Automatic Supervision of Machine Tools

Mechanical failures are by far the most significant non-controllable cause of lost production time in manufacturing systems. De Barr [1] estimates that these failures in conventional machine tools account for four times as much down-time as electrical failures. Kegg [2], Milacic and Majstorovic [3] suggest that in NC machines this ratio is at least 7 to 1. Detailed analysis of the component failure distribution in manufacturing systems can be found in [2–5]. According to these studies, the tool and workpiece changing systems may account for as much as 40% of down-time. Other basic components of machine tools, such as spindles, slides, bearings, gears and lubrication, are responsible for slightly over 10% of the lost production time. A thorough investigation of 44 lathes reported in [5] breaks down all components of these machines into 25 classes, including: control unit (NC, CNC, PLC), electric motor, bearing and spindle. For each class the frequency of failures and the average down-time per failure are estimated. According to these results, malfunctions of bearings and spindles alone account for about the same down-time as the control unit failures.

[1]  Ralph Deutsch,et al.  System Analysis Techniques , 1969 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  P. Eykhoff.  System Identification Parameter and State Estimation , 1974 .

[4]  Jacques Peters,et al.  Dynamic analysis of machine tools using complex modal method , 1976 .

[5]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[6]  Ilya B. Gertsbakh,et al.  Models of Preventive Maintenance , 1977 .

[7]  C. Baskiotis,et al.  Parameter identification and discriminant analysis for jet engine machanical state diagnosis , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[8]  Shozo Takata,et al.  Monitoring and Diagnosis System of Machine Tools , 1979 .

[9]  R. A. Collacott,et al.  Vibration monitoring and diagnosis: Techniques for cost-effective plant maintenance , 1979 .

[10]  Rolf Isermann.  Digital Control Systems , 1981 .

[11]  John S. Mitchell,et al.  An introduction to machinery analysis and monitoring , 1981 .

[12]  S. M. Wu,et al.  Compensatory Control of Roundness Error in Cylindrical Chuck Grinding , 1982 .

[13]  R. H. Russell,et al.  Signature Analysis Extended: A System Approach to Machinery Dynamics , 1982 .

[14]  J. Tlusty,et al.  A Critical Review of Sensors for Unmanned Machining , 1983 .

[15]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[16]  Hong Zan Bin,et al.  Microprocessor-Based Compensation of Leadscrew Drive Kinematic Errors by a Forecasting Technique , 1984 .

[17]  Fumihiko Kimura,et al.  IDENTIFICATION OF MACHINE AND MACHINING STATES BY USE OF PATTERN RECOGNITION TECHNIQUE. , 1984 .

[18]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[19]  Richard L. Kegg,et al.  One-Line Machine and Process Diagnostics , 1984 .

[20]  Manfred Weck,et al.  Handbook of machine tools , 1984 .

[21]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[22]  R. Offereins.  Book review: Digital control system analysis and design , 1985 .

[23]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[24]  Toshio Sata,et al.  Model referenced monitoring and diagnosis - application to the manufacturing system , 1986 .

[25]  M. Miki,et al.  A Sound Monitoring System for Fault Detection of Machine and Machining States , 1986 .

[26]  C. Picardi,et al.  Fault identification using a discrete square root method , 1986 .

[27]  M Weck.  Development and application of a flexible, modular monitoring an diagnosis system , 1986 .

[28]  L. F. Pau.  Failure Diagnosis and Performance Monitoring , 1986, IEEE Transactions on Reliability.

[29]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[30]  H. Saunders,et al.  Machinery Noise and Diagnostics , 1987 .

[31]  Jeffrey L. Stein,et al.  Measurement signal selection and a simultaneous state and input observer , 1987, American Control Conference.

[32]  V. R. Milacic,et al.  Diagnostic and preventive maintenance strategies in manufacturing systems: Elsevier Science Publishers BV, © 1988 xi + 252 pp. $65.75 , 1988 .

[33]  H. Saunders,et al.  Mechanical Signature Analysis—Theory and Applications , 1988 .

[34]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[35]  R. C. Kryter,et al.  Condition monitoring of machinery using motor current signature analysis , 1989 .

[36]  Jeffrey L. Stein,et al.  Steady-State Optimal State and Input Observer for Discrete Stochastic Systems , 1989 .

[37]  S. M. Wu,et al.  On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis , 1989 .

[38]  R. Skelton,et al.  Selection of Dynamic Sensors and Actuators in the Control of Linear Systems , 1989 .

[39]  S. Fassois,et al.  A Suboptimum Maximum Likelihood Approach to Parametric Signal Analysis , 1989 .

[40]  R. W. King,et al.  Use of a pattern recognition scheme to compensate for critical sensor failures , 1989 .

[41]  J T Tranter.  THE APPLICATION OF COMPUTERS TO MACHINERY PREDICTIVE MAINTENANCE , 1990 .

[42]  S. Fassois,et al.  Suboptimum Maximum Likelihood Identification of ARMAX Processes , 1990 .

[43]  Y. Shin.  System identification of multivariate systems with feedback , 1990 .

[44]  Hans Kurt Tönshoff,et al.  Machine Tool Monitoring Applied to Lathe Chucks , 1990 .

[45]  K. Danai,et al.  FAULT-DIAGNOSIS WITH PROCESS UNCERTAINTY , 1991 .

[46]  S. A. Spiewak,et al.  Adaptive Compensation of Dynamic Characteristics for In-Process Sensors , 1991 .

[47]  M. Szafarczyk,et al.  A Predictive Monitoring and Diagnosis System for Manufacturing , 1991 .

[48]  R. Isermann,et al.  Process Fault Diagnosis Based on Process Model Knowledge: Part I—Principles for Fault Diagnosis With Parameter Estimation , 1991 .

[49]  R. Isermann,et al.  Process Fault Diagnosis Based on Process Model Knowledge: Part II—Case Study Experiments , 1991 .

[50]  D. D. Cox,et al.  Modal and Spectrum Analysis: Data Dependent Systems in State Space , 1991 .

[51]  Stewart V. Bowers,et al.  REAL-WORLD MOUNTING OF ACCELEROMETERS FOR MACHINERY MONITORING , 1991 .

[52]  Kourosh Danai,et al.  A Method of Fault Signature Extraction for Improved Diagnosis , 1991, 1991 American Control Conference.

[53]  G. Pritschow.  Automatic Supervision of Control Systems , 1994 .