Skip to main content

Advertisement

GRUDMU-DSCNN: An edge computing method for fault diagnosis with missing data

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Traditional deep learning methods for rolling bearing fault diagnosis require a lot of computational time and resources. At the same time, the accuracy of fault diagnosis is affected by missing data collected due to the instability of sensors or data acquisition systems. In this paper, we propose a fault diagnosis method based on Gated Recurrent Unit with Decays and Maskless Update—Depthwise Separable Convolution Neural Network (GRUDMU-DSCNN). First, we use the trainable attenuation mechanism in GRUDMU for effective imputation of missingness and change the position of mask vectors to deal with missing data and solve the problem of missing data affecting the accuracy of fault diagnosis. In addition, we combine GRUDMU with DSCNN and deploy the model to edge devices. This improves the effectiveness of real-time fault diagnosis in edge computing scenarios. Furthermore, to verify whether the proposed method is effective in improving the accuracy of fault diagnosis in two missing patterns, namely Interval Missing and Missing Completely At Random (MCAR), we used a customized experimental equipment dataset and open experiments. The NVIDIA Jetson Xavier NX suite served as the edge computing platform to verify the effectiveness and superiority of the proposed model. The results indicate an average improvement in classification accuracy of 8.07% and 9.65% on both datasets when compared to existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Statement Data will be made available on request.

References

  1. Guo J, He Q, Zhen D, Gu F, Ball AD (2023) Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis. Reliab Eng Syst Saf 230:108969. https://doi.org/10.1016/j.ress.2022.108969

    Article  Google Scholar 

  2. Guo J, He Q, Zhen D, Gu F (2023) Intelligent fault detection for rotating machinery using cyclic morphological modulation spectrum and hierarchical teager permutation entropy. IEEE Trans Industr Inf 19:6196–6207. https://doi.org/10.1109/TII.2022.3185293

    Article  Google Scholar 

  3. Guo S, Zhang B, Yang T, Lyu D, Gao W (2020) Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization. IEEE Trans Industr Electron 67:8005–8015. https://doi.org/10.1109/TIE.2019.2942548

    Article  MATH  Google Scholar 

  4. Goyal D, Mongia C, Sehgal S (2021) Applications of Digital Signal Processing in Monitoring Machining Processes and Rotary Components: A Review. IEEE Sens J 21:8780–8804. https://doi.org/10.1109/JSEN.2021.3050718

    Article  MATH  Google Scholar 

  5. Fang H, Deng J, Chen D, Jiang W, Shao S, Tang M, Liu J (2023) You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis. Adv Eng Inform 55:101890. https://doi.org/10.1016/j.aei.2023.101890

    Article  Google Scholar 

  6. Dzaferagic M, Marchetti N, Macaluso I (2022) Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data. IEEE Internet Things J 9:8892–8900. https://doi.org/10.1109/JIOT.2021.3116785

    Article  MATH  Google Scholar 

  7. Yu W, Zhao C (2021) Low-Rank Characteristic and Temporal Correlation Analytics for Incipient Industrial Fault Detection With Missing Data. IEEE Trans Ind Inf 17:6337–6346. https://doi.org/10.1109/TII.2020.2990975

    Article  MATH  Google Scholar 

  8. Fan J, Chow TWS, Qin SJ (2022) Kernel-Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data. IEEE Trans Industr Inf 18:4477–4487. https://doi.org/10.1109/TII.2021.3119377

    Article  MATH  Google Scholar 

  9. Ren Z, Lin T, Feng K, Zhu Y, Liu Z, Yan K (2023) A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis. IEEE Trans Instrum Meas 72:1–35. https://doi.org/10.1109/TIM.2023.3246470

    Article  MATH  Google Scholar 

  10. Zhang J, Cheng Y, He X (2023) Fault Diagnosis of Energy Networks Based on Improved Spatial–Temporal Graph Neural Network With Massive Missing Data. IEEE Transact Autom Sci Eng 1–12. https://doi.org/10.1109/TASE.2023.3281394

  11. Yang G, Tao H, Wu K, Du R, Zhong Y (2024) Fault Diagnosis of Harmonic Drives Using Multimodal Collaborative Meta Network With Severely Missing Modality. IEEE Transact Ind Inform 1–9. https://doi.org/10.1109/TII.2024.3396339

  12. Huang B, Zhu Y, Usman M, Chen H (2024) Semi-supervised learning with missing values imputation. Knowl-Based Syst 284:111171. https://doi.org/10.1016/j.knosys.2023.111171

    Article  MATH  Google Scholar 

  13. Alamoodi AH, Zaidan BB, Zaidan AA, Albahri OS, Chen J, Chyad MA, Garfan S, Aleesa AM (2021) Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation. Chaos, Solitons Fractals 151:111236. https://doi.org/10.1016/j.chaos.2021.111236

