A novel information fusion approach using weighted neural networks for intelligent multi-class diagnostics of rotating machinery with unseen working conditions

Abstract

Analytical diagnostics tools have been proved effective. However, these techniques are unable to be adaptively implemented for diverse machines with various defect modes. Big data-based diagnostics tools have been demonstrated efficient, but they exploit massive data with a high computational cost and time that is impractical for online condition monitoring. Furthermore, the health signals may be acquired under unseen working conditions in real machines. These are the major challenges for previous diagnostics methods and make them infeasible in real-world applications. So, a novel technique is proposed in this research to solve mentioned problems. The main novelty of this method is to fuse information of various processing functions for multi-sensor signals and to apply an efficient feature bank for performing an efficacious feature learning method. The other novelty of this research is to select transferable features for diagnostics of rotating machinery under unseen working conditions. Mean squared error function for the improvement of diagnostics accuracy and transferable feature space with semidefinite dimension for the improvement of diagnostics speed and better visualization for the source and target machines with different working conditions are minimized jointly via weighted neural networks. This approach is verified by two case studies of machinery vibration datasets for the robustness analysis of bearing multi-fault diagnosis problems under various operational conditions for the sensitivity analysis. Results indicate that the proposed algorithm achieves good performance in real rotating machinery diagnostics with unseen working conditions.