A neural network application for reliability modelling and condition-based predictive maintenance

Traditionally, decisions on the use of machinery are based on previous experience, historical data and common sense. However, carrying out an effective predictive maintenance plan, information about current machine conditions must be made known to the decision-maker. In this paper, a new method of obtaining maintenance information has been proposed. By integrating traditional reliability modelling techniques with a real-time, online performance estimation model, machine reliability information such as hazard rate and mean time between failures can be calculated. Essentially, this paper presents an innovative method to synthesise low level information (such as vibration signals) with high level information (like reliability statistics) to form a rigorous theoretical base for better machine maintenance.

[1]  Diederik J.D. Wijnmalen,et al.  Optimum condition-based maintenance policies for deteriorating systems with partial information , 1996 .

[2]  R. M. Stewart,et al.  Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis , 1978 .

[3]  Jay Lee,et al.  Analysis of machine degradation using a neural network based pattern discrimination model , 1993 .

[4]  Y. L. Tu,et al.  Integrated maintenance management system in a textile company , 1997 .

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

[6]  Brian Stone,et al.  The use of vibration measurements for quality control of machine tool spindles , 1998 .

[7]  M. Marseguerra,et al.  Simulation modelling of repairable multi-component deteriorating systems for 'on condition' maintenance optimisation , 2002, Reliab. Eng. Syst. Saf..

[8]  S. K. Yang,et al.  An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor , 2002, Reliab. Eng. Syst. Saf..

[9]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[10]  Izak Duenyas,et al.  Integrated maintenance and production control of a deteriorating production system , 2002 .

[11]  P. M. Anderson,et al.  Application of the weibull proportional hazards model to aircraft and marine engine failure data , 1987 .

[12]  J. Mathew,et al.  The condition monitoring of rolling element bearings using vibration analysis , 1984 .

[13]  Snr. D. E. Dimla The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation , 2002 .

[14]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[15]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[16]  Khairy A.H. Kobbacy,et al.  A full history proportional hazards model for preventive maintenance scheduling , 1997 .

[17]  C. T. Lam,et al.  Optimal maintenance policies for deteriorating systems under various maintenance strategies C. Teresa Lam and R.H. Yeh. , 1993 .

[18]  A. Sideris,et al.  Learning convergence in the cerebellar model articulation controller , 1992, IEEE Trans. Neural Networks.

[19]  Khairy A.H. Kobbacy,et al.  Generalized non-stationary preventive maintenance model for deteriorating repairable systems. , 2002 .