A Time-Series Data Generation Method to Predict Remaining Useful Life

Abstract

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

“…Finally, it is easy to implement without any domain knowledge and analyzing degradation trends ( Cai et al, 2020 ), because it employs similar historical data as references and relies on the historical data itself. In addition, it is difficult to obtain enough degradation data in real world applications ( Ahn et al, 2021 ), but the similarity-based method has been proven effective to predict RUL with the limited data ( Lyu et al, 2020 ).…”

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

“…While considerable research has been conducted on the use of deep learning techniques relative to machine health monitoring, very few studies have focused on applying deep learning to the prediction of RUL with associated uncertainties [1,12,18]. Precise RUL prediction can considerably increase industrial components or systems' reliability and operational safety [19], prevent fatal failures, and lower maintenance costs [20]. Therefore, several attempts have been conducted in the literature to predict the RUL of a turbofan engine.…”

Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

“…Machine learning algorithms have been successfully used to build effective prediction models for different applications in the various area [35][36][37][38][39][40][41]. There is relatively fewer research applying machine learning methods for NBA game outcomes prediction and NBA game final score prediction [16][17][18][19][20][21][22][23][24].…”