Designing Expert System for Centrifugal using Vibration Signal and Bayesian Networks

Dedik Romahadi, Alief A. Luthfie, Wiwit Suprihatiningsih, Hui Xiong


Centrifugal machines are crucial in the process of making sugar. It requires proper centrifugal maintenance so that the production process runs smoothly. Efficiency and productivity are, therefore, critical factors in producing high-quality sugar. Centrifuge damages may occur, suddenly causing huge losses. Therefore, predictive treatment is essential. To achieve this, vibration analysis is a reliable and easy method to determine the vibration levels of spinning machines such as centrifuges. Simply by attaching the sensor to the outside of the engine, the engine condition will be read. Unfortunately, not all employees understand how to read vibration measurement data. Even experts need time to analyze the vibration signal. Therefore, the purpose of this study was to design an expert system that diagnoses centrifugal vibrations using the Bayesian Network. The vibration analysis process will be employed in the network using a series of complex nodes and trained according to the reference spectrum analysis. The presence or absence of spectral lines is evidence of Bayesian Network input in updating information regarding centrifugal damage. The result shows that the Bayesian network method was successfully applied to diagnose centrifuges based on vibration data. The inputs in the form of 1X and 2X produced an Unbalance probability value of 90%, Misalignment 93.2%, Looseness 57.38%, Bearing 33.79%, Pulley 50.3%, and produce a centrifugal damage probability of 7.86%. Therefore, these values were the actual conditions of the vibration data.


Centrifuge; vibration analysis; vibration spectrum; Bayesian networks.

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Y. Fu, B. J. Gu, J. Wang, J. Gao, G. M. Ganjyal, and M. P. Wolcott, “Novel micronized woody biomass process for production of cost-effective clean fermentable sugars,” Bioresour. Technol., vol. 260, pp. 311–320, Jul. 2018, doi: 10.1016/j.biortech.2018.03.096.

I. Prasertsung, K. Aroonraj, K. Kamwilaisak, N. Saito, and S. Damrongsakkul, “Production of reducing sugar from cassava starch waste (CSW) using solution plasma process (SPP),” Carbohydr. Polym., vol. 205, pp. 472–479, Feb. 2019, doi: 10.1016/j.carbpol.2018.10.090.

J. Chen, X. Cai, E. Lale, J. Yang, and G. Cusatis, “Centrifuge modeling testing and multiscale analysis of cemented sand and gravel (CSG) dams,” Constr. Build. Mater., vol. 223, pp. 605–615, Oct. 2019, doi: 10.1016/j.conbuildmat.2019.06.218.

G. Chen, M. Liu, and J. Chen, “Frequency-temporal-logic-based bearing fault diagnosis and fault interpretation using Bayesian optimization with Bayesian neural networks,” Mech. Syst. Signal Process., vol. 145, p. 106951, Nov. 2020, doi: 10.1016/j.ymssp.2020.106951.

C. Zhou et al., “Vibration singularity analysis for milling tool condition monitoring,” Int. J. Mech. Sci., vol. 166, p. 105254, Jan. 2020, doi: 10.1016/j.ijmecsci.2019.105254.

Y. Zhao, J. Pan, Z. Huang, Y. Miao, J. Jiang, and Z. Wang, “Analysis of vibration monitoring data of an onshore wind turbine under different operational conditions,” Eng. Struct., vol. 205, p. 110071, Feb. 2020, doi: 10.1016/j.engstruct.2019.110071.

D. Romahadi, A. A. Luthfie, and L. B. Desti Dorion, “Detecting classifier-coal mill damage using a signal vibration analysis,” SINERGI, vol. 23, no. 3, p. 175, Sep. 2019, doi: 10.22441/sinergi.2019.3.001.

D. Romahadi, H. Xiong, and H. Pranoto, “Intelligent system for gearbox fault detection & diagnosis based on vibration analysis using Bayesian Networks,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 694, doi: 10.1088/1757-899X/694/1/012001.

J. Jiang, H. Yang, G. Chen, and K. Wang, “Numerical and experimental analysis on the vibration and radiated noise of the cylinder washing machine,” Appl. Acoust., p. 107747, Nov. 2020, doi: 10.1016/j.apacoust.2020.107747.

T. Wang, Q. Han, F. Chu, and Z. Feng, “Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review,” Mech. Syst. Signal Process, vol. 126. Academic Press, pp. 662–685, Jul. 01, 2019, doi: 10.1016/j.ymssp.2019.02.051.

L. Tian, T. Ye, and G. Jin, “Vibration analysis of combined conical-cylindrical shells based on the dynamic stiffness method,” Thin-Walled Struct., p. 107260, Nov. 2020, doi: 10.1016/j.tws.2020.107260.

X. Lu et al., “Prediction and analysis of cold rolling mill vibration based on a data-driven method,” Appl. Soft Comput. J., vol. 96, p. 106706, Nov. 2020, doi: 10.1016/j.asoc.2020.106706.

