Abstract
Motivated by the rapid development of the next generation artificial intelligence, we propose a novel adaptive self-detection and self-classification algorithm using matrix eigenvector trajectory in the paper. This algorithm’s mathematical inferences are also described and proved theoretically. The proposed algorithm is used in a multi-class bearing faults classification problem to validate its effectiveness. Results show that in an online data processing scenario, it can automatically adapt to new data patterns so that self-detection and self-classification can be realized by monitoring the eigenvector evolution trajectory. By comparing with other machine learning algorithms, we have validated that the proposed algorithm does not require explicit training, its required data processing time dropped more than 78% and achieved the same classification accuracy on new testing data.
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References
Wijethilaka, S., Liyanage, M.: Survey on network slicing for Internet of Things realization in 5G networks. IEEE Commun. Surv. Tutor. 23(2), 957–994 (2021)
Khuntia, M., Singh, D., Sahoo, S.: Impact of Internet of Things (IoT) on 5G. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds.) Intelligent and Cloud Computing, vol. 153, pp. 125–136. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-6202-0_14
Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 1–21 (2021). https://doi.org/10.1007/s42979-021-00592-x
Artificial intelligence and machine learning: the next generation (2019)
https://brighterion.com/next-generation-artificial-intelligence-machine-learning
Basha, A.J., Balaji, B.S., Poornima, S., Prathilothamai, M., Venkatachalam, K.: Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel. J. Ambient Intell. Humaniz. Comput. 12(6), 6189–6197 (2020). https://doi.org/10.1007/s12652-020-02188-4
Gao, C., Elzarka, H.: The use of decision tree based predictive models for improving the culvert inspection process. Adv. Eng. Inform. 47, 101203 (2021)
Tervonen, J., et al.: Personalized mental stress detection with self-organizing map: from laboratory to the field. Comput. Biol. Med. 124, 103935 (2020)
Wang, Y., Guo, Y.: Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost. China Commun. 17(3), 205–221 (2020)
Entezami, A., Sarmadi, H., Behkamal, B., Mariani, S.: Big data analytics and structural health monitoring: a statistical pattern recognition-based approach. Sensors 20(8), 2328 (2020)
Mahmoudi, M.R., Baleanu, D., Mansor, Z., Tuan, B.A., Pho, K.-H.: Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries. Chaos Solitons Fractals 140, 110230 (2020)
Yu, C., Han, R., Song, M., Liu, C., Chang, C.-I.: A simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial–spectral fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2485–2501 (2020)
Chen, H., et al.: A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric. Water Manag. 240, 106303 (2020)
Wang, Q., Bu, S., He, Z.: Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Trans. Industr. Inf. 16(10), 6509–6517 (2020)
Partridge, M., Calvo, R.A.: Fast dimensionality reduction and simple PCA. Intell. Data Anal. 2(3), 203–214 (1998)
Gryllias, K.C., Antoniadis, I.A.: A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Eng. Appl. Artif. Intell. 25(2), 326–344 (2012)
Huang, Y., Liu, C., Zha, X.F., Li, Y.: A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion. Expert Syst. Appl. 37(5), 3815–3822 (2010)
Ocak, H., Loparo, K.A.: Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mech. Syst. Sig. Process. 18(3), 515–533 (2004)
Eren, L., Devaney, M.J.: Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans. Instrum. Meas. 53(2), 431–436 (2004)
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Jiang, C., Chen, L. (2023). An Adaptive Self-detection and Self-classification Approach Using Matrix Eigenvector Trajectory. In: Tang, L.C., Wang, H. (eds) Big Data Management and Analysis for Cyber Physical Systems. BDET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-031-17548-0_12
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DOI: https://doi.org/10.1007/978-3-031-17548-0_12
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