Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing
The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with othe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/284 |
| _version_ | 1797431253236973568 |
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| author | Tsung-Hsien Liu Jun-Zhe Chi Bo-Lin Wu Yee-Shao Chen Chung-Hsun Huang Yuan-Sun Chu |
| author_facet | Tsung-Hsien Liu Jun-Zhe Chi Bo-Lin Wu Yee-Shao Chen Chung-Hsun Huang Yuan-Sun Chu |
| author_sort | Tsung-Hsien Liu |
| collection | DOAJ |
| description | The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%. |
| first_indexed | 2024-03-09T09:41:15Z |
| format | Article |
| id | doaj.art-f49e561de9864c508bc24ba94424ba70 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:41:15Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f49e561de9864c508bc24ba94424ba702023-12-02T00:55:09ZengMDPI AGSensors1424-82202022-12-0123128410.3390/s23010284Design and Implementation of Machine Tool Life Inspection System Based on Sound SensingTsung-Hsien Liu0Jun-Zhe Chi1Bo-Lin Wu2Yee-Shao Chen3Chung-Hsun Huang4Yuan-Sun Chu5Communications Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanElectrical Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanElectrical Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanElectrical Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanElectrical Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanElectrical Engineering Department, National Chung Cheng University, Chiayi 62102, TaiwanThe main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%.https://www.mdpi.com/1424-8220/23/1/284deep learningDNNORMspeech enhancementmachine toolslife period |
| spellingShingle | Tsung-Hsien Liu Jun-Zhe Chi Bo-Lin Wu Yee-Shao Chen Chung-Hsun Huang Yuan-Sun Chu Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing Sensors deep learning DNN ORM speech enhancement machine tools life period |
| title | Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing |
| title_full | Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing |
| title_fullStr | Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing |
| title_full_unstemmed | Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing |
| title_short | Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing |
| title_sort | design and implementation of machine tool life inspection system based on sound sensing |
| topic | deep learning DNN ORM speech enhancement machine tools life period |
| url | https://www.mdpi.com/1424-8220/23/1/284 |
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