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Entropy measures for early detection of bearing faults

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  • Leite, Gustavo de Novaes Pires
  • Araújo, Alex Maurício
  • Rosas, Pedro André Carvalho
  • Stosic, Tatijana
  • Stosic, Borko

Abstract

This paper investigates the performance of the 12 entropy-based features for the monitoring and detection of bearing faults. These entropy measures were proposed both in time, frequency and time–frequency domain. Probability mass function (PMF) was extracted from the time waveforms using four different methods: (i) via power spectral density, (ii) via ordinal pattern distribution, (iii) via wavelet packet tree and iv) ensemble empirical mode decomposition. Three different entropy measures were used in the article: (i) Shannon entropy, (ii) Rényi entropy and (iii) Jensen–Rényi divergence. A new bearing produces a vibration time series characterised by random noise without prominent periodic content. As soon as a fault develops, impulses are produced, what excites structural resonances generating a train of impulse responses. As defect grows, it becomes a distributed fault, and then no sharp impulses are generated but rather an amplitude modulated random noise signal. The proposed methodology has been applied to detect bearing faults by the analysis of two real bearing datasets, from run-to-failure experiments. Three bearings that presented different defects in the test (inner race fault, rolling elements fault and outer race fault) were analysed to validate the performance of the entropy-based features. The modified Z-score has been implemented and used as an index to detect changes of the entropy features. The results clearly demonstrate that the proposed approach represents a valuable non-parametric tool for early detection of anomalies in bearings vibration signals.

Suggested Citation

  • Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko, 2019. "Entropy measures for early detection of bearing faults," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 458-472.
  • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:458-472
    DOI: 10.1016/j.physa.2018.09.052
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    References listed on IDEAS

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    1. Venkatesan, R.C. & Plastino, A., 2017. "Fisher information framework for time series modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 480(C), pages 22-38.
    2. Ribeiro, Haroldo V. & Zunino, Luciano & Mendes, Renio S. & Lenzi, Ervin K., 2012. "Complexity–entropy causality plane: A useful approach for distinguishing songs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2421-2428.
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    1. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    2. Ruben Medina & Mariela Cerrada & Shuai Yang & Diego Cabrera & Edgar Estupiñan & René-Vinicio Sánchez, 2022. "Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features," Mathematics, MDPI, vol. 10(17), pages 1-29, August.

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