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A task-driven remaining useful life predicting method for key parts of electromechanical equipment under dynamic service environment

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Abstract

Remaining useful life (RUL) prediction is the core link with prognostic and health management (PHM). Compared with the stable service environment, the degradation trend of key parts of electromechanical equipment under dynamic service environment has greater uncertainty. Dynamic changes of the service environment in the subsequent work tasks can result in inaccurate or even invalid current prediction results. To this end, considering that tasks can be known in advance and affect the service environment, a novel RUL prediction method is proposed which updates the predicting results as the tasks change based on dynamic correlation of planned tasks, service environment, degradation characteristics, and RUL. Firstly, the influence of the changes in service environment on the RUL of key parts is analyzed, and a task-driven classification and matching method of dynamic service environment is presented. Secondly, the key service environment variables affecting RUL of parts are screened employing grey relation analysis (GRA) and Pearson correlation coefficient. Thirdly, a task-driven deep long-short time memory (DLSTM) neural network prediction model under dynamic service environment is established, and a regularization method for optimizing the DLSTM model is given to improve the prediction accuracy. Finally, the RUL prediction of a machine tool spindle is taken as an example to verify the effectiveness of the proposed method.

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References

  1. Liu J, Lei F, Pan C, Hu D, Zuo H (2021) Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion. Reliab Eng Syst Saf 214:107807

    Article  Google Scholar 

  2. Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107:241–265

    Article  Google Scholar 

  3. Jiang X, Tian Z, Liu W, Tian G, Gao Y, Xiang F, Suo Y, Song B (2022) An energy-efficient method of laser remanufacturing process. Sustain Energy Technol Assess 52:102201

    Google Scholar 

  4. Cheng Y, Lin M, Wu J, Zhu H, Shao X (2021) Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowl-Based Syst 216:106796

    Article  Google Scholar 

  5. Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018) Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process 104:799–834

    Article  Google Scholar 

  6. Wen L, Li X, Gao L (2021) A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification. IEEE Trans Industr Electron 68(12):12890–12900

    Article  Google Scholar 

  7. Wang Z, Wang Z (2017) Method for calculating the life probability distribution characteristic of mechanical components with multiple failure modes. J Mech Eng 53(2):175–182

    Article  Google Scholar 

  8. Zhou J, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol 107:4715–4726

    Article  Google Scholar 

  9. Liao Y, Zhang L, Liu C (2018) Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method. IEEE International Conference on Prognostics and Health Management (ICPHM):58–61

  10. Li H, Liu Z, Jia W, Zhang D, Tan J (2021) Current research and challenges of deep learning for equipment remaining useful life prediction. Comput Integr Manuf Syst 27(1):34–52

    Google Scholar 

  11. Pan T, Chen J, Ye J, Li A (2022) A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines. Reliab Eng Syst Saf 225:108610

    Article  Google Scholar 

  12. Huang C, Huang H, Li Y (2019) A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Trans Industr Electron 66(11):8792–8802

    Article  Google Scholar 

  13. Tian G, Yuan G, Aleksandrov A, Zhang T, Li Z, Fathollahi-Fard M, Ivanov M (2022) Recycling of spent Lithium-ion batteries: a comprehensive review for identification of main challenges and future research trends. Sustain Energy Technol Assess 53:102447

    Google Scholar 

  14. Zhang X, Ao X, Jiang Z, Zhang H, Cai W (2019) A remanufacturing cost prediction model of used parts considering failure characteristics. Robot Comput-Integr Manuf 59:291–296

    Article  Google Scholar 

  15. Wang J, Zhang F, Zhang J, Liu W, Zhou K (2023) A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data. Reliab Eng Syst Saf 231:108976

    Article  Google Scholar 

  16. Liu X, Lei Y, Li N, Si X, Li X (2023) RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting. Mech Syst Signal Process 185:109788

    Article  Google Scholar 

  17. Peter ID, Mihaela M (2023) Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder. Eng Appl Artif Intell 117:105582

    Article  Google Scholar 

  18. Cai B, Wang Y, Zhang Y, Liu Y, Ge W, Li R, Liu Y, Liu G (2022) Condition-based maintenance method for multi-component system based on RUL prediction: Subsea tree system as a case study. Comput Ind Eng 173:108650

    Article  Google Scholar 

  19. Li X, Jiang X, Wang Q, Yang L, Wang Z, Shen C, Zhu Z (2022) Multi-perspective deep transfer learning model: a promising tool for bearing intelligent fault diagnosis under varying working conditions. Knowl-Based Syst 243:108443

    Article  Google Scholar 

  20. Su K, Liu J, Xiong H (2022) A multi-level adaptation scheme for hierarchical bearing fault diagnosis under variable working conditions. J Manuf Syst 64:251–260

    Article  Google Scholar 

  21. Yang C, Zhou J, Li E, Zhang H, Wang M, Li Z (2022) Milling cutter wear prediction method under variable working conditions based on LRCN. Int J Adv Manuf Technol 121:2647–2661

    Article  Google Scholar 

  22. Li W, Shang Z, Gao M, Qian S, Feng Z (2022) Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions. Reliab Eng Syst Saf 226:108722

