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

Fatih Alperen Erdoğan; Ayhan Küçükmanisa; Zeynep Hilal Kilimci

Makale detayları

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

Yazarlar:

Fatih Alperen Erdoğan | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 2, pp.148-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300863

pISSN:

2667-484X

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Fatih Alperen Erdoğan | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 2, pp.148-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300863

pISSN:

2667-484X

Fault detection

automobile engines

deep learning

sound processing

convolutional neural networks

Sayı

Makale detayları

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

Yazarlar: Fatih Alperen Erdoğan | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı: Kocaeli University

Ana Başlıklar: Yazılım Mühendisliği (Diğer) | Yapay Zeka

Dergi: Kocaeli Journal of Science and Engineering Vol.6 Issue 2, pp.148-154

Belge Tipi: Makale

Yayın Tarihi: 11/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001300863

pISSN: 2667-484X

Fault detection

automobile engines

deep learning

sound processing

convolutional neural networks

Sayı

Yazarlar:

Fatih Alperen Erdoğan | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 2, pp.148-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300863

pISSN:

2667-484X