Techniques for Apply Predictive Maintenance and Remaining Useful Life: A Systematic Mapping Study

Öz

With prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.

Anahtar Kelimeler

• Predictive Maintenance
• Remaining Useful Life
• Machine Learning
• Deep Learning
• Root Mean Square Error

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Öz

Prognostik faaliyetler ile endüstriyel sistemlerin kalan yararlıömrünü (RUL), mevcut sağlık durumlarının takip ederek yüksek doğrulukta tahmin edilmesi mümkündür. Bu çalışmadakestirimci bakım ve kalan faydalı ömür hakkında 199 makale topladık. Sistematik haritalamaçalışmamızın amacı, kestirimci bakım ve kalan faydalı ömür alanlarında hangi teknik ve yöntemlerin kullanıldığını belirlemektir. Amaçladığımız bir diğer konu da bu alanda çalışacak araştırmacılara ana konu hakkında fikir vermektir. IEEE ve Science Direct gibi veritabanları belirli kriterler ile aranarak makale havuzu oluşturuldu ve elde edilen makaleler sınıflandırıldı. Toplanılan makale havuzunda gerekli dahil etme ve hariç tutma kriterleri uygulanarak en uygun makaleler belirlendi ve çalışmamız bu makaleler üzerinden gerçekleştirildi. Sonuçlara odaklandığımızda Destek Vektör Makinesi algoritmasının en çok tercih edilen kestirimci bakım yöntemi olduğu öğrenildi. Performansı değerlendirmeyi ve sonuçların doğruluğunu hesaplamayı amaçlayan çoğu çalışmada Kök Ortalama Kare Hatası algoritması kullanılmıştır. Çalışmamızda makalelerde yer alan her yöntem ve algoritma tartışılmıştır. Makaleler, belirlediğimiz amaç ve sorularla birlikte incelenerek sonuçlar elde edilmiştir. Elde edilen sonuçlar makalede açıklanmış ve grafik olarak gösterilmiştir. Elde edilen sonuçlara göre, kestirimci bakım ve kalan faydalı ömür konularının, kullanıldıkları ortama işlevsellik ve finansal kazanç sağladığı görülmüştür. Çalışmamız, kestirimci bakım uygulaması ile ilgili birçok soruyu aydınlatarak sonuçlandırılmıştır.

Anahtar Kelimeler

• Kestirimci Bakım
• Kalan Yararlı Ömür
• Makine Öğrenmesi
• Derin Öğrenme
• Kök Ortalama Kare Hata

Kaynakça

1. Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability, 65(3), 1314-1326.
2. Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314-334.
3. EN13306, “Maintenance terminology,” Br. Stand. Inst., no. CEN (European Committee for Standardization), p. 58, 2010. (CEN (2001) EN 13306 Maintenance Terminology. Brussels: CEN)
4. Wang, H., Ye, X., & Yin, M. (2016). Study on predictive maintenance strategy. International. Journal of Science and Technology, 9(4), 295-300.
5. Liao, L., &Köttig, F. (2014). Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191-207.
6. Dicheva, D., Dichev, C., Agre, G., &Angelova, G. (2015). Gamification in education: A systematic mapping study. Journal of Educational Technology & Society, 18(3).
7. Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Vol. 5). Technical report, Ver. 2.3 EBSE Technical Report. EBSE.
8. Budgen, D., & Brereton, P. (2006, May). Performing systematic literature reviews in software engineering. In Proceedings of the 28th international conference on Software engineering (pp. 1051-1052).