    Article  Google Scholar 

  14. Wells BJ, Nowacki AS, Chagin K, Kattan MW (2013) Strategies for Handling Missing Data in Electronic Health Record Derived Data. eGEMs 1:7. https://doi.org/10.13063/2327-9214.1035

    Article  Google Scholar 

  15. Cui Z, Ke R, Pu Z, Wang Y (2020) Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transport Res Part C: Emerg Technol 118:102674. https://doi.org/10.1016/j.trc.2020.102674

    Article  Google Scholar 

  16. Decorte T, Mortier S, Lembrechts JJ, Meysman FJR, Latré S, Mannens E, Verdonck T (2024) Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring. Sens 24(8):2416. https://doi.org/10.3390/s24082416

    Article  Google Scholar 

  17. Fang L, Xiang W, Zhou Y, Fang J, Chi L, Ge Z (2023) Dual-branch cross-dimensional self-attention-based imputation model for multivariate time series. Knowl-Based Syst 279:110896. https://doi.org/10.1016/j.knosys.2023.110896

    Article  MATH  Google Scholar 

  18. Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent Neural Networks for Multivariate Time Series with Missing Values. Sci Rep 8:6085. https://doi.org/10.1038/s41598-018-24271-9

    Article  Google Scholar 

  19. Li G, Wu J, Deng C, Chen Z (2022) Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments. ISA Trans 128:545–555. https://doi.org/10.1016/j.isatra.2021.10.023

    Article  MATH  Google Scholar 

  20. Yan R, Shang Z, Xu H, Wen J, Zhao Z, Chen X, Gao RX (2023) Wavelet transform for rotary machine fault diagnosis:10 years revisited. Mech Syst Signal Process 200:110545. https://doi.org/10.1016/j.ymssp.2023.110545

    Article  MATH  Google Scholar 

  21. Yang C, Cai B, Wu Q, Wang C, Ge W, Hu Z, Zhu W, Zhang L, Wang L (2023) Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data. J Ind Inf Integr 33:100469. https://doi.org/10.1016/j.jii.2023.100469

    Article  Google Scholar 

  22. Lu S, Lu J, An K, Wang X, He Q (2023) Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review. IEEE Internet Things J 10:11093–11116. https://doi.org/10.1109/JIOT.2023.3239944

    Article  MATH  Google Scholar 

  23. Fang L, Wan J, Cai H, Wang S, Pang Z, Safran M, AlQahtani SA (2024) A Scalable Cloud–Edge Collaborative Approach for Intelligent Low-Code Fault Diagnosis: Successful Applications of Agile Migration Deployment in Heterogeneous Fault Diagnosis Scenarios. IEEE Ind Electron Mag 2–15. https://doi.org/10.1109/MIE.2024.3391943

  24. Gao J, Heng F, Yuan Y, Liu Y (2024) A novel machine learning method for multiaxial fatigue life prediction: Improved adaptive neuro-fuzzy inference system. Int J Fatigue 178:108007. https://doi.org/10.1016/j.ijfatigue.2023.108007

    Article  Google Scholar 

  25. Wang Y, Yu Z, Wu J, Wang C, Zhou Q, Hu J (2024) Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing. IEEE Internet Things J 11:23156–23169. https://doi.org/10.1109/JIOT.2024.3387328

    Article  MATH  Google Scholar 

  26. An K, Wang X, Song J, Xie F, Lu S (2023) Compressed Channel-Based Edge Computing for Online Motor Fault Diagnosis With Privacy Protection. IEEE Trans Instrum Meas 72:1–12. https://doi.org/10.1109/TIM.2023.3314827

    Article  MATH  Google Scholar 

  27. Ding A, Qin Y, Wang B, Jia L, Cheng X (2023) Lightweight Multiscale Convolutional Networks With Adaptive Pruning for Intelligent Fault Diagnosis of Train Bogie Bearings in Edge Computing Scenarios. IEEE Trans Instrum Meas 72:1–13. https://doi.org/10.1109/TIM.2022.3231325

    Article  Google Scholar 

  28. Wang Y, Wu J, Yu Z, Hu J, Zhou Q (2023) A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenarios. Eng Appl Artif Intell 126:107091. https://doi.org/10.1016/j.engappai.2023.107091

    Article  MATH  Google Scholar 

  29. He C, Han P, Lu J, Wang X, Song J, Li Z, Lu S (2023) Real-Time Fault Diagnosis of Motor Bearing via Improved Cyclostationary Analysis Implemented onto Edge Computing System. IEEE Trans Instrum Meas 72:1–11. https://doi.org/10.1109/TIM.2023.3295476

    Article  MATH  Google Scholar 

  30. Wu Q, Ding X, Zhang Q, Liu R, Wu S, He Q (2023) An Intelligent Edge Diagnosis System Based on Multiplication-Convolution Sparse Network. IEEE Sens J 23:26753–26764. https://doi.org/10.1109/JSEN.2023.3304301

