Condition Monitoring And Control for Intelligent Manufacturing

by Wang, Lihui; Gao, Robert X.

• ISBN13: 9781846282683
• ISBN10: 1846282683
• Format: Hardcover
• Copyright: 2006-05-01
• Publisher: Springer Verlag

Summary

Manufacturing systems and processes are becoming increasingly complex, making more rational decision-making in process control a necessity. Better information gathering and analysis techniques are needed and condition-based monitoring is gaining attention from researchers worldwide as a framework that will enable these improvements.Condition Monitoring and Control for Intelligent Manufacturing brings together the world's authorities on condition-based monitoring to provide a broad treatment of the subject accessible to researchers and practitioners in manufacturing industry.The book presents a wide and comprehensive review of the key areas of research in machine condition monitoring and control, before focusing on an in-depth treatment of each important technique, from multi-domain signal processing for defect diagnosis to web-based information delivery for real-time control. Condition Monitoring and Control for Intelligent Manufacturing is a valuable resource for researchers in manufacturing and control engineering, as well as practising engineers in industries from automotive to packaging manufacturing.

Author Biography

Dr. Lihui Wang is a research officer of Integrated Manufacturing Technologies Institute at National Research Council of Canada (NRC). He received his Ph.D. and M.Sc. degrees from the Kobe University, Japan in 1993 and 1990, and his B.Sc. from China in 1982, respectively. Prior to joining NRC, he has worked for two years at the Kobe University and another two years at the Toyohashi University of Technology (both in Japan) as an Assistant Professor. His work on web-based monitoring and remote control has won the Best Paper Award at the FAIM 2002 international conference in Germany, and his research on intelligent shop floor has won the Best Poster Award at PRO-VE'03, the 4th IFIP Working Conference on Virtual Enterprises in Switzerland. In addition, he is also a five-time winner of the NRC Institute Awards on Excellence & Leadership in R&D and Global Reach. His research interests are focused on web-based real-time monitoring and control, distributed artificial intelligence, intelligent manufacturing systems, and distributed process planning. He published over 100 research papers in engineering journals and refereed conference proceedings, and has edited 3 conference proceedings on manufacturing research. Dr. Robert X. Gao is an Associate Professor of Mechanical Engineering at the University of Massachusetts Amherst, USA. He received his B.S. degree from China, and his M.S. and Ph.D. from the Technical University Berlin, Germany, in 1982, 1985, and 1991, respectively. Since starting his academic career in 1992, he has been conducting research in the general area of embedded sensors and sensor networks, "smart" electromechanical systems, wireless data communication, and signal processing for machine health monitoring, diagnosis, and prognosis. Dr. Gao has published over 100 refereed papers on journals and international conferences, and has one US patent and two pending patent applications on sensing.  He is an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, and served as the Guest Editor for the Special Issue on Sensors of the ASME Journal of Dynamic Systems, Measurement, and Control, published in June, 2004.  Condition-based Monitoring and Control for Intelligent Manufacturing has arisen from the Flexible Automation and Intelligent Manufacturing (FAIM 2004) conference, held in Toronto, Canada on July12-14 2004. Thirty papers have been selected out of 170 presented at the conference and the authors of these papers have been invited to submit extended updated versions of these papers in order to create a state of the art review of condition-based monitoring and control in manufacturing.

