Automated diagnosis of rolling element bearing defects using time-domain features and neural networks

Abstract

Rolling element bearings are essential components in most mining equipment and mineral processing plants. Condition monitoring procedures need to be developed to prevent unexpected failure of these elements during operation to avoid costly consequences. Procedures for automated defect detection and diagnosis of bearings from vibration signals collected using accelerometer are presented in this article. Time-domain signals are processed to obtain the negative log-likelihood values and the root mean square (RMS) value, which are used as input features for an artificial neural network (ANN) classifier. Effectiveness of the proposed method is illustrated using the bearing vibration data obtained experimentally, and the results showed 100% accuracy in defect detection and diagnosis. The proposed procedures do not require preprocessing of the signal before feature extraction. The algorithm uses fewer input features, resulting in faster training. The results demonstrate the potential of negative log-likelihood values and neural networks for on-line defect detection and diagnosis of bearings.

Keywords:

• defect diagnosis
• rolling element bearing
• vibration analysis
• artificial neural network

Acknowledgement

The authors thank Professor K.A. Loparo of Case Western Reserve University for providing access to the bearing vibration dataset.