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Online damage severity level classification in gears under natural damage progression

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Abstract

Effective diagnosis of the gear damage stages is critical for the industries to reduce unexpected failures and maximise life utilisation. In geared systems, pitting is one of the most common failure modes observed, which originates from the surface/subsurface cracks. The gear damage levels were classified using seeded defect data instead of naturally progressed in the reported works. It is difficult to simulate a natural pitting failure on the gear tooth using an artificial process. As implemented in prior experimental studies involving seeded defects, a sudden change in the gear pitting area may not occur in practice. This study presents an ensemble decision tree-based random forest (RF) classifier methodology for the online classification of gear damage stages under natural pitting progression. A health indicator (HI) termed CCR (i.e. correlation coefficient of residual vibration signal) is extracted using a raw vibration signal to represent the pitting progression in spur gears. The exact relationship between the HI and gear tooth degradation stages is crucial during the implementation of the classifier model. Hence, a binary segmentation (BS) methodology identifies the relationship between HI and gear health stages (i.e. healthy, initial pitting, medium pitting and severe pitting). The output of BS methodology is used for classifier model training, and later based on the trained model, gear pitting severity levels were estimated for a newly installed gear. The performance of the proposed framework (i.e. combining BS and RF methodology) is validated through six accelerated runs to failure gear pitting experiments.

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Funding

The authors were financially supported by the Aeronautics Research and Development Board, D.R.D.O., Government of India (project grant no. ARDB/01/1071913/M/I), and SERB, Government of India (project grant no. SB/S9/Z-16/2016-UALBERTA-IV (2018–19)).

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Authors and Affiliations

  1. Center for Intelligent Maintenance Systems, University of Cincinnati, Cincinnati, USA

    Pradeep Kundu

  2. Department of Mechanical Engineering, IIT Delhi, New Delhi, India

    Ashish K. Darpe

  3. Department of Mechanical Engineering, IIT Bombay, Mumbai, India

    Makarand S. Kulkarni

  4. Qingdao International Academician Park Research Institute, Qingdao, China

    Mingjian Zuo

  5. Department of Mechanical Engineering, University of Alberta, Edmonton, Canada

    Mingjian Zuo

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  1. Pradeep Kundu
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  2. Ashish K. Darpe
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  3. Makarand S. Kulkarni
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  4. Mingjian Zuo
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Contributions

Pradeep Kundu: data collection, methodology, experiment, result analysis and original draft writing. Ashish K. Darpe: methodology, review and editing. Makarand S. Kulkarni: methodology, review, and editing. Ming J. Zuo: methodology.

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Correspondence to Pradeep Kundu.

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Appendix Calculation of residual vibration signal

Appendix Calculation of residual vibration signal

After TSA implementation, the obtained gear vibration signal only contains frequencies that are synchronous with gear rotation, i.e. rotational speed, GMF and its harmonics and sidebands, as shown in Fig. 12. The gear considered in this example has ten teeth and a rotational speed of 10 Hz (i.e. GMF = 10 × 10 = 100 Hz). The residual vibration signal is estimated by removing the shaft rotational frequency, GMF and its harmonics from the raw vibration signal and only extracting the sidebands from the signal, as shown in Fig. 12. The following steps are followed to estimate the residual vibration signal.

  • Perform time synchronous averaging of raw vibration signal in the time domain.

  • Convert the TSA signal in the frequency domain and remove the shaft rotation frequency, GMF and its harmonics.

  • This filtered signal obtained in the frequency domain is converted back to the time domain and is termed a residual vibration signal.

Fig. 12
figure 12

a TSA signal. b Residual signal

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Kundu, P., Darpe, A.K., Kulkarni, M.S. et al. Online damage severity level classification in gears under natural damage progression. Int J Adv Manuf Technol 124, 1–20 (2023). https://doi.org/10.1007/s00170-022-10428-4

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