Fault diagnosis for vehicle air conditioning blower using deep learning neural network
This study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-09-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/14613484221085891 |
| _version_ | 1818494783131222016 |
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| author | Jian-Da Wu Jun-Yuan Ke Fan-Yu Shih Wen-Jye Shyr |
| author_facet | Jian-Da Wu Jun-Yuan Ke Fan-Yu Shih Wen-Jye Shyr |
| author_sort | Jian-Da Wu |
| collection | DOAJ |
| description | This study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in time or frequency domains. Unfortunately, the frequency smearing effect often arises in some critical conditions. In the present study, discrete wavelet transform (DWT) and wavelet packet transform (WPT) are proposed in fault diagnosis. Meanwhile, when using mechanical learning methods, the data are relatively large, in order to reduce the amount of data, DWT and WPT low-frequency decomposition could be used to improve the performance. Furthermore, the signal characteristics more comprehensive, this study attempts to use the feature extraction method of wavelet packet conversion to improve the signal characteristics. In the experiment process, the operation state of the blade blower in the vehicle air conditioner, four different faults were designed, test database was established through sound to classify, and identify the data using deep neural networks to achieve the purpose of blower fault diagnosis. In data analysis, the original signal is presented through wavelet packet decomposition and discrete packet conversion technology, compared with traditional time and frequency domain signals to explore the identification rate, identification speed and related issues. Experimental results show that using WPT combined with deep neural networks have good fault diagnosis and discrimination capabilities, training, and identification time is shorter than time-frequency domain signals. |
| first_indexed | 2024-12-10T18:11:08Z |
| format | Article |
| id | doaj.art-cc1b7f566d9b4068932222d36a714522 |
| institution | Directory Open Access Journal |
| issn | 1461-3484 2048-4046 |
| language | English |
| last_indexed | 2024-12-10T18:11:08Z |
| publishDate | 2022-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Low Frequency Noise, Vibration and Active Control |
| spelling | doaj.art-cc1b7f566d9b4068932222d36a7145222022-12-22T01:38:26ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462022-09-014110.1177/14613484221085891Fault diagnosis for vehicle air conditioning blower using deep learning neural networkJian-Da WuJun-Yuan KeFan-Yu ShihWen-Jye ShyrThis study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in time or frequency domains. Unfortunately, the frequency smearing effect often arises in some critical conditions. In the present study, discrete wavelet transform (DWT) and wavelet packet transform (WPT) are proposed in fault diagnosis. Meanwhile, when using mechanical learning methods, the data are relatively large, in order to reduce the amount of data, DWT and WPT low-frequency decomposition could be used to improve the performance. Furthermore, the signal characteristics more comprehensive, this study attempts to use the feature extraction method of wavelet packet conversion to improve the signal characteristics. In the experiment process, the operation state of the blade blower in the vehicle air conditioner, four different faults were designed, test database was established through sound to classify, and identify the data using deep neural networks to achieve the purpose of blower fault diagnosis. In data analysis, the original signal is presented through wavelet packet decomposition and discrete packet conversion technology, compared with traditional time and frequency domain signals to explore the identification rate, identification speed and related issues. Experimental results show that using WPT combined with deep neural networks have good fault diagnosis and discrimination capabilities, training, and identification time is shorter than time-frequency domain signals.https://doi.org/10.1177/14613484221085891 |
| spellingShingle | Jian-Da Wu Jun-Yuan Ke Fan-Yu Shih Wen-Jye Shyr Fault diagnosis for vehicle air conditioning blower using deep learning neural network Journal of Low Frequency Noise, Vibration and Active Control |
| title | Fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| title_full | Fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| title_fullStr | Fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| title_full_unstemmed | Fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| title_short | Fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| title_sort | fault diagnosis for vehicle air conditioning blower using deep learning neural network |
| url | https://doi.org/10.1177/14613484221085891 |
| work_keys_str_mv | AT jiandawu faultdiagnosisforvehicleairconditioningblowerusingdeeplearningneuralnetwork AT junyuanke faultdiagnosisforvehicleairconditioningblowerusingdeeplearningneuralnetwork AT fanyushih faultdiagnosisforvehicleairconditioningblowerusingdeeplearningneuralnetwork AT wenjyeshyr faultdiagnosisforvehicleairconditioningblowerusingdeeplearningneuralnetwork |