Ruonan Lu;Da Zheng;Chengyuan Zhu;Weiwei Cao;Qinmin Yang
{"title":"基于对比学习的双自编码器在装载机变速箱异常检测中的应用","authors":"Ruonan Lu;Da Zheng;Chengyuan Zhu;Weiwei Cao;Qinmin Yang","doi":"10.1109/TCSII.2025.3590139","DOIUrl":null,"url":null,"abstract":"Anomaly detection (AD) of gearboxes is essential for ensuring the operational safety and reliability of the loader. However, identifying anomalies in non-stationary signals remains challenging as anomalies often emerge within the normal fluctuation, especially when normal and abnormal samples exhibit high similarity. This brief proposes a contrastive learning-based dual autoencoder (AE) AD method for loader gearboxes. Specifically, the continuous wavelet transform is employed to capture dynamic characteristics of non-stationary signals. A compound scaling network is then designed into the unified encoder to extract complex features while maintaining a lightweight architecture. Subsequently, a sparse representation channel is integrated into the second AE framework, complementing the basis for contrastive mechanisms and promoting the learning of consistency across normal samples with the reconstruction channel. By minimizing the contrastive loss between two samples from different channels, the model learns the inherent consistency of normal samples. Finally, the contrastive loss of the second AE and the reconstruction error of the first AE serve as indicators for detecting abnormalities. Experimental results on real-world loader gearbox data demonstrate that the proposed method achieves a high fault detection rate, a low false alarm rate, and robust reliability, validating its effectiveness.","PeriodicalId":13101,"journal":{"name":"IEEE Transactions on Circuits and Systems II: Express Briefs","volume":"72 9","pages":"1223-1227"},"PeriodicalIF":4.9000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes\",\"authors\":\"Ruonan Lu;Da Zheng;Chengyuan Zhu;Weiwei Cao;Qinmin Yang\",\"doi\":\"10.1109/TCSII.2025.3590139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anomaly detection (AD) of gearboxes is essential for ensuring the operational safety and reliability of the loader. However, identifying anomalies in non-stationary signals remains challenging as anomalies often emerge within the normal fluctuation, especially when normal and abnormal samples exhibit high similarity. This brief proposes a contrastive learning-based dual autoencoder (AE) AD method for loader gearboxes. Specifically, the continuous wavelet transform is employed to capture dynamic characteristics of non-stationary signals. A compound scaling network is then designed into the unified encoder to extract complex features while maintaining a lightweight architecture. Subsequently, a sparse representation channel is integrated into the second AE framework, complementing the basis for contrastive mechanisms and promoting the learning of consistency across normal samples with the reconstruction channel. By minimizing the contrastive loss between two samples from different channels, the model learns the inherent consistency of normal samples. Finally, the contrastive loss of the second AE and the reconstruction error of the first AE serve as indicators for detecting abnormalities. Experimental results on real-world loader gearbox data demonstrate that the proposed method achieves a high fault detection rate, a low false alarm rate, and robust reliability, validating its effectiveness.\",\"PeriodicalId\":13101,\"journal\":{\"name\":\"IEEE Transactions on Circuits and Systems II: Express Briefs\",\"volume\":\"72 9\",\"pages\":\"1223-1227\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Circuits and Systems II: Express Briefs\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11083538/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems II: Express Briefs","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11083538/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes
Anomaly detection (AD) of gearboxes is essential for ensuring the operational safety and reliability of the loader. However, identifying anomalies in non-stationary signals remains challenging as anomalies often emerge within the normal fluctuation, especially when normal and abnormal samples exhibit high similarity. This brief proposes a contrastive learning-based dual autoencoder (AE) AD method for loader gearboxes. Specifically, the continuous wavelet transform is employed to capture dynamic characteristics of non-stationary signals. A compound scaling network is then designed into the unified encoder to extract complex features while maintaining a lightweight architecture. Subsequently, a sparse representation channel is integrated into the second AE framework, complementing the basis for contrastive mechanisms and promoting the learning of consistency across normal samples with the reconstruction channel. By minimizing the contrastive loss between two samples from different channels, the model learns the inherent consistency of normal samples. Finally, the contrastive loss of the second AE and the reconstruction error of the first AE serve as indicators for detecting abnormalities. Experimental results on real-world loader gearbox data demonstrate that the proposed method achieves a high fault detection rate, a low false alarm rate, and robust reliability, validating its effectiveness.
期刊介绍:
TCAS II publishes brief papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes:
Circuits: Analog, Digital and Mixed Signal Circuits and Systems
Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic
Circuits and Systems, Power Electronics and Systems
Software for Analog-and-Logic Circuits and Systems
Control aspects of Circuits and Systems.