Friction change detection in industrial robot arms

Industrial robots have been used as a key factor to improve productivity, quality and safety in manufacturing. Many tasks can be done by industrial robots and they usually play an important role in the system they are used, a robot stop or malfunction can compromise the whole plant as well as cause personal damages. The reliability of the system is therefore very important.Nevertheless, the tools available for maintenance of industrial robots are usually based on periodical inspection or a life time table, and do not consider the robot’s actual conditions. The use of condition monitoring and fault detection would then improve diagnosis.The main objective of this thesis is to define a parameter based diagnosis method for industrial robots. In the approach presented here, the friction phenomena is monitored and used to estimate relevant parameters that relate faults in the system. To achieve the task, the work first presents robot and friction models suitable to use in the diagnosis. The models are then identified with several different identification methods, considering the most suitable for the application sought.In order to gather knowledge about how disturbances and faults affect the friction phenomena, several experiments have been done revealing the main influences and their behavior. Finally, considering the effects caused by faults and disturbances, the models and estimation methods proposed, a fault detection scheme is built in order to detect three kind of behavioral modes of a robot (normal operation, increased friction and high increased friction), which is validated within some real scenarios.

[1]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[2]  Mattias Nordin,et al.  Controlling mechanical systems with backlash - a survey , 2002, Autom..

[3]  Erik Wernholt,et al.  On Multivariable and Nonlinear Identification of Industrial Robots , 2004 .

[4]  V. F. Filaretov,et al.  Parity relation approach to fault diagnosis in manipulation robots , 2003 .

[5]  R. Isermann.  Estimation of physical parameters for dynamic processes with application to an industrial robot , 1992 .

[6]  Fabrizio Caccavale.  Experiments of observer-based fault detection for an industrial robot , 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

[7]  L. W. Tsai,et al.  Robot Analysis: The Mechanics of Serial and Parallel Ma-nipulators , 1999 .

[8]  F. Moon,et al.  Chaos in a Forced Dry-Friction Oscillator: Experiments and Numerical Modelling , 1994 .

[9]  Geir Hovland,et al.  Nonlinear identification of backlash in robot transmissions , 2002 .

[10]  Qiang Bi,et al.  Parameter identification of continuous-time mechanical systems without sensing accelerations , 1996 .

[11]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[12]  Erik Wernholt,et al.  Multivariable Frequency-Domain Identification of Industrial Robots , 2007 .

[13]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .

[14]  Mehrdad Saif,et al.  Robust discrete time observer with application to fault diagnosis , 1998 .

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

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

[17]  Joachim Holtz,et al.  Identification and compensation of torque ripple in high-precision permanent magnet motor drives , 1996, IEEE Trans. Ind. Electron..

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

[19]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[20]  Ian D. Walker,et al.  Observer-based fault detection for robot manipulators , 1997, Proceedings of International Conference on Robotics and Automation.

[21]  Béla Lantos,et al.  Modeling, Identification, and Compensation of Stick-Slip Friction , 2007, IEEE Transactions on Industrial Electronics.

[22]  Peter Young,et al.  Parameter estimation for continuous-time models - A survey , 1979, Autom..

[23]  Paul M. Frank,et al.  Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation , 1996, IEEE Trans. Control. Syst. Technol..

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

[25]  M. Indri,et al.  Nonlinear friction estimation for digital control of direct-drive manipulators , 2003, 2003 European Control Conference (ECC).