Table of Contents

List of Contributors	xvii
1 Monitoring and Control of Machining	1	(32)
A. Galip Ulsoy
1.1 Introduction	1	(5)
1.2 Machining Processes	6	(4)
1.3 Monitoring	10	(5)
1.3.1 Tool Failure	10	(2)
1.3.2 Tool Wear	12	(3)
1.4 Servo Control	15	(2)
1.5 Process Control	17	(6)
1.6 Supervisory Control	23	(2)
1.7 Concluding Remarks	25	(2)
References	27	(6)
2 Precision Manufacturing Process Monitoring with Acoustic Emission	33	(22)
D.E. Lee, Inkil Hwang, C.M.O. Valente, J.F.G. Oliveira and David A. Dornfeld
2.1 Introduction	33	(2)
2.2 Requirements for Sensor Technology at the Precision Scale	35	(2)
2.3 Sources of AE in Precision Manufacturing	37	(2)
2.4 AE-based Monitoring of Grinding Wheel Dressing	39	(4)
2.4.1 Fast AE RMS Analysis for Wheel Condition Monitoring	40	(1)
2.4.2 Grinding Wheel Topographical Mapping	41	(1)
2.4.3 Wheel Wear Mechanism	42	(1)
2.5 AE-based Monitoring of Face Milling	43	(1)
2.6 AE-based Monitoring of Chemical Mechanical Planarization	44	(4)
2.6.1 Precision Scribing of CMP-treated Wafers	45	(1)
2.6.2 AE-based Endpoint Detection for CMP	46	(2)
2.7 AE-based Monitoring of Ultraprecision Machining	48	(4)
2.7.1 Monitoring of Precision Scribing	48	(1)
2.7.2 Monitoring of Ultraprecision Turning of Single Crystal Copper	49	(3)
2.7.3 Monitoring of Ultraprecision Turning of Polycrystalline Copper	52	(1)
2.8 Conclusions	52	(1)
References	53	(2)
3 Tool Condition Monitoring in Machining	55	(28)
Mo A. Elbestawi, Mihaela Dumitrescu and Eu-Gene Ng
3.1 Introduction	55	(1)
3.2 Research Issues	56	(7)
3.2.1 Sensing Techniques	57	(4)
3.2.2 Feature Extraction Methods	61	(1)
3.2.3 Decision-making Methods	62	(1)
3.3 Neural Networks for Tool Condition Monitoring	63	(5)
3.3.1 Structure of MPC Fuzzy Neural Networks	64	(1)
3.3.2 Construction of MPC Fuzzy Neural Networks	65	(1)
3.3.3 Evaluation of MPC Fuzzy Neural Networks	66	(1)
3.3.4 Fuzzy Classification and Uncertainties in Tool Condition Monitoring	67	(1)
3.4 Case Studies	68	(10)
3.4.1 Experimental Tests on MPC Fuzzy Neural Networks for Tool Condition Monitoring	68	(7)
3.4.2 Online Monitoring Technique for the Detection of Drill Chipping	75	(3)
3.5 Conclusions	78	(2)
References	80	(3)
4 Monitoring Systems for Grinding Processes	83	(26)
Bernhard Karpuschewski and Ichiro Inasaki
4.1 Introduction to Grinding Processes	83	(1)
4.2 Need for Monitoring during Grinding	83	(1)
4.3 Monitoring of Process Quantities	84	(7)
4.4 Sensors for the Grinding Wheel	91	(3)
4.5 Workpiece Sensors	94	(5)
4.6 Sensors for Peripheral Systems	99	(3)
4.7 Adaptive Control Systems	102	(1)
4.8 Intelligent Systems for Abrasive Processes	103	(3)
References	106	(3)
5 Condition Monitoring of Rotary Machines	109	(28)
N. Tandon and A. Parey
5.1 Introduction	109	(2)
5.2 Performance Monitoring	111	(1)
5.3 Vibration Monitoring	111	(13)
5.3.1 Vibration Signal Processing	118	(6)
5.4 Shock Pulse Analysis (SPA)	124	(1)
5.5 Current Monitoring	125	(1)
5.6 Acoustic Emission Monitoring	126	(3)
5.7 Wear Debris and Lubricating Oil Analysis	129	(5)
5.7.1 Magnetic Plugs and Chip Detectors	129	(1)
5.7.2 Ferrography	129	(3)
5.7.3 Particle Counter	132	(1)
5.7.4 Spectrographic Oil Analysis (SOA)	133	(1)
5.7.5 Lubricating Oil Analysis	133	(1)
5.8 Thermography	134	(1)
5.9 Conclusions	135	(1)
References	135	(2)
6 Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings	137	(30)
Thomas R. Kurfess, Scott Billington and Steven Y. Liang
6.1 Introduction	137	(1)
6.2 Measurement Basics	138	(7)
