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Dergiler / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2025 / Volume: 67 Issue: 2

A comparative analysis of machine and deep learning models for predictive maintenance and fault estimation of tools in industrial machinery within small-scale settings

Yusuf Öztürk(ANTALYA BİLİM ÜNİVERSİTESİ)0000-0003-2762-5265
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Yayın Tarihi
2025-12-24
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, the study shows that advanced DL models can be deployed on resource-constrained embedded systems, supporting the wider adoption of Industry 4.0 practices in small and medium-sized enterprises.

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Amaç & Hipotez:This study presents a comparative evaluation of machine learning (ML) and deep learning (DL) models for predictive maintenance (PdM) in small-scale industrial systems.
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Temel Bulgular & Veri:According to the evaluation results, RNN-LSTM achieved the highest performance, with 95.31% accuracy, 0.047 MSE, 0.217 RMSE, 0.047 MAE, and 23.4% SMAPE.
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Sonuç & Katkı:Overall, the study shows that advanced DL models can be deployed on resource-constrained embedded systems, supporting the wider adoption of Industry 4.0 practices in small and medium-sized enterprises.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Predictive maintenance · RNN-LSTM · industrial IoT

Abstract

This study presents a comparative evaluation of machine learning (ML) and deep learning (DL) models for predictive maintenance (PdM) in small-scale industrial systems. A low-cost Arduino-based testbed equipped with vibration, temperature, and rotational speed sensors was developed to emulate real-world conditions. The primary focus of the study is the detailed implementation and analysis of a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). For benchmarking, two baseline models—Linear Regression and K-Nearest Neighbors (KNN)—were also implemented. According to the evaluation results, RNN-LSTM achieved the highest performance, with 95.31% accuracy, 0.047 MSE, 0.217 RMSE, 0.047 MAE, and 23.4% SMAPE. In comparison, Linear Regression and KNN yielded lower accuracies (92.30% and 93.27%) and higher error values (e.g., SMAPE of 58.7% and 41.2%). These findings confirm the superiority of RNN-LSTM in modeling temporal dependencies, while baseline models demonstrated limited generalization. Overall, the study shows that advanced DL models can be deployed on resource-constrained embedded systems, supporting the wider adoption of Industry 4.0 practices in small and medium-sized enterprises.

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Bu Makaleye Atıf Yap

Öztürk, Y. (2025). A comparative analysis of machine and deep learning models for predictive maintenance and fault estimation of tools in industrial machinery within small-scale settings. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 67 Issue: 2. https://doi.org/10.33769/aupse.1729175

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Kaynakça (16)

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