与制造、节能机械装备设计、机电设备与控制四个主要研究方向，开展相关理论、方法及其应用等方面的研究。建立高效、洁净、节能、精密、复合创新设计和加工制造理论方法及其技术应用体系。注重以交叉研究、综合研究为特色，以本领域重要理论、方法和技术问题、科技前沿问题和国家重大工程需求为主要研究内容，以获取原始创新成果和自主知识产权为主要目标。经过建设，将 “ 高效洁净机械制造 ” 教育部重点实验室建成在学校创新人才培养体系和科技创新体系框架下的、国内领先的、在国际上有一定影响的高级专业人才培养基地、科学创新研究基地和高技术成果转化基地。经过 5 ～ 10 年建设，使 “ 高效洁净机械制造 ” 重点实验室达到国家重点实验室的水平。

Authors WANG Teng, received his B.Eng degree from Hefei University of Technology in 2016, and now is a master candidate at Shandong University. His research interest includes machine monitoring, prognostics and health management. LU Guoliang, received the B.E. and M.E. degrees from Shandong University, Jinan, China, in 2006 and 2009, respectively. He received his Dr. degree from the Graduate School of Information Science and Technology of Hokkaido University, Sapporo, Japan, in March 2013. He is currently an associate professor in Shandong University. His research interests mainly include signal processing, machine monitoring, machine vision and visual servo control.

LIU Jie, is currently an Associate Professor in the Dept LIU Jie, is currently an Associate Professor in the Dept. of Mechanical & Aerospace Engineering at Carleton University, Ottawa, Canada. He obtained his B.Eng. in Electronics and Precision Engineering from Tianjin University (China) in 1998, his M.Sc. in Control Engineering from Lakehead University (Canada) in 2005, and his Ph.D. in Mechanical Engineering from the University of Waterloo (Canada) in 2008. Prior to his graduate studies, he worked at Invensys Controls Inc as a Product Engineer and then Production Manager for three years. Before joining Carleton, he worked as an Postdoctoral Fellow in the Dept. of Mechanical Engineering at UC Berkeley for one and half years. Dr. Liu has been engaged in interdisciplinary research in the areas of prognostics and health management, intelligent mechatronic systems, and battery management systems for more than thirteen years. His research results have been disseminated through over 35 journal publications and 20 conference papers. He is a Steering Committee Member of Annual IEEE PHM Conferences, an Associate Editor of IEEE Transactions on Reliability, an IEEE Senior Member, and a registered Professional Engineer in Ontario, Canada. YAN Peng, received the B.Eng and M.Eng. degrees from Southeast University in 1997 and 1999 respectively, and the PhD degree from the Ohio State University, Columbus, OH, in 2003, all in Electrical Engineering. From 2004 to 2005, he worked as postdoctorate researcher at the Ohio State University. From 2005 to 2011, he held various industry positions including a Senior Staff Engineer at Seagate Technology at Shakopee MN and a Staff Scientist at United Technology Research Center (UTRC) at Easthartford CT. Currently he is a full professor at the school of Mechanical Engineering of Shandong University, China. His research interests include robust control and control of high precision mechatronics.

Other related papers of our research group in this direction [1] Lu, Guoliang, J. Liu, and P. Yan. "Graph-based structural change detection for rotating machinery monitoring." Mechanical Systems & Signal Processing 99(2018):73-82. [2] Guoliang Lu, et al. A novel framework of change-point detection for machine monitoring. Mechanical Systems and Signal Processing (Elsevier), 2017, 83: 533-548. [3] Teng Wang, Guoliang Lu, Peng Yan, Changhou Lu, Multidimensional Analysis of Time Series for Condition Monitoring of Rotating Machinery , International Conference on Sensing, Diagnostics, Prognostics, and Control, 2017.