Prediction of Remaining Useful Life for Plastic Injection Molding Machines Using Artificial Intelligence Methods

Yıl 2022, Cilt: 2 Sayı: 1, 8 - 15, 30.06.2022

Gözde Aslantaş , Mustafa Özsaraç , Merve Rumelli , Tuna Alaygut , Gözde Bakırlı , Derya Bırant

Öz

Sustaining productivity with guaranteed machine availability is of the utmost significance while reducing costs. With the rising technology and the collected data in the industry, accomplishing such a goal is not fictional anymore. This paper proposes an artificial intelligence-based model that predicts the remaining useful life (RUL) of the plastic injection molding machines before requiring maintenance. Data collected from machines in production via sensors is preprocessed by performing various techniques, and anomalies in the data are detected and cleaned. Based on the historical data, the RUL of the machine, which is the duration until maintenance is required, is calculated, and the data is labeled with the RULs accordingly. In the proposed method, the labeling step is followed by feature engineering where the useful features are extracted from the raw data, such as entropy, peak to peak, and crest factor. A feature selection method is also applied to determine their contribution to the estimation accuracy of the RULs. As a comparison, we experimented with various regression models along with various evaluation metrics. The experimental results showed that our proposed approach achieved around 98% in the R2 performance metric.

Anahtar Kelimeler

Artificial intelligence, manufacturing, plastic injection molding machines, predictive maintenance, regression, remaining useful life

Proje Numarası

9190028

Kaynakça

• A. Theissler, J. Perez-Velazquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliability Engineering System Safety, vol. 215, pp. 1-21, 2021.
• D. Zhao and F. Liu, “Cross-condition and cross-platform remaining useful life estimation via adversarial-based domain adaptation,” Scientific Reports, vol. 12, pp. 1-13, 2022.
• F. Yao, W. He, Y. Wu, F. Ding, and D. Meng, “Remaining useful life prediction of lithium-ion batteries using a hybrid model,” Energy, vol. 248, pp. 1-13, 2022.
• H. Chen, Z. Zhan, P. Jiang, Y. Sun, L. Liao, X. Wan, Q. Du, X. Chen, H. Song, R. Zhu, Z. Shu, S. Li, and M. Pan, “Whole life cycle performance degradation test and RUL prediction research of fuel cell mea,” Applied Energy, vol. 310, pp. 1-10, 2022.
• C. Peng, Y. Chen, Q. Chen, Z. Tang, L. Li, and W. Gui, “A remaining useful life prognosis of turbofan engine using temporal and spatial feature fusion,” Sensors, vol. 21, no. 2, pp. 1-20, 2021.
• S. Falconer, E. Nordgard-Hansen, and G. Grasmo, “Remaining useful life estimation of HMPE rope during CBOS testing through machine learning,” Ocean Engineering, vol. 238, pp. 1-12, 2021.
• J.-Y. Wu, M. Wu, Z. Chen, X.-L. Li, and R. Yan, “Degradation aware remaining useful life prediction with LSTM autoencoder,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021.
• M. Ragab, Z. Chen, M. Wu, C.-K. Kwoh, R. Yan, and X. Li, “Attention based sequence to sequence model for machine remaining useful life prediction,” Neurocomputing, vol. 466, pp. 58-68, 2021.
• Z. Kang, C. Catal, and B. Tekinerdogan, “Remaining useful life (RUL) prediction of equipment in production lines using artificial neural networks,” Sensors, vol. 21, no. 3, pp. 1-20, 2021.
• P. F. Orru, A. Zoccheddu, L. Sassu, C. Mattia, R. Cozza, and S. Arena, “Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry,” Sustainability, vol. 12, no. 11, pp. 1-15, 2020.
• A. Bala, I. Ismail, R. Ibrahim, S. M. Sait, and D. Oliva, “An improved grasshopper optimization algorithm based echo state network for predicting faults in airplane engines,” IEEE Access, vol. 8, pp. 159773– 159789, 2020.
• M. Utah and J. Jung, “Fault state detection and remaining useful life prediction in ac powered solenoid operated valves based on traditional machine learning and deep neural networks,” Nuclear Engineering and Technology, vol. 52, no. 9, pp. 1998–2008, 2020.
• H.-C. Trinh and Y.-K. Kwon, “A data-independent genetic algorithm framework for fault-type classification and remaining useful life prediction,” Applied Sciences, vol. 10, no. 1, pp. 1-20, 2020.
• M. Calabrese, M. Cimmino, F. Fiume, M. Manfrin, L. Romeo, S. Ceccacci, M. Paolanti, G. Toscano, G. Ciandrini, A. Carrotta, M. Mengoni, E. Frontoni, and D. Kapetis, “Sophia: An event-based iot and machine learning architecture for predictive maintenance in industry 4.0,” Information, vol. 11, no. 4, pp. 1-17, 2020.
• Y. Chen, T. Zhang, W. Zhao, Z. Luo, and H. Lin, “Rotating machinery fault diagnosis based on improved multiscale amplitude-aware permutation entropy and multiclass relevance vector machine,” Sensors, vol. 19, no. 20, pp. 1-26, 2019.