    Article  MATH  Google Scholar 

  31. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge Computing: Vision and Challenges. IEEE Internet Things J 3:637–646. https://doi.org/10.1109/JIOT.2016.2579198

    Article  MATH  Google Scholar 

  32. Yu W, Liu Y, Dillon T, Rahayu W (2023) Edge Computing-Assisted IoT Framework With an Autoencoder for Fault Detection in Manufacturing Predictive Maintenance. IEEE Trans Industr Inf 19:5701–5710. https://doi.org/10.1109/TII.2022.3178732

    Article  MATH  Google Scholar 

  33. Qian G, Lu S, Pan D, Tang H, Liu Y, Wang Q (2019) Edge Computing: A Promising Framework for Real-Time Fault Diagnosis and Dynamic Control of Rotating Machines Using Multi-Sensor Data. IEEE Sens J 19:4211–4220. https://doi.org/10.1109/JSEN.2019.2899396

    Article  MATH  Google Scholar 

  34. Wang T, Liang Y, Shen X, Zheng X, Mahmood A, Sheng QZ (2023) Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions. ACM Comput Surv 55:1–37

    MATH  Google Scholar 

  35. Fouladgar N, Främling K (2020) A Novel LSTM for Multivariate Time Series with Massive Missingness. Sens 20(10):2832. https://doi.org/10.3390/s20102832

    Article  MATH  Google Scholar 

  36. Lavin A, Gray S (2016) Fast algorithms for convolutional neural networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4013–4021. https://doi.org/10.1109/CVPR.2016.435

  37. Hendriks J, Dumond P, Knox DA (2022) Towards better benchmarking using the CWRU bearing fault dataset. Mech Syst Signal Proc 169:108732. https://doi.org/10.1016/j.ymssp.2021.108732

    Article  MATH  Google Scholar 

  38. Yoo Y, Jo H, Ban S-W (2023) Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset. Sensors 23:3157. https://doi.org/10.3390/s23063157

    Article  MATH  Google Scholar 

  39. Liu J-J, Yao J-P, Liu J-H, Wang Z-Y, Huang L (2024) Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning. Appl Intell 54:2528–2550. https://doi.org/10.1007/s10489-024-05314-3

    Article  MATH  Google Scholar 

  40. Liu J, Wan Z, Hu X, Zhu Q (2024) Safe drug recommendation through forward data imputation and recurrent residual neural network. Appl Soft Comput 161:111723. https://doi.org/10.1016/j.asoc.2024.111723

    Article  MATH  Google Scholar 

  41. Mim TR, Amatullah M, Afreen S, Yousuf MA, Uddin S, Alyami SA, Hasan KF, Moni MA (2023) GRU-INC: An inception-attention based approach using GRU for human activity recognition. Expert Syst Appl 216:119419. https://doi.org/10.1016/j.eswa.2022.119419

    Article  Google Scholar 

  42. Zhang Y, Zhou T, Huang X, Cao L, Zhou Q (2021) Fault diagnosis of rotating machinery based on recurrent neural networks. Meas 171:108774. https://doi.org/10.1016/j.measurement.2020.108774

    Article  MATH  Google Scholar 

  43. Zhang J, Tian J, Li M, Leon JI, Franquelo LG, Luo H, Yin S (2023) A Parallel Hybrid Neural Network With Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics. IEEE Trans Instrum Meas 72:1–12. https://doi.org/10.1109/TIM.2022.3227956

    Article  Google Scholar 

  44. Howard A, Sandler M, Chen B, Wang W, Chen L-C, Tan M, Chu G, Vasudevan V, Zhu Y, Pang R, Adam H, Le Q (2019) Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp 1314–1324 https://doi.org/10.1109/ICCV.2019.00140

Download references

Funding

This work was supported in part by the National Key R&D Program of China Young Scientists Project [Grant number 2022YFC2204700].

Author information

Authors and Affiliations

Authors

Contributions

Ziyang Yu: Conceptualization, Data curation, Methodology, Software, Visualization and Writing – original draft. Yanzhi Wang: Formal analysis, Project administration, Software, Writing – review & editing. Xiaofeng Zong: Formal analysis, Investigation, Resources, Supervision and Validation. Jinhong Wu: Data curation, Investigation and Software. Qi Zhou: Formal analysis, Funding acquisition, Supervision and Validation. ← 

Corresponding author

Correspondence to Qi Zhou.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Z., Wang, Y., Zong, X. et al. GRUDMU-DSCNN: An edge computing method for fault diagnosis with missing data. Appl Intell 55, 140 (2025). https://doi.org/10.1007/s10489-024-06104-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06104-7

Keywords