An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

• Published in: Journal of intelligent manufacturing Vol. 23; no. 2; pp. 227 - 237
• Main Author: Tian, Zhigang
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2012; Springer Nature B.V
• Subjects: Accuracy; Age; Analysis; Bearings; Business and Management; Case studies; Comparative studies; Control; Crack initiation; Failure; Intelligent systems; Machines; Maintenance costs; Maintenance management; Manufacturing; Mechatronics; Methods; Monitoring; Neural networks; Physics; Processes; Production; Robotics; Studies; Useful life; Artificial neural network; Remaining useful life; Prediction; Accurate; Bearing
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-009-0356-9

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.