    Article  Google Scholar 

  23. Ding N, Li H, Xin Q, Wu B, Jiang D (2023) Multi-source domain generalization for degradation monitoring of journal bearings under unseen conditions. Reliab Eng Syst Saf 230:108966

    Article  Google Scholar 

  24. Cai S, Hu J, Ma S, Yang Z, Wu H (2022) Remaining useful life prediction method of EV power battery for DC fast charging condition. Energy Rep 8(13):1003–1010

    Article  Google Scholar 

  25. Li J, Chen Y, Xiang H, Cai Z (2020) Remaining useful life prediction for aircraft engine based on LSTM-DBN. J Syst Eng Electron 42(7):1637–1644

    Google Scholar 

  26. Marei M, Li W (2022) Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning. Int J Adv Manuf Technol 118:817–836

    Article  Google Scholar 

  27. Wang F, Liu X, Deng G, Yu X, Li H, Han Q (2019) Remaining life prediction method for rolling bearing based on the long short-term memory network. Neural Process Lett 50:2437–2454

    Article  Google Scholar 

  28. Cheng Y, Hu K, Wu J, Zhu H, Shao X (2021) A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings. Adv Eng Inform 48:101247

    Article  Google Scholar 

  29. Ding N, Li H, Yin Z, Jiang F (2021) A novel method for journal bearing degradation evaluation and remaining useful life prediction under different working conditions. Meas 177:109273

    Article  Google Scholar 

  30. Wang J, Zhang J, Wang X (2018) Bilateral LSTM: a two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans Industr Inf 14(2):748–758

    Article  Google Scholar 

  31. Akpudo UE, Hur JW (2020) A feature fusion-based prognostics approach for rolling element bearing. J Mech Sci Technol 34(10):4025–4035

    Article  Google Scholar 

  32. Wu J, Hu K, Cheng Y, Zhu H, Shao X, Wang Y (2020) Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans 97:241–250

    Article  Google Scholar 

  33. Foix S, Alenyà G, Torras C (2018) Task-driven active sensing framework applied to leaf probing. Comput Electron Agric 147:166–175

    Article  Google Scholar 

  34. Wang Y, Zheng L, Wang Y (2021) Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet. J Manuf Syst 58:205–222

    Article  Google Scholar 

  35. Zheng L, He Y, Chen X, Pu X (2022) Optimization of dilated convolution networks with application in remaining useful life prediction of induction motors. Meas 200:111588

    Article  Google Scholar 

  36. Wei M, Gu H, Ye M, Wang Q, Xu X, Wu C (2021) Remaining useful life prediction of lithium-ion batteries based on Monte Carlo Dropout and gated recurrent unit. Energy Rep 7:2862–2871

    Article  Google Scholar 

  37. Boujamza A, Elhaq SL (2022) Attention-based LSTM for remaining useful life estimation of aircraft engines. IFAC-PapersOnLine 55(12):450–455

    Article  Google Scholar 

  38. Wielgosza M, Skoczeń A, Mertik M (2017) Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets. Nucl Instrum Meth Phys Res 867:40–50

    Article  Google Scholar 

  39. Zhang Y, Li Y, Song J, Chen X, Yang L, Wang K (2020) Pearson correlation coefficient of current derivatives based pilot protection scheme for long-distance LCC-HVDC transmission lines. Int J Electr Power Energy Syst 116:105526

    Article  Google Scholar 

  40. Wang Y, Zhao J, Yang C, Xu D, Ge J (2022) Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion. Meas 201:111572

    Article  Google Scholar 

  41. Yao L, Fang Z, Xiao Y, Hou J, Fu Z (2021) An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy 214:118866

    Article  Google Scholar 

  42. Qu Z, Xu J, Wang Z, Chi R, Liu H (2021) Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method. Energy 227:120309

    Article  Google Scholar 

  43. Cheng Y, Wang C, Wu J, Zhu H, Lee CKM (2022) Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Appl Soft Comput 118:108507

    Article  Google Scholar 

  44. Chen JC, Chen TL, Liu W, Cheng CC, Li M (2021) Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Adv Eng Inform 50:101405

    Article  Google Scholar 

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Funding

This research is funded by the National Natural Science Foundation of China (Grant No. 51905392, Grant No. 52075396). These financial contributions are gratefully acknowledged.

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Authors and Affiliations

  1. Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China

    Zhigang Jiang

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Qing Zhang

  3. Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, 430081, China

    Shuo Zhu

  4. Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Hua Zhang & Wei Yan

Authors
  1. Zhigang Jiang
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  2. Qing Zhang
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  3. Shuo Zhu
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  4. Hua Zhang
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  5. Wei Yan
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Contributions

Zhigang Jiang and Shuo Zhu are responsible for the idea and methodology development; Qing Zhang is responsible for manuscript writing, idea, and methodology discussion. All authors are responsible for supervision and manuscript refinement.

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Correspondence to Shuo Zhu.

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Jiang, Z., Zhang, Q., Zhu, S. et al. A task-driven remaining useful life predicting method for key parts of electromechanical equipment under dynamic service environment. Int J Adv Manuf Technol 125, 4149–4162 (2023). https://doi.org/10.1007/s00170-023-10981-6

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  • DOI: https://doi.org/10.1007/s00170-023-10981-6

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