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The precise estimate of remaining useful life (RUL) is vital for theprognostic analysis and predictive maintenance that can significantly reducefailure rate and maintenance costs. The degradation-related features extractedfrom the sensor streaming data with neural networks can dramatically improvethe accuracy of the RUL prediction. The Temporal deep degradation network(TDDN) model is proposed to make the RUL prediction with thedegradation-related features given by the one-dimensional convolutional neuralnetwork (1D CNN) feature extraction and attention mechanism. 1D CNN is used toextract the temporal features from the streaming sensor data. Temporal featureshave monotonic degradation trends from the fluctuating raw sensor streamingdata. Attention mechanism can improve the RUL prediction performance bycapturing the fault characteristics and the degradation development with theattention weights. The performance of the TDDN model is evaluated on the publicC-MAPSS dataset and compared with the existing methods. The results show thatthe TDDN model can achieve the best RUL prediction accuracy in complexconditions compared to current machine learning models. The degradation-relatedfeatures extracted from the high-dimension sensor streaming data demonstratethe clear degradation trajectories and degradation stages that enable TDDN topredict the turbofan-engine RUL accurately and efficiently.

Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction