Güler, A.Balli, T.Yetkin, E.F.2025-02-152025-02-15202409798350365887https://doi.org/10.1109/UBMK63289.2024.10773607https://hdl.handle.net/20.500.12469/7191This work proposed a new forecasting approach for predictive maintenance in industrial settings, combining standard segmentation approaches like Symbolic Aggregate Approximation (SAX) and Piecewise Aggregate Approximation (PAA) with LSTM (Long-Short Time Memory). The work aims to construct a robust forecasting mechanism to estimate maintenance requirements in advance properly. We first demonstrated the results of the proposed approach for synthetically generated data and extended the results with real industrial vibration data. The algorithm's performance is assessed using real-world industry data from steel production furnaces, where timely maintenance is critical for increasing operating efficiency and reducing downtime. Experimental results show that using SAX and LSTM for forecasting industrial time series data achieves high accuracy rates (90.2 %) in a reasonable computational time. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessLong Short Term Memory (Lstm)Machine Learning AlgorithmsMaintenance ForecastingPiecewise Aggregate Approximation (Paa)Predictive MaintenanceSymbolic Aggregate Approximation (Sax)Conference Object95095410.1109/UBMK63289.2024.107736072-s2.0-85215531188N/AN/A