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.