Pub Date : 2023-04-14DOI: 10.1109/ICPHM57936.2023.10194183
Matthias Kreuzer, Walter Kellermann
In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regu-larization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspection.
{"title":"1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge","authors":"Matthias Kreuzer, Walter Kellermann","doi":"10.1109/ICPHM57936.2023.10194183","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194183","url":null,"abstract":"In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regu-larization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspection.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117069578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-14DOI: 10.1109/ICPHM57936.2023.10194026
Matthias Kreuzer, D. Schmidt, Simon Wokusch, Walter Kellermann
In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that bearing faults can be reliably detected with the chosen MFCC features even for bearing damages that were not included in training.
{"title":"Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data","authors":"Matthias Kreuzer, D. Schmidt, Simon Wokusch, Walter Kellermann","doi":"10.1109/ICPHM57936.2023.10194026","DOIUrl":"https://doi.org/10.1109/ICPHM57936.2023.10194026","url":null,"abstract":"In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that bearing faults can be reliably detected with the chosen MFCC features even for bearing damages that were not included in training.","PeriodicalId":169274,"journal":{"name":"2023 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124730414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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