Abstract
Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.
Highlights
Machinery prognostics have recently emerged as an important research area because of its huge impact on the dependability, durability, and efficiency of complex systems
2: simple standard convolutional neural network (CNN) model trained with the acoustic emission (AE) signals; To evaluate the performance of our trained models,with we the used metrics of root mean square error (RMSE) (Equation (3)) and the Method-3: SqueezeNet based model
Even though the RF algorithm achieved better RMSE results for Method-1: random-forest model trained with the 15 expert-selected features; Method-2: simple standard CNN model trained with the AE signals; Method-3: SqueezeNet based model trained with the AE signals
Summary
Machinery prognostics have recently emerged as an important research area because of its huge impact on the dependability, durability, and efficiency of complex systems. The number of publications between 1997 and 2011 amount to 576 while the total count of publications between 2012 and 2016 is 854 Along with their increasing popularity, there is an anticipation of those machinery prognostics techniques in the industry [2,3]. Their expected impact is meant to maximize the operational availability, reduce maintenance costs, and improve system reliability and safety [1,2]. An important application of machinery prognostics is the remaining useful life (RUL) prediction: the goal is to predict the time left before observing a failure in a given machine or system [3]. RUL prediction algorithms can be largely grouped into two main
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