6.3 Bearing Models	145	(2)
6.4 Diagnostics	147	(11)
6.4.1 Signal Analysis	147	(6)
6.4.2 Effects of Operating Conditions	153	(4)
6.4.3 Appropriate Use of Fast Fourier Transforms (FFTs)	157	(1)
6.4.4 Trending	157	(1)
6.5 Prognostics	158	(5)
6.6 Conclusions	163	(1)
References	163	(4)
7 Sensor Placement and Signal Processing for Bearing Condition Monitoring	167	(26)
Robert X. Gao, Ruqiang Yan, Shuangwen Sheng and Li Zhang
7.1 Introduction	167	(2)
7.2 Sensor Placement	169	(11)
7.2.1 Structural Attenuation	169	(2)
7.2.2 Simulation of Structural Effects	171	(2)
7.2.3 Experimental Evaluation	173	(2)
7.2.4 Sensor Location Ranking	175	(5)
7.3 Signal Processing Techniques	180	(8)
7.3.1 Frequency Domain Techniques	180	(2)
7.3.2 Time—frequency Techniques	182	(4)
7.3.3 Performance Comparison	186	(2)
7.4 Conclusions	188	(1)
References	189	(4)
8 Monitoring and Diagnosis of Sheet Metal Stamping Processes	193	(26)
R. Du
8.1 Introduction	193	(1)
8.2 A Brief Description of Sheet Metal Stamping Processes	194	(5)
8.3 Online Monitoring Based on the Tonnage Signal and Support Vector Regression	199	(10)
8.3.1 A Study of the Tonnage Signal	199	(1)
8.3.2 A Brief Introduction to Support Vector Regression (SVR)	200	(6)
8.3.3 Experiment Results	206	(1)
8.3.4 Remarks	207	(2)
8.4 Diagnosis Based on Infrared Imaging	209	(6)
8.4.1 A Study of Diagnosis Methods	209	(2)
8.4.2 Thermal Energy and Infrared Imaging	211	(4)
8.5 Conclusions	215	(2)
References	217	(2)
9 Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling	219	(26)
Yimin Zhan and Viliam Makis
9.1 Introduction	219	(2)
9.2 Modeling	221	(10)
9.2.1 Noise-adaptive Kalman Filter-based Model	221	(3)
9.2.2 Bispectral Feature Energy	224	(2)
9.2.3 AR Model Residual-based State Parameter	226	(2)
9.2.4 Improved AR Model Residual-based State Parameter	228	(3)
9.3 Experimental Set-up	231	(2)
9.4 Performance Analysis of BFE	233	(2)
9.5 Performance Analysis of MRP	235	(4)
9.6 Performance Analysis of IMRP	239	(3)
9.7 Conclusions	242	(1)
References	243	(2)
10 Signal Processing in Manufacturing Monitoring	245	(22)
C. James Li
10.1 Introduction	245	(1)
10.2 Types of Signatures	246	(1)
10.3 Signal Processing	247	(14)
10.3.1 Time Domain Methods	247	(4)
10.3.2 Frequency Domain Methods	251	(5)
10.3.3 Time–frequency Methods	256	(4)
10.3.4 Model-based Methods	260	(1)
10.4 Decision-making Strategy	261	(3)
10.4.1 Simple Thresholds	261	(1)
10.4.2 Statistical Process Control (SPC)	262	(1)
10.4.3 Time/Position-dependent Thresholds	262	(1)
10.4.4 Part Signature	262	(1)
10.4.5 Waveform Recognition	263	(1)
10.4.6 Pattern Recognition	263	(1)
10.4.7 Severity Estimator	263	(1)
10.5 Conclusions	264	(1)
References	264	(3)
11 Autonomous Active-sensor Networks for High-accuracy Monitoring in Manufacturing	267	(22)
Ardevan Bakhtari and Beno Benhabib
11.1 Sensor Networks	267	(5)
11.1.1 Sensor Fusion	268	(1)
11.1.2 Sensor Selection	268	(1)
11.1.3 Sensor Modeling	269	(1)
11.1.4 An Example of a Multi-sensor Network	270	(2)
11.2 Active Sensors	272	(4)
11.2.1 Active-sensor Networks for Surveillance of Moving Objects in Static Environments	272	(3)
11.2.2 Online Sensor Planning for Surveillance of Dynamic Environments	275	(1)
11.3 Agent-based Approach to Online Sensor Planning	276	(6)
11.3.1 Agents	276	(1)
11.3.2 Advantages and Drawbacks of Multi-agent Systems	277	(1)
11.3.3 Examples of Agent-based Sensor-planning Systems	277	(5)
11.4 An Active-sensor Network Example for Object Localization in a Multi-object Environment	282	(4)
