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Detection of Fault from Acoustic Signals in Automobile Engines using Deep Learning Techniques

Yıl 2023, Cilt: 6 Sayı: 2, 148 - 154, 30.11.2023

Fatih Alperen Erdoğan Ayhan Küçükmanisa Zeynep Hilal Kilimci

https://doi.org/10.34088/kojose.1225591

Öz

Detecting faults in automobile engines from sound signals is a challenging task in the production phase of automobiles. That is why it attracts engineers and researchers to handle this issue thereby applying various solutions. In this work, we propose a deep learning-based fault detection mechanism in automobile engines from different sound resources. In the dataset collection phase, various vehicle breakdown sounds are gathered from social media environments by constructing our own customized crawler. Moreover, noise addition is applied to increase the amount of data. Subsequently, raw audio files are processed at the feature extraction step employing mel-frequency cepstral coefficients. To detect the vehicle breakdown sounds, 1-D and 2-D convolutional neural networks, long short-term memory networks, artificial neural networks, and support vector machines are modeled. Experiment results show that the usage of a 1-D convolutional neural network is transcendent with 99% accuracy compared to the other techniques, especially, state-of-the-art studies are considered.

Anahtar Kelimeler

Fault detection automobile engines deep learning sound processing convolutional neural networks

Kaynakça

  • [1] Wu J. D., Chuang, C. Q., 2005. Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals, NDT & e International, 38(8), pp. 605-614.
  • [2] Kabiri P., Makinejad A., 2011. Using PCA in acoustic emission condition monitoring to detect faults in an automobile engine, In 29th European Conference on Acoustic Emission Testing (EWGAE2010), 8-10 September, pp. 8-10.
  • [3] Wu J. D., Chen J. C., 2006. Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT & e International, 39(4), 304-311.
  • [4] Wu J. D., Liu C. H., 2008. Investigation of engine fault diagnosis using discrete wavelet transform and neural network, Expert Systems with Applications, 35(3), pp. 1200-1213.
  • [5] Widodo A., Yang B. S., 2008. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal, Expert Systems with Applications, 35(1-2), pp. 307-316.
  • [6] Ghaderi H., Kabiri P., 2011. Automobile independent fault detection based on acoustic emission using FFT, In Singapore International NDT Conference & Exhibition (SINCE 2011), 3-4 November.
  • [7] Ghaderi H., Kabiri P., 2017. Automobile engine condition monitoring using sound emission, Turkish Journal of Electrical Engineering and Computer Sciences, 25(3), pp. 1807-1826.
  • [8] Wang Y. S., Liu N. N., Guo H., Wang X. L., 2020. An engine-fault-diagnosis system based on sound intensity analysis and wavelet packet pre-processing neural network, Engineering applications of artificial intelligence, 94, 103765.
  • [9] Altinors A., Yol F., Yaman O., 2021. A sound-based method for fault detection with statistical feature extraction in UAV motors, Applied Acoustics, 183, 108325.
  • [10] Ramteke S. M., Chelladurai H., Amarnath M., 2022. Diagnosis and classification of diesel engine components faults using time–frequency and machine learning approach, Journal of Vibration Engineering & Technologies, 10(1), pp. 175-192.
  • [11] Ravikumar K. N., Madhusudana C. K., Kumar H., Gangadharan K. V., 2022. Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm, Engineering Science and Technology, an International Journal, 30, 101048.
  • [12] Xiao D., Qin C., Yu H., Huang Y., Liu C., Zhang J., 2021. Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals, Measurement, 176, 109186.
  • [13] Tran M. Q., Liu M. K., Tran Q. V., Nguyen T. K., 2021. Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors, IEEE Transactions on Instrumentation and Measurement, 71, pp. 1-13.
  • [14] Tang S., Zhu Y., Yuan, S., 2022. A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images, Advanced Engineering Informatics, 52, 101554.
  • [15] Yıldırım M., 2022. MFCC Yöntemi ve önerilen derin model ile çevresel Seslerin Otomatik olarak sınıflandırılması, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34, 449-457.
  • [16] Salamon J., Jacoby C., Bello J. P., 2014. A dataset and taxonomy for Urban Sound Research, Proceedings of the 22nd ACM International Conference on Multimedia, 3-7 November.
  • [17] Salamon J., Bello J. P., 2015. Unsupervised feature learning for Urban Sound Classification, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-24 April.
  • [18] Lezhenin I., Bogach N., Pyshkin, E., 2019. Urban sound classification using long short-term memory neural network, Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 1-4 September.

