A rule-based classifier ensemble for fault diagnosis of rotating machinery

To predict potential problems and avoid an unexpected breakdown of rotating machinery, a rule-based classifier ensemble approach is presented. Feature reduction was first implemented on a fault decision table using discernibility matrices and the genetic algorithm. The generated rules of the reducts were used to build the candidate base classifiers. Then, several base classifiers were selected according to their diversity and scale. The weights of the selected base classifiers were also calculated based on the support rate measurements. A classifier ensemble was constructed through an integration of the base classifiers using an improved weighted voting technique. Finally, the proposed classifier ensemble was verified based on the vibration data of bearing types SKF6203 and NU205. The accuracy for the SKF6203 bearing type reached 88.75 %, which is at least 5 % higher than that of the three base classifiers for this type of bearing. In addition, the recognition rate for the latter bearing type was 90 %. The reasoning process was much easier to comprehend owing to the semantic descriptions of the rules. The results show that this is a promising and transparent approach for diagnosing typical faults of rotating machinery.