11.4.1 Experimental Set-up	282	(1)
11.4.2 Experiments	283	(3)
References	286	(3)
12 Remote Monitoring and Control in a Distributed Manufacturing Environment	289	(26)
Lihui Wang, Weiming Shen, Peter Orban and Sherman Lang
12.1 Introduction	289	(1)
12.2 WISE-SHOPFLOOR Concept	290	(2)
12.3 Architecture Design	292	(3)
12.4 Data Collection and Distribution	295	(3)
12.4.1 Information Flow	295	(1)
12.4.2 Applet–Servlet Communication	295	(1)
12.4.3 Sensor Signal Collection and Distribution	296	(1)
12.4.4 Virtual Control versus Real Control	297	(1)
12.5 Shop Floor Security	298	(1)
12.6 Case Study 1: Remote Robot Control	299	(8)
12.6.1 Constrained Kinematic Model	300	(2)
12.6.2 Inverse Kinematic Model	302	(1)
12.6.3 Java 3D Scene-graph Model	303	(2)
12.6.4 Remote Tripod Manipulation	305	(2)
12.7 Case Study 2: Remote CNC Machining	307	(4)
12.7.1 Test Bed Configuration	307	(1)
12.7.2 Java 3D Visualization	308	(1)
12.7.3 Data Communication	309	(1)
12.7.4 Remote Machine Control	309	(2)
12.8 Toward Condition-based Monitoring	311	(1)
12.9 Conclusions	312	(1)
References	313	(2)
13 An Intelligent Nanofabrication Probe for Surface Displacement/Profile Measurement	315	(32)
Wei Gao
13.1 Introduction	315	(2)
13.2 Design of the Nanofabrication Probe	317	(10)
13.2.1 Concept of the Probe	317	(3)
13.2.2 Design of the Probe	320	(7)
13.3 Evaluation of the Nanofabrication Probe	327	(8)
13.3.1 Evaluation of FTC Performance of the Probe	327	(3)
13.3.2 Evaluation of Force Detection by the Probe	330	(3)
13.3.3 Evaluation of Displacement Detection by the Probe	333	(2)
13.4 Nanofabrication and Workpiece Surface Profile Measurement Using the Probe	335	(9)
13.5 Conclusions	344	(1)
References	344	(3)
14 Smart Transducer Interface Standards for Condition Monitoring and Control of Machines	347	(26)
Kang B. Lee
14.1 Introduction	347	(2)
14.2 IEEE 1451 Smart Transducer Interface Standards	349	(10)
14.2.1 IEEE 1451.0 – Common Functions and Commands	350	(1)
14.2.2 IEEE 1451.1 – Networked Smart Transducer Model	351	(2)
14.2.3 IEEE 1451.2 – Transducer-to-Microprocessor Communication Interface	353	(2)
14.2.4 IEEE 1451.3 – Distributed Multi-drop Systems for Interfacing Smart Transducers	355	(1)
14.2.5 IEEE 1451.4 – Mixed-mode Transducer Interface	356	(2)
14.2.6 IEEE P1451.5 – Wireless Transducer Interface	358	(1)
14.3 Distributed Control Architecture	359	(7)
14.3.1 Networked Smart Sensor Standards	360	(1)
14.3.2 Network Communications using Ethernet	360	(1)
14.3.3 Distributed Measurement and Control Model	361	(2)
14.3.4 Web-based Access to Control Network	363	(1)
14.3.5 Internet-based Condition Monitoring	364	(2)
14.4 Networked Sensor Application – Machine Tool Condition Monitoring	366	(4)
14.4.1 Design Approach	369	(1)
14.4.2 System Implementation	369	(1)
14.4.3 Hardware System Layout	369	(1)
14.5 Conclusions	370	(1)
References	371	(2)
15 Rocket Testing and Integrated System Health Management	373	(20)
Fernando Figueroa and John Schmalzel
15.1 Introduction	373	(2)
15.2 Background	375	(3)
15.3 ISHM for Rocket Test	378	(6)
15.3.1 Implementation Strategy	378	(1)
15.3.2 DIaK Architecture	378	(3)
15.3.3 Object Framework	381	(3)
15.4 ISHM Implementation	384	(4)
15.4.1 Overall System	384	(1)
15.4.2 Intelligent Sensors	385	(3)
15.4.3 Process Models	388	(1)
15.5 Implementation/Validation: Rocket Engine Test Stand	388	(1)
15.6 Conclusions and Future Work	389	(1)
References	390	(3)
Index	393