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Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

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A Correction to this article was published on 05 December 2022

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Abstract

Purpose

The global interest of develo** monitoring system is increasing due to the continuous challenges in reliability and accuracy. Automatic fault detection and diagnosis of rotating machinery play an important role for the high efficiency and reliability of modern industrial systems. The key point of having high accurate automatic model for fault detection and diagnosis is obtaining defect features and choosing a representative approach for the model.

Methods

In this paper, a model is developed based on Mel Frequency Cepstral Coefficients (MFCC) and gammatone cepstral coefficients (GTCC) that are computed for the input signal frames. Additionally, two global representations (feature concatenation and feature statistics) are adopted to feed Support Vector Machine (SVM) and a temporal representation is used with Long Short-Term Memory (LSTM) and Echo State Network (ESN) classification models. To generalize the proposed model, the experiments are evaluated based on two different datasets (PHM09 and DDS), where the PHM09 contains samples of helical and spur gears while the DDS contains samples from parallel and plenary gearboxes.

Results

The results show that the proposed SVM model based on feature concatenation can effectively detect faults from gears and outperforms the other existing methods in the state-of-the-art studies.

Conclusion

Base on the result of this paper, a global representation by concatenating frame-based features outperforms global statistical and time-series feature representations.

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Availability of Data and Material

Both datasets (PHM09 and DDS) utilized during the current study are available in public. The PHM09 and DDS dataset can be found on (Public Data Sets - PHM Society) and (GitHub - cathysiyu/Mechanical-datasets) respectively.

Code Availability

The implementation of the concat-SVM and other models will be send upon request.

Change history

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Acknowledgements

The authors wish to thank the Erbil Polytechnic University, the Koya University and charmo University.

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Authors and Affiliations

  1. Department of Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq

    Zrar Kh. Abdul

  2. Department of Applied Computer Science, College of Medicals and Applied Sciences, Charmo University, Chamchamal, Sulaymaniyah, Kurdistan Region, Iraq

    Zrar Kh. Abdul

  3. Department of Software Engineering, Faculty of Engineering, Koya University, Koya, KOY45, Kurdistan Region, Iraq

    Abdulbasit K. Al-Talabani

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Abdul, Z.K., Al-Talabani, A.K. Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine. J. Vib. Eng. Technol. 11, 3565–3577 (2023). https://doi.org/10.1007/s42417-022-00768-6

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