M. Y. Asr, M. M. Ettefagh, R. Hassannejad, and S. N. Razavi, “Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach,” Mech. Syst. Signal Process., vol. 85, pp. 56–70, Feb. 2017, doi: 10.1016/j.ymssp.2016.08.005.

J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, “Prognostics and health management design for rotary machinery systems - Reviews, methodology and applications,” Mech. Syst. Signal Process., vol. 42, no. 1–2, pp. 314–334, Jan. 2014, doi: 10.1016/j.ymssp.2013.06.004.

A. W. Biantoro, H. Maryanto, A. K. Hidayanto, and A. Hamid, “The investigation of end mill feeds on CNC router machine using vibration method,” SINERGI, vol. 24, no. 2, p. 117, Apr. 2020, doi: 10.22441/sinergi.2020.2.005.

M. Hu et al., “A machine learning bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system,” Energy Build., vol. 158, pp. 668–676, Jan. 2018, doi: 10.1016/j.enbuild.2017.10.012.

S. Kabir and Y. Papadopoulos, “Applications of bayesian networks and petri nets in safety, reliability, and risk assessments: A review,” Safety Science, vol. 115. Elsevier B.V., pp. 154–175, Jun. 01, 2019, doi: 10.1016/j.ssci.2019.02.009.

T. Huang and K. U. Schröder, “Bayesian probabilistic damage characterization based on a perturbation model using responses at vibration nodes,” Mech. Syst. Signal Process., vol. 139, p. 106444, May 2020, doi: 10.1016/j.ymssp.2019.106444.

A. R. Sahu and S. K. Palei, “Real-time fault diagnosis of HEMM using Bayesian Network: A case study on drag system of dragline,” Eng. Fail. Anal., vol. 118, p. 104917, Dec. 2020, doi: 10.1016/j.engfailanal.2020.104917.

G. Sharma and R. N. Rai, “Modeling and analysis of factors affecting repair effectiveness of repairable systems using Bayesian network,” Appl. Soft Comput. J., vol. 92, p. 106261, Jul. 2020, doi: 10.1016/j.asoc.2020.106261.

B. Chen, L. Xie, Y. Li, and B. Gao, “Acoustical damage detection of wind turbine yaw system using Bayesian network,” Renew. Energy, vol. 160, pp. 1364–1372, Nov. 2020, doi: 10.1016/j.renene.2020.07.062.

T. Adedipe, M. Shafiee, and E. Zio, “Bayesian Network Modelling for the Wind Energy Industry: An Overview,” Reliability Engineering and System Safety, vol. 202. Elsevier Ltd, p. 107053, Oct. 01, 2020, doi: 10.1016/j.ress.2020.107053.

D. Lee and D. Choi, “Analysis of the reliability of a starter-generator using a dynamic Bayesian network,” Reliab. Eng. Syst. Saf., vol. 195, p. 106628, Mar. 2020, doi: 10.1016/j.ress.2019.106628.

B. R. Cobb and L. Li, “Bayesian network model for quality control with categorical attribute data,” Appl. Soft Comput., vol. 84, p. 105746, Nov. 2019, doi: 10.1016/j.asoc.2019.105746.

J. Pearl, Bayesian networks: A model of self-activated: memory for evidential reasoning. Los Angeles: UCLA, 1985.

J. Pearl, “Fusion, propagation, and structuring in belief networks,” Artif. Intell., vol. 29, no. 3, pp. 241–288, 1986, doi: 10.1016/0004-3702(86)90072-X.

D. Lee and R. Pan, “A nonparametric Bayesian network approach to assessing system reliability at early design stages,” Reliab. Eng. Syst. Saf., vol. 171, pp. 57–66, Mar. 2018, doi: 10.1016/j.ress.2017.11.009.

X. Li, Y. Yang, L. Li, G. Zhao, and N. He, “Uncertainty quantification in machining deformation based on Bayesian network,” Reliab. Eng. Syst. Saf., vol. 203, p. 107113, Nov. 2020, doi: 10.1016/j.ress.2020.107113.

W. Huang, Y. Zhang, X. Kou, D. Yin, R. Mi, and L. Li, “Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach,” Reliab. Eng. Syst. Saf., vol. 204, p. 107220, Dec. 2020, doi: 10.1016/j.ress.2020.107220.

S. Fan, J. Zhang, E. Blanco-Davis, Z. Yang, and X. Yan, “Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS,” Ocean Eng., vol. 210, p. 107544, Aug. 2020, doi: 10.1016/j.oceaneng.2020.107544.

D. A. Quintanar-Gago, P. F. Nelson, Á. Díaz-Sánchez, and M. S. Boldrick, “Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network,” Reliab. Eng. Syst. Saf., p. 107329, Nov. 2020, doi: 10.1016/j.ress.2020.107329.



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