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Journals / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 12 Issue: 2

Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan

Salih Ermiş(KIRŞEHİR AHİ EVRAN ÜNİVERSİTESİ)0000-0002-1053-9160
Oğuz Taşdemir(AHI EVRAN UNIVERSITY, FACULTY OF ENGINEERING-ARCHITECTURE)0000-0003-1782-0024
Çağatay Başgül(AHI EVRAN UNIVERSITY, FACULTY OF ENGINEERING-ARCHITECTURE)0009-0009-2198-9122
Pages
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Publication Date
2026-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed methodology bridges the gap between model outputs and field implementation, offering a transferable framework for data-driven predictive maintenance in industrial settings.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Unplanned machine downtime in manufacturing facilities leads to significant production losses, rising maintenance costs, and occupational safety risks.
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Key Findings & Data:Both models were trained on historical data and validated on a held-out period, then evaluated against actual fault events in early 2026.
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Conclusion & Impact:The proposed methodology bridges the gap between model outputs and field implementation, offering a transferable framework for data-driven predictive maintenance in industrial settings.
🔬Design:Modeling & Simulation
📍Scope:2026 Verileri
⚙️Variables:CNN-LSTM · CNN-GRU · fault prediction

Abstract

Unplanned machine downtime in manufacturing facilities leads to significant production losses, rising maintenance costs, and occupational safety risks. This study compares two hybrid deep learning architectures—Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) —for monthly fault frequency prediction using an eight-year operational dataset from the CLKKSM facility. One-dimensional convolutional layers extract local temporal features from the raw time series, which are subsequently encoded by recurrent cells to capture long-range dependencies. Both models were trained on historical data and validated on a held-out period, then evaluated against actual fault events in early 2026. CNN-LSTM consistently outperformed CNN-GRU across all error metrics on the out-of-sample test set. The performance gap is attributed to the four-gate memory mechanism of LSTM, which models medium- and long-range temporal dependencies more effectively than the two-gate GRU structure. Building on CNN-LSTM forecasts, a three-phase predictive maintenance action plan was developed for the April–December 2026 period, incorporating machine-level priority classification, risk-stratified schedules, and trackable Key Performance Indicator (KPI) targets. The proposed methodology bridges the gap between model outputs and field implementation, offering a transferable framework for data-driven predictive maintenance in industrial settings.

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Cite This Article

Ermiş, S., Taşdemir, O., Başgül, Ç. (2026). Hybrid Deep Learning-Based Fault Prediction for Industrial Machinery Using CNN-LSTM and CNN-GRU: CLKKSM Facility Application and Data-Driven Predictive Maintenance Action Plan. Gazi Mühendislik Bilimleri Dergisi, Volume: 12 Issue: 2. https://doi.org/10.30855/gmbd.070526A01

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