Abstract
In this paper, a method for equipment fault diagnosis of gearbox using principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method is to study and monitor the working state of the gearbox by studying the original vibration signal of the gearbox, and establish a corresponding experimental model by using the normal gear and the fault gear, respectively. Firstly, the vibration signal of the gearbox is preprocessed by wavelet packet transform (WPT). Then the time domain signal analysis method is used to extract the characteristic parameters of the vibration signal and the data is reduced by PCA. After the data are reduced in dimension, the principal element with the highest contribution rate are selected as the input parameter of SPRT. Test parameters to verify the proposed SPRT algorithm and Root Mean Square Error (RMSE). The results show that the proposed method is effective and practical.
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References
Zeng, L., Chen, G., Chen, H.: Comparative study on flow-accelerated corrosion and erosion-corrosion at a 900 carbon steel bend. Materials 12, 1780 (2020). https://doi.org/10.3390/ma13071780
Yang, L., Chen, H.: Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Comput. Appl. 31(9), 4463–4478 (2018). https://doi.org/10.1007/s00521-018-3525-y
Chen, H.: Model-based method with nonlinear ultrasonic system identification for mechanical structural health assessment. Trans. Emerg. Telecommun. Technol. e3955 (2020). http://doi.org/10.1002/ett.3955
Chen, H.: Nonlinear Lamb wave analysis for microdefect identification in mechanical structural health assessment. Measurement 164, 108026 (2020). https://doi.org/10.1016/j.measurement.2020.108026
Zeng, L., Guo, X.P., Zhang, G.A., Chen, H.X.: Semiconductivities of passive films formed on stainless steel bend under erosion-corrosion conditions. Corros. Sci. 144, 258–265 (2018)
Zeng, L., Shi, J., Luo, J., Chen, H.: Silver sulfide anchored on reduced graphene oxide as a high -performance catalyst for CO2 electroreduction. J. Power Sources 398, 83–90 (2018)
Chen, H., Chen, Y., Yang, L.: Intelligent early structural health prognosis with nonlinear system identification for RFID signal analysis. Comput. Commun. 157, 150–161 (2020). https://doi.org/10.1016/j.comcom.2020.04.026
Yang, L., Hanxin, C.: A novel time-frequency-space method with parallel factor theory for big data analysis in condition monitoring of complex system. Int. J. Adv. Robot. Syst. 17(2) (2020). https://doi.org/10.1177/1729881420916948
Ding, J., Zhao, L., Huang, D.R.: Incipient fault feature extraction method of gearbox based on wavelet package and PCA. In: IEEE International Conference on Data Driven Control and Learning Systems, pp. 656–660 (2017)
Chen, H., Fan, D., Huang, J., Huang, W.: Finite element analysis model on ultrasonic phased array technique for material defect time of flight diffraction detection. Sci. Adv. Mater. 12(5), 665–675 (2020)
Ray, A.: Sequential testing for fault detection in multiply-redundant systems. J. Dyn. Syst. Meas. Control Trans. ASME 111(2), 329–332 (1989)
Hanxin, C., Dong, L.F., Lu, F.: Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis. Int. J. Pattern Recognit. Artif. Intell. https://doi.org/10.1142/S0218001420580124
Hanxin, C., Wenjian, H., **min, H.: Multi-fault condition monitoring of slurry pump with principle component analysis and sequential hypothesis test. Int. J. Pattern Recognit. Artif. Intell. https://doi.org/10.1142/S0218001420590193
He, Y., **ong, W., Chen, H.: Image quality enhanced recognition of laser cavity based on improved random hough transform. J. Vis. Commun. Image Representation (2020). https://doi.org/10.1016/j.jvcir.2019.102679
Acknowledgement
The experimental data is provided by the Reliability Research Lab in the Department of Mechanical Engineering at the University of Alberta in Canada. This work was supported by the National Natural Science Foundation of China (Grant 51775390).
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Chen, H., Huang, L., Miao, Y., Wang, Q., Yang, L., Ke, Y. (2020). Model-Based Intelligent Non-linear Signal Recognition for Gearbox Condition Monitoring. In: Yuan, X., Elhoseny, M., Shi, J. (eds) Urban Intelligence and Applications. ICUIA 2020. Communications in Computer and Information Science, vol 1319. Springer, Singapore. https://doi.org/10.1007/978-981-33-4601-7_10
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DOI: https://doi.org/10.1007/978-981-33-4601-7_10
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