A Cotraining-Based Semisupervised Approach for Remaining-Useful-Life Prediction of Bearings

Abstract

The failure of bearings can have a significant negative impact on the safe operation of equipment. Recently, deep learning has become one of the focuses of RUL prediction due to its potent scalability and nonlinear fitting ability. The supervised learning process in deep learning requires a significant quantity of labeled data, but data labeling can be expensive and time-consuming. Cotraining is a semisupervised learning method that reduces the quantity of required labeled data through exploiting available unlabeled data in supervised learning to boost accuracy. This paper innovatively proposes a cotraining-based approach for RUL prediction. A CNN and an LSTM were cotrained on large amounts of unlabeled data to obtain a health indicator (HI), then the monitoring data were entered into the HI and the RUL prediction was realized. The effectiveness of the proposed approach was compared and analyzed against individual CNN and LSTM and the stacking networks SAE+LSTM and CNN+LSTM in the existing literature using RMSE and MAPE values on a PHM 2012 dataset. The results demonstrate that the RMSE and MAPE value of the proposed approach are superior to individual CNN and LSTM, and the RMSE value of the proposed approach is 54.72, which is significantly lower than SAE+LSTM (137.12), and close to CNN+LSTM (49.36). The proposed approach has also been tested successfully on a real-world task and thus has strong application value.

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network

“…The results demonstrate superior forecasting performance when compared with SVM, CNN, and LSTM methods. The root mean square error (RMSE) evaluates the performance of different methods for forecasting [ 50 , 51 ]. The RMSE was 0.799 for SVM, 0.593 for CNN, 0.53 for LSTM, and 0.485 for HINN in Table 1 .…”

“…The fault frequency at 12 Hz was readily identifiable and more pronounced compared with ACYCBD processing. The extraction of frequency multiplications, such as [12,25,38,50,63, 75, 88, 100, 113, 125 …], was notably accurate, with peak frequencies closely aligned with 1-10 times the fault frequency.…”