Disrupting Downtime: Different Deep Learning Journeys into Predictive Maintenance Anomaly Detection

Yıl 2024, Cilt: 5 Sayı: 1, 47 - 53, 29.06.2024

Hayriye Tanyıldız , Canan Batur Şahin , Özlem Batur Dinler

https://doi.org/10.46572/naturengs.1490748

Öz

This article discusses the innovative application of artificial intelligence (AI) to develop a predictive model that aims to evaluate the condition of the machine by focusing on the probability of failure. The research uses a synthetic dataset prepared to simulate real-world situations where machines are equipped with sensors that monitor various health indicators and record the occurrence of faults. This data set consists of 10,000 inputs, each containing five numerical measurements: air temperature, process temperature, rotation speed, torque, and machine wear, in addition to the type of product produced, for a total of six input variables. The output of the model is the fault state of the machine, displayed as true or false.
A hybrid artificial neural network integrating a GRU (Gated Recurrent Unit)-based model with the Transformer Encoder block was used for prediction. This combination highlights the superior predictive capabilities of the model. This approach represents a shift from traditional maintenance programs, which are often time-based and often result in unnecessary resource use, to a more efficient, condition-based maintenance strategy. This new strategy aims to ensure that maintenance activities are carried out only when necessary, thus optimizing resource use and minimizing downtime.

Anahtar Kelimeler

Industrial Control Systems, Deep Learning, Anaomaly Detection, Cyber Attack

Kaynakça

• Kim D-G, Choi J-Y. (2021), Optimization of Design Parameters in LSTM Model for Predictive Maintenance. Applied Sciences. 11(14):6450.
• Carrasco, J., López, D., Aguilera-Martos, I., García-Gil, D., Markova, I., Garcia-Barzana, M., ... & Herrera, F. (2021). Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms. Neurocomputing, 462, 440-452.
• Doe, J., Smith, J., et al. (2019), A Deep Learning Approach for Predictive Maintenance Anomaly Detection. Norwegian University of Science and Technology.
• Rivas, A., Fraile, J.M., Chamoso, P., González-Briones, A., Sittón, I., Corchado, J.M. (2020), A Predictive Maintenance Model Using Recurrent Neural Networks. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham.
• Davis, C., Lee, D., et al. (2022), Unsupervised Anomaly Detection Using Autoencoders for Predictive Maintenance. CEUR Workshop Proceedings, Vol. 3478.
• Green, E., Thompson, F., et al. (2018), A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future. ResearchGate.
• Qureshi, M. S., Umar, S., & Nawaz, M. U. (2024). Machine Learning for Predictive Maintenance in Solar Farms. International Journal of Advanced Engineering Technologies and Innovations, 1(3), 27-49.
• https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset
• Sengupta, P., Mehta, A., & Rana, P. S. (2023). Predictive Maintenance of Armored Vehicles using Machine Learning Approaches. arXiv preprint arXiv:2307.14453.
• H. Sarker, (2021), Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions, SN Computer Science, vol. 2, no. 6, p. 420.
• S. Abbaspour, F. Fotouhi, A. Sedaghatbaf, H. Fotouhi, M. Vahabi, and M. Linden, (2020), A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition, Sensors, vol. 20, no. 19,
• Chakraborty, K., Mehrotra, K., Mohan, C.K., and Ranka, S., (1992). Forecasting The Behavior of Multivariate Time Series Using Neural Networks. Neural Networks 5(6):961-970.
• Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2023), A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv preprint arXiv:2305.17473.
• Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014), Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
• Bulut, A., & Batur Dinler, Ö. (2023). The Effect of Industry 4.0 and Artificial Intelligence on Human Resource Management. Uluslararası Doğu Anadolu Fen Mühendislik ve Tasarım Dergisi, 5(2), 143-166.
• Şahin,C.B., Dinler, Ö.B. & Abualigah, L. Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Appl Intell 51, 8271–8287 (2021).
• Şahin,C.B., Dinler, Ö.B. & Abualigah, L. (2021).Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Appl Intell 51, 8271–8287. https://doi.org/10.1007/s10489-021-02324-3.
• C. B. Şahin, (2021).DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network, 2021 International Conference on Innovations in Intelligent Systems and Applications (INISTA), Kocaeli, Turkey, pp. 1-8, doi: 10.1109/INISTA52262.2021.9548609.
• BATUR ŞAHİN, C. (2022). Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism. Avrupa Bilim Ve Teknoloji Dergisi(41), 156-165.
• Batur Şahin, C. (2022). Optimization of Software Vulnerability Patterns with Meta-Heuristic Algorithms. Türk Doğa ve Fen Dergisi, 11(4), 117-125. https://doi.org/10.46810/tdfd.1201248.
• Ullah, A., Batur Dinler, Ö., & Batur Şahin, C. (2021). (2021). The Effect of Technology and Service on Learning Systems During the COVID-19 Pandemic.
• Ozlem Batur Dinler, Canan Batur Şahin, & Hanane Aznaoui. (2024). HYBRID MODEL USED FOR REDUCING LATENCY IN SMART HEALTHCARE SYSTEMS. Journal of Advancement in Computing, 2(1), 10–20.
• Ulah, A., Aznaoui, H., Batur Şahin, C., Sadie, M., Dinler, O., (2022), Cloud computing and 5G challenges and open issues. Int. J. Adv. Appl. Sci.