9. Petersen, K., Feldt, R., Mujtaba, S., &Mattsson, M. (2008, June). Systematic mapping studies in software engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12 (pp. 1-10).
10. Pautasso, M. (2013). Ten simple rules for writing a literature review. PLoSComput Biol, 9(7).
11. Bruneo, D., & De Vita, F. (2019, June). On the use of LSTM networks for Predictive Maintenance in Smart Industries. In 2019 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 241-248). IEEE.
12. Schenkendorf, R., Groos, J. C., & Johannes, L. (2015). Strengthening the rail mode of transport by condition based preventive maintenance. IFAC-PapersOnLine, 48(21), 964-969.
13. Said, A. B., Shahzad, M. K., Zamaï, É., Hubac, S., &Tollenaere, M. (2016). Towards proactive maintenance actions scheduling in the Semiconductor Industry (SI) using Bayesian approach. IFAC-PapersOnLine, 49(12), 544-549.
14. Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., &Mentzas, G. (2017). A proactive event-driven decision model for joint equipment predictive maintenance and spare parts inventory optimization. Procedia Cirp, 59, 184-189.
15. Mathew, V., Toby, T., Singh, V., Rao, B. M., & Kumar, M. G. (2017, December). Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning. In 2017 IEEE International Conference on Circuits and Systems (ICCS) (pp. 306-311). IEEE.
16. Yiwei, W. A. N. G., Christian, G. O. G. U., Binaud, N., Christian, B. E. S., &Haftka, R. T. (2017). A cost driven predictive maintenance policy for structural airframe maintenance. Chinese Journal of Aeronautics, 30(3), 1242-1257.
17. Li, X., Er, M. J., Ge, H., Gan, O. P., Huang, S., Zhai, L. Y., ... &Torabi, A. J. (2012, October). Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society (pp. 2821-2826). IEEE.
18. Xia, L., Fang, H., & Zhang, H. (2013, May). HMM based modeling and health condition assessment for degradation process. In 2013 25th Chinese Control and Decision Conference (CCDC) (pp. 2945-2948). IEEE.
19. Blancke, O., Tahan, A., Komljenovic, D., Amyot, N., Lévesque, M., &Hudon, C. (2018). A holistic multi-failure mode prognosis approach for complex equipment. Reliability Engineering & System Safety, 180, 136-151.
20. Quatrini, E., Costantino, F., Pocci, C., &Tronci, M. (2020). Predictive model for the degradation state of a hydraulic system with dimensionality reduction. Procedia Manufacturing, 42, 516-523.
21. Chen, F., Yang, Y., Tang, B., Chen, B., Xiao, W., & Zhong, X. (2020). Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. Measurement, 151, 107116.
22. Tongyang, L. I., Shaoping, W. A. N. G., Jian, S. H. I., &Zhonghai, M. A. (2018). An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps. Chinese Journal of Aeronautics, 31(5), 941-948.
23. Yu, J. (2011). A hybrid feature selection scheme and self-organizing map model for machine health assessment. Applied Soft Computing, 11(5), 4041-4054.
24. Liao, L., Jin, W., & Pavel, R. (2016). Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Transactions on Industrial Electronics, 63(11), 7076-7083.
25. Susto, G. A., Wan, J., Pampuri, S., Zanon, M., Johnston, A. B., O'Hara, P. G., & McLoone, S. (2014, August). An adaptive machine learning decision system for flexible predictive maintenance. In 2014 IEEE International Conference on Automation Science and Engineering (CASE) (pp. 806-811). IEEE.
26. Melendez, I., Doelling, R., &Bringmann, O. (2019, December). Self-supervised Multi-stage Estimation of Remaining Useful Life for Electric Drive Units. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 4402-4411). IEEE.
27. Benkedjouh, T., Medjaher, K., Zerhouni, N., &Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751-1760.
28. Bagheri, B., Yang, S., Kao, H. A., & Lee, J. (2015). Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC-PapersOnLine, 48(3), 1622-1627.
29. Aye, S. A., &Heyns, P. S. (2017). An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mechanical Systems and Signal Processing, 84, 485-498.
30. Kraus, M., &Feuerriegel, S. (2019). Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Decision Support Systems, 125, 113100.
31. Calabrese, F., Regattieri, A., Botti, L., &Galizia, F. G. (2019). Prognostic Health Management of Production Systems. New Proposed Approach and Experimental Evidences. Procedia Manufacturing, 39, 260-269.
32. Heng, W., Guangxian, N., Jinhai, C., &Jiangming, Q. (2020). Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor. Measurement, 107657.
33. Zhu, J., Chen, N., & Shen, C. (2020). A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical Systems and Signal Processing, 139, 106602.
34. Wu, H., Huang, A., & Sutherland, J. W. (2020). Avoiding Environmental Consequences of Equipment Failure via an LSTM-Based Model for Predictive Maintenance. Procedia Manufacturing, 43, 666-673.
35. Chen, C., Liu, Y., Wang, S., Sun, X., Di Cairano-Gilfedder, C., Titmus, S., &Syntetos, A. A. (2020). Predictive maintenance using cox proportional hazard deep learning. Advanced Engineering Informatics, 44, 101054.
36. Cui, L., Jianzhong, S., He, L., Shiying, L., & Xinhua, H. (2020). Complex Engineered System Health Indexes Extraction Using Low Frequency Raw Time-Series Data Based on Deep Learning Methods. Measurement, 107890.
37. Li, Z., Wu, D., Hu, C., &Terpenny, J. (2019). An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliability Engineering & System Safety, 184, 110-122.
38. Zhang, C., Wang, C., Lu, N., & Jiang, B. (2019). An RBMs-BN method to RUL prediction of traction converter of CRH2 trains. Engineering Applications of Artificial Intelligence, 85, 46-56.
39. Utah, M. N., & Jung, J. C. (2020). 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.
40. Sadoughi, M., Lu, H., & Hu, C. (2019, June). A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings. In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-7). IEEE.
41. Hwang, H. J., Lee, J. H., Hwang, J. S., & Jun, H. B. (2018). A study of the development of a condition-based maintenance system for an LNG FPSO. Ocean Engineering, 164, 604-615.
42. Ahmad, W., Khan, S. A., & Kim, J. M. (2017). A hybrid prognostics technique for rolling element bearings using adaptive predictive models. IEEE Transactions on Industrial Electronics, 65(2), 1577-1584.
43. Diaz-Rozo, J., Bielza, C., &Larranaga, P. (2017). Machine learning-based CPS for clustering high throughput machining cycle conditions. Procedia Manuf, 10, 997-1008.
44. Kim, H. E., Tan, A. C., Mathew, J., & Choi, B. K. (2012). Bearing fault prognosis based on health state probability estimation. Expert Systems with Applications, 39(5), 5200-5213.
45. Liao, L., &Köttig, F. (2016). A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing, 44, 191-199.
46. Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020). A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Robotics and Computer-Integrated Manufacturing, 65, 101974.
47. Selected papers of SMS study (2021). https://drive.google.com/drive/folders/1-EwSqppnBnCaAEnFOrPV7IHdP_1RzcC9?usp=sharing