Yıl 2023, Cilt: 6 Sayı: 2, 148 - 154, 30.11.2023

Fatih Alperen Erdoğan Ayhan Küçükmanisa Zeynep Hilal Kilimci

https://doi.org/10.34088/kojose.1225591

Öz

Kaynakça

  • [1] Wu J. D., Chuang, C. Q., 2005. Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals, NDT & e International, 38(8), pp. 605-614.
  • [2] Kabiri P., Makinejad A., 2011. Using PCA in acoustic emission condition monitoring to detect faults in an automobile engine, In 29th European Conference on Acoustic Emission Testing (EWGAE2010), 8-10 September, pp. 8-10.
  • [3] Wu J. D., Chen J. C., 2006. Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT & e International, 39(4), 304-311.
  • [4] Wu J. D., Liu C. H., 2008. Investigation of engine fault diagnosis using discrete wavelet transform and neural network, Expert Systems with Applications, 35(3), pp. 1200-1213.
  • [5] Widodo A., Yang B. S., 2008. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal, Expert Systems with Applications, 35(1-2), pp. 307-316.
  • [6] Ghaderi H., Kabiri P., 2011. Automobile independent fault detection based on acoustic emission using FFT, In Singapore International NDT Conference & Exhibition (SINCE 2011), 3-4 November.
  • [7] Ghaderi H., Kabiri P., 2017. Automobile engine condition monitoring using sound emission, Turkish Journal of Electrical Engineering and Computer Sciences, 25(3), pp. 1807-1826.
  • [8] Wang Y. S., Liu N. N., Guo H., Wang X. L., 2020. An engine-fault-diagnosis system based on sound intensity analysis and wavelet packet pre-processing neural network, Engineering applications of artificial intelligence, 94, 103765.
  • [9] Altinors A., Yol F., Yaman O., 2021. A sound-based method for fault detection with statistical feature extraction in UAV motors, Applied Acoustics, 183, 108325.
  • [10] Ramteke S. M., Chelladurai H., Amarnath M., 2022. Diagnosis and classification of diesel engine components faults using time–frequency and machine learning approach, Journal of Vibration Engineering & Technologies, 10(1), pp. 175-192.
  • [11] Ravikumar K. N., Madhusudana C. K., Kumar H., Gangadharan K. V., 2022. Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm, Engineering Science and Technology, an International Journal, 30, 101048.
  • [12] Xiao D., Qin C., Yu H., Huang Y., Liu C., Zhang J., 2021. Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals, Measurement, 176, 109186.
  • [13] Tran M. Q., Liu M. K., Tran Q. V., Nguyen T. K., 2021. Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors, IEEE Transactions on Instrumentation and Measurement, 71, pp. 1-13.
  • [14] Tang S., Zhu Y., Yuan, S., 2022. A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images, Advanced Engineering Informatics, 52, 101554.
  • [15] Yıldırım M., 2022. MFCC Yöntemi ve önerilen derin model ile çevresel Seslerin Otomatik olarak sınıflandırılması, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34, 449-457.
  • [16] Salamon J., Jacoby C., Bello J. P., 2014. A dataset and taxonomy for Urban Sound Research, Proceedings of the 22nd ACM International Conference on Multimedia, 3-7 November.
  • [17] Salamon J., Bello J. P., 2015. Unsupervised feature learning for Urban Sound Classification, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-24 April.
  • [18] Lezhenin I., Bogach N., Pyshkin, E., 2019. Urban sound classification using long short-term memory neural network, Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 1-4 September.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Fatih Alperen Erdoğan 0000-0001-9871-6784

Ayhan Küçükmanisa 0000-0002-1886-1250

Zeynep Hilal Kilimci 0000-0003-1497-305X

Erken Görünüm Tarihi 16 Ekim 2023
Yayımlanma Tarihi 30 Kasım 2023
Kabul Tarihi 28 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 2

Kaynak Göster

APA Erdoğan, F. A., Küçükmanisa, A., & Kilimci, Z. H. (2023). Detection of Fault from Acoustic Signals in Automobile Engines using Deep Learning Techniques. Kocaeli Journal of Science and Engineering, 6(2), 148-154. https://doi.org/10.34088/kojose.1225591