{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T08:07:54Z","timestamp":1767514074818,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013077","name":"National Science and Technology Planning Project","doi-asserted-by":"publisher","award":["MOST 110-2221-E-194-044"],"award-info":[{"award-number":["MOST 110-2221-E-194-044"]}],"id":[{"id":"10.13039\/501100013077","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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%.<\/jats:p>","DOI":"10.3390\/s23010284","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:31:54Z","timestamp":1672205514000},"page":"284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing"],"prefix":"10.3390","volume":"23","author":[{"given":"Tsung-Hsien","family":"Liu","sequence":"first","affiliation":[{"name":"Communications Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Zhe","family":"Chi","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo-Lin","family":"Wu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yee-Shao","family":"Chen","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chung-Hsun","family":"Huang","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7511-8340","authenticated-orcid":false,"given":"Yuan-Sun","family":"Chu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National Chung Cheng University, Chiayi 62102, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1243\/09544062JMES1588","article-title":"Applying a Case-Based Reasoning Method for Fault Diagnosis During Maintenance","volume":"223","author":"Tsai","year":"2009","journal-title":"Proc. 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