Fault diagnosis for discrete monitoring data based on fusion algorithm

Fault diagnosis has a significant role in enhancing the safety, reliability, and availability of complex systems. However, the problem of enormous condition monitoring data and multiple failure modes makes the diagnostics great challenge. The imbalance between normal and fault monitoring data will increase the false alarm rate and the false negative rate. On the other hand, discrete monitoring data such as events are frequent and critical to fault diagnosis of complex systems. In this work, we propose a fusion fault diagnostic method which combines Naïve Bayes with AdaBoost ensemble algorithm. This integrated method is appropriate for discrete data and improves the adaptability for imbalanced condition monitoring data. Experimental results based on PHM 2013 dataset show that fault diagnosis performance using the fusion method can be ameliorated.