Peter Wißbrock , Lukas Koschek , Zhao Ren , Wolfgang Nejdl
{"title":"Enhancing quality inspection of highly variant geared motors","authors":"Peter Wißbrock , Lukas Koschek , Zhao Ren , Wolfgang Nejdl","doi":"10.1016/j.apacoust.2025.110687","DOIUrl":null,"url":null,"abstract":"<div><div>Quality inspection is an important step in industrial production to avoid early failures and customer complaints. Custom-built products are often produced in a high number of variants and a low number of instances per variant, which makes automation of quality inspection a difficult task. Today’s manual inspection is cost-intensive and error-prune. This paper introduces the unexplored problem setting of quality inspection of highly variant geared motors and provides a full concept solving this industrial sound analytics problem. To enable research on this important topic, we introduce the publicly accessible Lenze quality inspection dataset, named Lenze-QI* and highlight its special challenges and characteristics. In contrast to other existing datasets, it is fully real-world, and it includes the variants configuration that can be used in machine learning algorithms. It is further demonstrated that the problem setting cannot be solved via state-of-the-art methods but using our proposed two-step approach. First, three feature vectors from the domain of advanced signal processing are proposed. The best performing approach is based on psychoacoustics, while also the approaches using the logarithm envelope or deep learning in combination with optimized spectrogram parameters are still useful. Second, we introduce the conditional node type to be used in an isolation forest, taking the variant’s configuration as input. The resulting conditional isolation forest is a novel anomaly detection approach taking additional attributes into account. Overall, the best performance for Lenze-QI can be achieved by an ensemble of the three advanced signal processing approaches in combination with the novel conditional isolation forest. The state-of-the-art performance is overruled by an increase of 13% in the area under the receiver operating characteristic. We demonstrate that the quality inspection of geared motors can be automated despite many challenges.</div><div>*The dataset Lenze-QI can be downloaded following https://doi.org/10.5281/zenodo.13854459.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"235 ","pages":"Article 110687"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-22","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/S0003682X25001598","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}
引用次数: 0
Abstract
Quality inspection is an important step in industrial production to avoid early failures and customer complaints. Custom-built products are often produced in a high number of variants and a low number of instances per variant, which makes automation of quality inspection a difficult task. Today’s manual inspection is cost-intensive and error-prune. This paper introduces the unexplored problem setting of quality inspection of highly variant geared motors and provides a full concept solving this industrial sound analytics problem. To enable research on this important topic, we introduce the publicly accessible Lenze quality inspection dataset, named Lenze-QI* and highlight its special challenges and characteristics. In contrast to other existing datasets, it is fully real-world, and it includes the variants configuration that can be used in machine learning algorithms. It is further demonstrated that the problem setting cannot be solved via state-of-the-art methods but using our proposed two-step approach. First, three feature vectors from the domain of advanced signal processing are proposed. The best performing approach is based on psychoacoustics, while also the approaches using the logarithm envelope or deep learning in combination with optimized spectrogram parameters are still useful. Second, we introduce the conditional node type to be used in an isolation forest, taking the variant’s configuration as input. The resulting conditional isolation forest is a novel anomaly detection approach taking additional attributes into account. Overall, the best performance for Lenze-QI can be achieved by an ensemble of the three advanced signal processing approaches in combination with the novel conditional isolation forest. The state-of-the-art performance is overruled by an increase of 13% in the area under the receiver operating characteristic. We demonstrate that the quality inspection of geared motors can be automated despite many challenges.
*The dataset Lenze-QI can be downloaded following https://doi.org/10.5281/zenodo.13854459.
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.