Bearing failure and remaining useful life estions using artificial intelligence techniques

This study presents innovative approaches for fault diagnosis and life prediction of bearings, which are critical components of industrial machines. Bearing failures can lead to significant performance losses and high economic costs, emphasizing the importance of early diagnosis and life prediction in this field. In the study, artificial defects were created using laser beams, and these defects were analyzed in detail through vibration analysis under varying speed and load conditions. Features were extracted using 18 different entropy-based methods and classified using the Extreme Learning Machine (ELM) model. Notably, Fuzzy Entropy and Slope Entropy methods demonstrated high performance with accuracy rates of 98.48% and 100%, respectively. The proposed method outperformed other modern approaches in the literature. Another significant aspect of the study involves the use of the MM-1D-LBP method for feature extraction and a hybrid 1D-CNN-LSTM-based model for fault prediction. This method achieved accuracy rates of 99.31% to 99.65%. Compared to commonly used models in the literature, such as GRU and LSTM, the proposed approach provided higher accuracy, particularly in classifying complex fault types. For bearing life prediction, a method combining 1D-TP and LSTM models was developed and tested on datasets from the Pronostia platform. In analyses based on vibration signals, the Bearing3_3 scenario yielded low error and high performance metrics, with RMSE = 0.0470 and Score = 0.6360. The proposed model stands out with lower error rates compared to other methods in the literature. For instance, the Bi-LSTM model (RMSE = 0.2300) and the Relief-SVM model (RMSE = 0.2500) showed inferior accuracy compared to the proposed 1D- TP+LSTM model, which achieved an RMSE of 0.2074. In conclusion, this study offers significant contributions in fault diagnosis and life prediction through innovative methods such as entropy-based ELM and 1D-TP+LSTM. The developed models provide faster, more reliable, and cost-effective solutions in industrial maintenance processes. The findings of the study serve as a valuable reference for both academic literature and industrial applications. In the future, enhancing the generalization capacity of the model with different datasets and operating conditions, as well as diversifying entropy methods, could further improve the effectiveness of the proposed approaches.