Condition-based Maintenance Optimization Using Neural Network-based Health Condition Prediction
نویسندگان
چکیده
Artificial neural network (ANN) based methods have been extensively investigated for equipment health condition prediction. However, effective condition based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (1) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (2) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above-mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real-world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies, and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Corresponding author. 1515 Ste-Catherine Street West, EV-7.637, Montreal, Quebec, H3G 2W1, Canada. Phone: 1-514-848-2424 ext. 7918; Fax: 1-514-848-3171. Email: [email protected]. 2
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ورودعنوان ژورنال:
- Quality and Reliability Eng. Int.
دوره 29 شماره
صفحات -
تاریخ انتشار 2013