Abstract
Intelligent manufacturing poses a challenge for fault diagnosis of rotor systems to meet the three tasks: whether exists faults, faults location and quantitative diagnosis. Traditional methods hardly meet all the three tasks in online fault diagnosis. This paper proposes a modified classification and regression tree (CART) algorithm named D-CART algorithm to provide much faster fault classification by reducing the iteration times in computation while still ensuring accuracy. Experiments are carried on to achieve a comprehensive online fault diagnosis for rotor systems such as faults location, faults types and quantitative analysis of unbalanced mass in this paper. In comparison with the other 4 novel CART-based algorithms, the experimental results indicate that the speed of D-CART algorithm is improved by a factor of 23.92 compared to the fastest improved algorithm (Adaboost-CART) and a model accuracy of up to 96.77%. Thus demonstrating the speed superiority of D-CART algorithm in both diagnosing locations of different faults types and determining the loading masses of unbalanced faults. The proposed method has the potential to realize high-accuracy online fault diagnosis for rotor systems.
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References
Gungor VC, Networks GPH (2009) Industrial wireless sensor challenges, design principles, and technical approaches. IEEE Trans Indust Electron 56(10):4258–4265
Bae YH, Lee SH, Kim HC, Lee BR, Jang JJ, Lee J (2006) A real-time intelligent multiple fault diagnostic system. Int J Adv Manuf Technol 29(5):590–597
Cerrada M, Sanchez R-V, Pacheco F, Cabrera D, Zurita G, Li C (2016) Hierarchical feature selection based on relative dependency for gear fault diagnosis. Appl Intell 44(3):687–703
Wang C, Gan M, Chang’an Z (2018) Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory. J Intell Manuf 29(4):937–951
Lautre NK, Manna A (2006) A study on fault diagnosis and maintenance of CNC-WEDM based on binary relational analysis and expert system. Int J Adv Manuf Technol 29(5):490–498
Lees AW, Sinha JK, Friswell MI (2009) Model-based identification of rotating machines. Mech Syst Signal Process 23(6):1884–1893
Xue Y, Li Z, Wang B, Zhang Z, Li F (2018) Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis. Appl Intell 48(10):3306–3331
Patil MS, Mathew J, Rajendrakumar PK, Desai S (2010) A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing. Int J Mech Sci 52(9, SI):1193–1201
Rashid Md, Amar M, Gondal I, Kamruzzaman J (2016) Mamunur a data mining approach for machine fault diagnosis based on associated frequency patterns. Appl Intell 45(3):638–651
Li B-h, Hou B-c, Yu W-t, Lu X-b, Yang C-w (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Front Inf Technol Electron Eng 18(1):86–96
Seera M, Lim CP, Ishak D, Singh H (2013) Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model. Appl Soft Comput 13(12):4493–4507
Seera M, Lim CP (2014) Online motor fault detection and diagnosis using a hybrid FMM-CART model. IEEE Trans Neural Netw Learn Syst 25(4):806–812
Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72-73:303–315
Demetgul M (2013) Fault diagnosis on production systems with support vector machine and decision trees algorithms. Int J Adv Manuf Technol 67(9-12):2183–2194
Cernak M (2010) A comparison of decision tree classifiers for automatic diagnosis of speech recognition errors. Comput Inf 29(3):489–501
Breiman L (2001) Random forests. Mach Learn 45:5–32
Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and Regression Trees. CRC Press, Boca Raton
Li H, Sun J, Wu J (2010) Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods. Expert Syst Appl 37 (8):5895–5904
Liu J, Boyle LN, Banerjee AG (2018) Predicting interstate motor carrier crash rate level using classification models. Accid Anal Prevent 120:211–218
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Quinlan JR (1996) Bagging, boosting, and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference. AAAI, vol 1, pp 725–30. Proceedings of National Conference on Artificial Intelligence, 4-8 Aug. 1996, Portland, OR, USA
Hurst KD, Habetler TG (1997) A comparison of spectrum estimation techniques for sensorless speed detection in induction machines. IEEE Trans Ind Appl 33:898–905
Upton A, Jefferson B, Moore G, Jarvis P (2017) Rapid gravity filtration operational performance assessment and diagnosis for preventative maintenance from on-line data. Chem Eng J 313:250–260
Zhu X, Zhang Y, Zhu Y (2012) Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features. J Mech Sci Technol 26(9):2649–2657
Lu N, Xiao Z, Malik OP (2015) Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 52-53:393–415
Li G, Hu Y, Chen H, Wang J, Guo Y, Liu J, Li J (2017) Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system: A data mining approach. Energy Build 146:257–270
Ahmad I, Mabuchi H, Kano M, Hasebe S, Inoue Y, Uegaki H (2013) Data-Based ground fault diagnosis of power cable systems SICE. J Control Measur Syst Integr 6:290–7
Li G, Chen H, Hu Y, Wang J, Guo Y, Liu J, Li H, Huang R, Lv H, Li J (2018) An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators. Appl Therm Eng 129:1292–1303
Tran VT, Yang B-S, Oh M-S, Tan ACC (2009) Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Syst Appl 36(2):1840–1849
Gopinath R, Santhosh Kumar C, Ramachandran KI, Upendranath V, Sai Kiran PVR (2016) Intelligent fault diagnosis of synchronous generators. Expert Syst Appl 45:142–149
Seera M, Lim CP, Loo CK (2016) Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning. J Intell Manuf 27(6):1273–1285
Seera M, Lim CP, Ishak D, Singh H (2012) Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model. IEEE Trans Neural Netw Learn Syst 23(1):97–108
Zhang C, Liu C, Zhang X, Almpanidis G (2017) An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst Appl 82:128–150
Zhang C, Bi J, Xu S, Enislay R, Fan G, Qiao B, Hamido F (2019) Multi-imbalance: An open-source software for multi-class imbalance learning. Knowledge-Based Systems
Yarveicy H, Ghiasi MM, Mohammadi AH (2018) Performance evaluation of the machine learning approaches in modeling of CO2 equilibrium absorption in Piperazine aqueous solution. J Mol Liq 255:375–383
Hassan AR (2016) Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Process Control 29:22–30
Ranaie M, Soffianian A, Pourmanafi S, Mirghaffari N, Tarkesh M (2018) Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape. Adv Space Res 61(6):1558–1572
Seera M, Lim CP, Tan SC (2018) A hybrid FAM-CART model for online data classification. Comput Intell 34(2):562–581
Acknowledgements
The authors appreciate the support of the National Natural Science Foundation of China (Nos. 51575156, 51675156, 51775164, and 51705122) and the Fundamental Research Funds for the Central Universities (Nos. JZ2017HGPA0165, PA2017GDQT0024).
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Huaxia Deng, Yifan Diao and Wei wu conceived the idea and conducted the experiments. All the authors including Huaxia Deng, Yifan Diao, Wei wu, Jin Zhang, Mengchao Ma and Xiang Zhong contributed to the discussion of the paper and approved the manuscript. Huaxia Deng directed the scientific research of this work.
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Deng, H., Diao, Y., Wu, W. et al. A high-speed D-CART online fault diagnosis algorithm for rotor systems. Appl Intell 50, 29–41 (2020). https://doi.org/10.1007/s10489-019-01516-2
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DOI: https://doi.org/10.1007/s10489-019-01516-2