Abstract
Reliable condition monitoring methods are required for rotating machines operating under time-varying operating conditions. The measured vibration signals typically contain information related to the different interacting components (e.g. gear mesh components, bearing fault components), the transmission paths between the excitation sources and the sensors, the environmental conditions (e.g. changes in temperature) and the operating conditions of the machine. Hence, multiple sources could be present in the measured signals, which could impede the detection of weak sources attributed to incipient damage. Several methods have been proposed to solve this problem, including, synchronous statistics (e.g. time-synchronous averages, synchronous average of the squared envelope, synchronous median of the squared envelope), the squared envelope spectrum, the order-frequency spectral coherence and the integrated squared spectral coherence (e.g. the enhanced envelope spectrum and the improved envelope spectrum). Independent Component Analysis (ICA) is a well-established technique that has not been compared against the aforementioned methods. In this work, we compare the performance of ICA against the performance against established signal analysis methods for fault detection under time-varying operating conditions. We show that ICA performs well against established signal analysis-based condition monitoring methods for machines operating under time-varying conditions.
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Notes
- 1.
We refer to extraneous events as signal components (deterministic, stationary, or cyclostationary) that impede damage detection.
References
Salameh, J.P., Cauet, S., Etien, E., Sakout, A., Rambault, L.: Gearbox condition monitoring in wind turbines: a review. Mech. Syst. Sig. Process. 111, 251–264 (2018)
Kruczek, P., Zimroz, R., Antoni, J., Wyłomańska, A.: Generalized spectral coherence for cyclostationary signals with \(\alpha \)-stable distribution. Mech. Syst. Sig. Process. 159, 107737 (2021)
Randall, R.B., Antoni, J.: Rolling element bearing diagnostics - a tutorial. Mech. Syst. Sig. Process. 25, 485–520 (2011)
Abboud, D., Baudin, S., Antoni, J., Rémond, D., Eltabach, M., Sauvage, O.: The spectral analysis of cyclo-non-stationary signals. Mech. Syst. Sig. Process. 75, 280–300 (2016)
Schmidt, S., Heyns, P.S., Gryllias, K.C.: An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions. Mech. Syst. Sig. Process. 158, 107771 (2021)
Schmidt, S., Zimroz, R., Heyns, P.S.: Enhancing gearbox vibration signals under time-varying operating conditions by combining a whitening procedure and a synchronous processing method. Mech. Syst. Sig. Process. 156, 107668 (2021)
Abboud, D., Antoni, J., Sieg-Zieba, S., Eltabach, M.: Envelope analysis of rotating machine vibrations in variable speed conditions: a comprehensive treatment. Mech. Syst. Sig. Process. 84, 200–226 (2017)
Antoni, J., Xin, G., Hamzaoui, N.: Fast computation of the spectral correlation. Mech. Syst. Sig. Process. 92, 248–277 (2017)
Schmidt, S., Zimroz, R., Chaari, F., Heyns, P.S., Haddar, M.: A simple condition monitoring method for gearboxes operating in impulsive environments. Sensors 20, 2115 (2020)
Comon, P.: Independent component analysis, a new concept? Sig. Process. 36, 287–314 (1994)
He, Q., Feng, Z., Kong, F.: Detection of signal transients using independent component analysis and its application in gearbox condition monitoring. Mech. Syst. Sig. Process. 21, 2056–2071 (2007)
Tian, X., Lin, J., Fyfe, K.R., Zuo, M.J.: Gearbox fault diagnosis using independent component analysis in the frequency domain and wavelet filtering. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. (ICASSP 2003), vol. 2, pp. II-245. IEEE (2003)
Wodecki, J., Stefaniak, P., Sawicki, M., Zimroz, R.: Application of independent component analysis in temperature data analysis for gearbox fault detection. In: Cyclostationarity: Theory and Methods III, pp. 187–198. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51445-1_11
Albarbar, A., Gu, F., Ball, A.: Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis. Measurement 43, 1376–1386 (2010)
Zuo, M.J., Lin, J., Fan, X.: Feature separation using ICA for a one-dimensional time series and its application in fault detection. J. Sound Vibr. 287, 614–624 (2005)
Duan, F., Corsar, M., Zhou, L., Mba, D.: Using independent component analysis scheme for helicopter main gearbox bearing defect identification. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 252–259. IEEE (2017)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10, 626–634 (1999)
Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Proceedings of the 8th International Conference on Neural Information Processing Systems, NIPS 1995, (Cambridge, MA, USA), pp. 757–763. MIT Press (1995)
Lee, T.-W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11, 417–441 (1999)
Borghesani, P., Smith, W., Zhang, X., Feng, P., Antoni, J., Peng, Z.: A new statistical model for acoustic emission signals generated from sliding contact in machine elements. Tribol. Int. 127, 412–419 (2018)
Peeters, C., et al.: Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data. Mech. Syst. Sig. Process. 129, 407–436 (2019)
Borghesani, P., Pennacchi, P., Randall, R., Sawalhi, N., Ricci, R.: Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mech. Syst. Sig. Process. 36, 370–384 (2013)
Mauricio, A., Smith, W.A., Randall, R.B., Antoni, J., Gryllias, K.: Improved envelope spectrum via feature optimisation-gram (IESFOgram): a novel tool for rolling element bearing diagnostics under non-stationary operating conditions. Mech. Syst. Sig. Process. 144, 106891 (2020)
Smith, W.A., Borghesani, P., Ni, Q., Wang, K., Peng, Z.: Optimal demodulation-band selection for envelope-based diagnostics: a comparative study of traditional and novel tools. Mech. Syst. Sig. Process. 134, 106303 (2019)
Schmidt, S., Gryllias, K.C.: Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions. Measurement. 169, 108517 (2021)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Schmidt, S., Wilke, D.N., Heyns, P.S. (2022). A Comparison Between Independent Component Analysis and Established Signal Processing Methods for Gearbox Fault Diagnosis Under Time-Varying Operating Conditions. In: Hammami, A., Heyns, P.S., Schmidt, S., Chaari, F., Abbes, M.S., Haddar, M. (eds) Modelling and Simulation of Complex Systems for Sustainable Energy Efficiency. MOSCOSSEE 2021. Applied Condition Monitoring, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-85584-0_21
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