{"title":"船舶旋转机械故障诊断框架:基于压缩堆叠自编码器的半监督学习框架","authors":"Penghao Pan, Dong Zhao, Yueyang Li","doi":"10.1177/14750902221143827","DOIUrl":null,"url":null,"abstract":"Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":"49 1","pages":"625 - 636"},"PeriodicalIF":1.5000,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder\",\"authors\":\"Penghao Pan, Dong Zhao, Yueyang Li\",\"doi\":\"10.1177/14750902221143827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.\",\"PeriodicalId\":20667,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment\",\"volume\":\"49 1\",\"pages\":\"625 - 636\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2022-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/14750902221143827\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/14750902221143827","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder
Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.
期刊介绍:
The Journal of Engineering for the Maritime Environment is concerned with the design, production and operation of engineering artefacts for the maritime environment. The journal straddles the traditional boundaries of naval architecture, marine engineering, offshore/ocean engineering, coastal engineering and port engineering.