Abstract
Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on the original data, which largely depends on the selected sensors and signal features. Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results. These concerns significantly limit the effectiveness of modern machine learning and thus should be investigated. Meanwhile, ball bearings are fundamental key machine elements in rotating machinery and their condition monitoring should be critical for both quality control and longevity assessment. In this paper, by utilizing ball bearing failure diagnosis as the main theme, the flow of feature selection and evaluation, as well as the evaluation flow for multiple failure diagnosis, is developed for accessing the status of bearings in their imbalance, lubrication, and grease contamination levels based on unsupervised machine learning. The experimental results indicated that with proper feature selection, the failure identification could be more definite. Finally, a novel model based on the second norm to quantify the classification level of each cluster in hyperspace is proposed as the measure for unsupervised machine learning as the basis for performance evaluation and optimization of unsupervised machine learning schemes and should benefit related machine reliability evaluation studies and applications.
Similar content being viewed by others
Availability of data and material
Available upon request.
Code availability
Available upon request.
References
Barlow RE, Hunter LC (1960) Optimum preventive main-tenance policies. Oper Res 8:90–100
Pham H, Wang H (1996) Imperfect maintenance. Eur J Oper Res 94:425–438
Wang H (2002) A survey of maintenance policies of deteriorating systems. Eur J Oper Res 139:469–489
Qin J, Liu Y, Grosvenor R (2016) A categorical framework of manufacturing for industry 4.0 and beyond. Procedia Cirp 52:173–178
Wang K (2016) Intelligent predictive maintenance (IPdM) system–Industry 4.0 scenario. WIT Trans Eng Sci 113:259–268
Zaretsky E, Poplawski J, Miller CR (2000) Rolling bearing life prediction: past, present, and future. NASA Tech Rep 210529
Zaretsky E (2013) Rolling bearing life prediction, theory, and application. NASA Tech Rep 2013-215305
Harris TA, McCool JI (1996) On the accuracy of rolling bearing fatigue life prediction. ASME J Tribol 118:297–309
Mishra C, Samantaray A, Chakraborty G (2017) Ball bearing defect models: a study of simulated and experimental fault signatures. J Sound Vib 400:86–112
Gao Q, Duan C, Fan H, Meng Q (2008) Rotating machine fault diagnosis using empirical mode decomposition. Mech Syst Signal Process 22:1072–1081
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11:2300–2312
Schoen RR, Habetler TG, Kamran F, Bartfield RG (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 31:1274–1279
Elasha F, Greaves M, Mba D (2018) Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission. Struct Health Monit 17:1192–1212
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26
Sutrisno E, Oh H, Vasan A, Pecht M (2012) Estimation of remaining useful life of ball bearings using data driven methodologies. Proc Prognostics Health Manag 1–7
Guo L, Li N, Jia F, Lei Y, Lin J (2017) A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240:98–109
Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Industr Electron 47:1060–1069
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38:1876–1886
Ren L, Sun Y, Cui J, Zhang L (2018) Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. J Manuf Syst 48:71–77
Lu W, Li Y, Cheng Y, Meng D, Liang B, Zhou P (2018) Early fault detection approach with deep architectures. IEEE Trans Instrument Measure 67:1679–1689
Ma M, Sun C, Chen X (2018) Deep coupling autoencoder for fault diagnosis with multimodal sensory data. IEEE Trans Ind Inform 14:1137–1145
Deng J, Zhang Z, Eyben F, Schuller B (2014) Autoencoder-based unsupervised domain adaptation for speech emotion recognition. IEEE Signal Process Lett 21(9):1068–1072
Lore KG, Akintayo A, Sarkar S (2017) LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662
Merrill N, Eskandarian A (2020) Modified autoencoder training and scoring for robust unsupervised anomaly detection in deep learning. IEEE Access 8:101824–101833
Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204
Lee N, Azarian M, Pecht M (2020) Octave-band filtering for convolutional neural network-based diagnostics for rotating machinery. Ann Conf PHM Soc 12:9
Sun I-C, Cheng R-C, Chen K-S (2021) Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis. Int J Adv Manuf. https://doi.org/10.1007/s00170-021-08526-w
Liu Y, Li Z, Xiong H, Gao X, Wu J, Wu S (2013) Understanding and enhancement of internal clustering validation measures. IEEE Trans Cybern 43:982–994
Cheng RC, Chen KS, Liu YH, Chang LK, Tsai MC (2021) Development of autoencoder-based status diagnosis method for ball bearing tribology status monitoring. Proc. 9th IIAE International Conference on Industrial Application Engineering 2021 (ICIAE 2021), Kitakyushu, Japan, p. 45–52
Cheng RC (2021) Development of autoencoder-based unsupervised fault recognition method for application in bearing condition diagnosis. Master Thesis, National Cheng-Kung University, Taiwan
Schervish MJ (2012) Theory of statistics. Springer Science & Business Media
Jia F, Lei Y, Lu N, Xing S (2018) Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech Syst Signal Process 110:349–367
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52
Subramanian V (2018) Deep learning with PyTorch: a practical approach to building neural network models using PyTorch. Packt Publishing Ltd
Ding Y, Ma L, Ma J, Suo M, Tao L, Cheng Y, Lu C (2019) Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: a deep reinforcement learning approach. Adv Eng Inform 42:100977
Pihlgren GG, Sandin F, Liwicki M (2020) Improving image autoencoder embeddings with perceptual loss. Proc. 2020 IEEE International Joint Conference on Neural Networks (IJCNN), p. 1–7
Abouzid H, Chakkor O (2020) Autoencoders in deep neural network architecture for real work applications: convolutional denoising autoencoders. In Handbook of Research on Recent Developments in Electrical and Mechanical Engineering, IGI Global, p. 214–236
Tsai JM, Sun IC, Chen KS (2021) Realization and performance evaluation of a machine tool vibration monitoring module by multiple MEMS accelerometer integrations. Int J Adv Manuf Technol 114:465–479
Acknowledgements
Technical supports from Prof. M-C Tsai of NCKU are also greatly appreciated.
Funding
This study was financially supported by the Ministry of Science and Technology (MOST) of Taiwan government with project IDs: 109-2622-8-006-005, 108-2221-E-006-209108-2221-E-006-209, and 110-2221-E-006-172, is acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
All the authors agree with the publication.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cheng, RC., Chen, KS. Ball bearing multiple failure diagnosis using feature-selected autoencoder model. Int J Adv Manuf Technol 120, 4803–4819 (2022). https://doi.org/10.1007/s00170-022-09054-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-09054-x