{"title":"MC-ABDS:用于工业生产中密集重叠和干扰的低信噪比故障诊断系统","authors":"","doi":"10.1016/j.apacoust.2024.110217","DOIUrl":null,"url":null,"abstract":"<div><p>Planetary gearboxes are vital in industrial production due to their large transmission ratios. Therefore, accurate fault diagnosis of planetary gearboxes is crucial. However, in industrial applications, the acoustic fault signals from two different adjacent planetary gearboxes may overlap and interfere with each other, resulting in a low Signal-to-Noise Ratio (SNR) for each acoustic fault source, which in turn prevents accurate fault diagnosis. In this context, the Multi-Task Learning-Temporal Convolutional Network (MTL-TCN) is proposed to simultaneously output the orientation of the acoustic sources as well as the fault type to solve the problem of interference between adjacent acoustic sources. A Spatial information based Multi-Task Channel Attention (SMTCA) mechanism is also proposed to solve the problem of acoustic signal overlapping by using the orientation information to calculate the weight of the acoustic signal channel and assigning it to the fault diagnostic task, which combines the sound field information into the separation of fault sources. Finally, a Multi-Channel Acoustic based diagnose System (MC-ABDS) is proposed, which contains a customized microphone array as well as a sound field information and fault feature information extraction method called Multi-Task Attention TCN (MTA-TCN). The system is validated by the data collected in the anechoic chamber, and it is effective for the acoustic overlapping and interference that occurs when two adjacent planetary gearboxes are operating. The orientation accuracy of the acoustic source reached 99.98 %and the diagnostic accuracy of the fault reached 92.08 %.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MC-ABDS: A system for low SNR fault diagnosis in industrial production with intense overlapping and interference\",\"authors\":\"\",\"doi\":\"10.1016/j.apacoust.2024.110217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Planetary gearboxes are vital in industrial production due to their large transmission ratios. Therefore, accurate fault diagnosis of planetary gearboxes is crucial. However, in industrial applications, the acoustic fault signals from two different adjacent planetary gearboxes may overlap and interfere with each other, resulting in a low Signal-to-Noise Ratio (SNR) for each acoustic fault source, which in turn prevents accurate fault diagnosis. In this context, the Multi-Task Learning-Temporal Convolutional Network (MTL-TCN) is proposed to simultaneously output the orientation of the acoustic sources as well as the fault type to solve the problem of interference between adjacent acoustic sources. A Spatial information based Multi-Task Channel Attention (SMTCA) mechanism is also proposed to solve the problem of acoustic signal overlapping by using the orientation information to calculate the weight of the acoustic signal channel and assigning it to the fault diagnostic task, which combines the sound field information into the separation of fault sources. Finally, a Multi-Channel Acoustic based diagnose System (MC-ABDS) is proposed, which contains a customized microphone array as well as a sound field information and fault feature information extraction method called Multi-Task Attention TCN (MTA-TCN). The system is validated by the data collected in the anechoic chamber, and it is effective for the acoustic overlapping and interference that occurs when two adjacent planetary gearboxes are operating. The orientation accuracy of the acoustic source reached 99.98 %and the diagnostic accuracy of the fault reached 92.08 %.</p></div>\",\"PeriodicalId\":55506,\"journal\":{\"name\":\"Applied Acoustics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Acoustics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003682X24003682\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X24003682","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
MC-ABDS: A system for low SNR fault diagnosis in industrial production with intense overlapping and interference
Planetary gearboxes are vital in industrial production due to their large transmission ratios. Therefore, accurate fault diagnosis of planetary gearboxes is crucial. However, in industrial applications, the acoustic fault signals from two different adjacent planetary gearboxes may overlap and interfere with each other, resulting in a low Signal-to-Noise Ratio (SNR) for each acoustic fault source, which in turn prevents accurate fault diagnosis. In this context, the Multi-Task Learning-Temporal Convolutional Network (MTL-TCN) is proposed to simultaneously output the orientation of the acoustic sources as well as the fault type to solve the problem of interference between adjacent acoustic sources. A Spatial information based Multi-Task Channel Attention (SMTCA) mechanism is also proposed to solve the problem of acoustic signal overlapping by using the orientation information to calculate the weight of the acoustic signal channel and assigning it to the fault diagnostic task, which combines the sound field information into the separation of fault sources. Finally, a Multi-Channel Acoustic based diagnose System (MC-ABDS) is proposed, which contains a customized microphone array as well as a sound field information and fault feature information extraction method called Multi-Task Attention TCN (MTA-TCN). The system is validated by the data collected in the anechoic chamber, and it is effective for the acoustic overlapping and interference that occurs when two adjacent planetary gearboxes are operating. The orientation accuracy of the acoustic source reached 99.98 %and the diagnostic accuracy of the fault reached 92.08 %.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.