Remaining Useful Life Prediction in an Aerospace Engine: A Multivariable Fuzzy Time Series Classification Approach

ABSTRACT Failures in safety‐critical systems such as aircraft engines pose severe economic and societal risks. This study introduces a novel Remaining Useful Life (RUL) prediction method uniquely combining diverse techniques. Specifically, the proposed methodology integrates fuzzy time series analysis with sliding window segmentation and Multinomial Naive Bayes (MNB) classification. These techniques transform raw sensor data from NASA's C‐MAPSS turbofan engine datasets into a symbolic representation that effectively captures degradation patterns leading to system failure. Tested across the four subsets—FD001, FD002, FD003 and FD004—from the C‐MAPSS NASA dataset, the proposed approach achieved competitive RMSE values of 24.73, 36.03, 34.71 and 39.07, respectively, while demonstrating robust PHM score metrics of as low as 1508 for one of the datasets. By optimising key parameters to enhance accuracy and computational efficiency, this low‐computational‐cost alternative to conventional deep learning models significantly advances RUL prediction, offering a promising alternative prognostic strategy in environments where the balance between computational efficiency and accuracy is essential.