文献类型：期刊文献

英文题名：A Semi-Supervised Fault Diagnosis Approach Using Feature-Aligned Ensemble Stacked Autoencoders

作者：Song, Bing[1]; Zhang, Junshuai[1]; Shi, Hongbo[1]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shang Hai, China

年份：2025

外文期刊名：SAFEPROCESS 2025 - 14th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes

收录：EI(收录号：20260920168997)

语种：英文

外文关键词：Computer aided diagnosis - Labeled data - Learning systems - Semi-supervised learning

摘要：The fault diagnosis method based on stacked autoencoder (SAE) usually needs sufficient labeled data to train an effective diagnosis model, and it is difficult to obtain labeled data in industrial process. To solve the above problems, a semi-supervised fault diagnosis method based on feature alignment-ensemble learning stacked autoencoder (FA-ELSAE) was proposed. First, samples from the same class should follow the same distribution since the method is based on stacked autoencoders. This means that while training the SAE diagnostic model, this restriction is introduced to the model's loss function, maximizing the information of unlabeled data, enhancing the diagnostic model's capacity for generalization, and decreasing its overfitting. Then, ensemble learning is included into the SAE diagnostic model framework to enhance the method of false labeling unlabeled samples, lower the likelihood of false labeling unlabeled samples, enhance the quality of false labels, and enhance the model's diagnostic efficacy. Lastly, an industrial procedure was used for test validation. ? 2025 IEEE.