Development of an AI-Based Predictive Maintenance and Fault Diagnosis System for Marine Diesel Engines
2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, Bursa, Türkiye, 10 - 12 Eylül 2025, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/asyu67174.2025.11208261
- Basıldığı Şehir: Bursa
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: anomaly detection, deep learning architecture, fault classification, marine diesel engine, supervised learning, timeseries data
- Gazi Üniversitesi Adresli: Evet
Özet
Accurate classification of fault types in marine diesel engines is critical for ensuring operational reliability and optimizing maintenance efficiency. In this paper, we introduce a hybrid deep-learning framework for multi-class fault diagnosis based on time-series sensor data. The proposed architecture synergistically combines Convolutional Neural Networks for local feature extraction with Bidirectional Long Short-Term Memory layers to capture temporal dependencies and incorporates the Time2Vec encoding scheme for explicit modeling of the time dimension. Sensor signals are segmented via a three-step sliding-window approach and grouped according to operational conditions. Performance is evaluated using accuracy, precision, recall and F1-score metrics. A comparative analysis of multiple configurations identifies the Time2Vec-CNN-BiLSTM model as the optimal architecture, achieving an accuracy of 96%. These results demonstrate the framework's robustness in time-series fault diagnosis and highlight its potential to advance predictive maintenance practices in marine diesel engine systems.