@inproceedings{92a5cac003574d96872e6f2f2b43da2f,
title = "A Novel ARMA-Based Approach for Online Early Fault Detection of Rolling Bearings",
abstract = "Considering that only a small amount of bearing training data can be collected in real-world situations, making it difficult to build a reliable and robust deep learning-based model, and that early fault signatures are often subtle and unobtrusive, an ARMA-based online Early Fault Detection (EFD) method is proposed to solve these two problems. Firstly, an sensitive dedicated health indicator is first found to make early failures apparent. And then, Adaptive Piecewise Constant Approximate S egmentation (APCAS) is introduced to enable the classification of health stages. Finally, experiments are carried out on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that the proposed method is effective in accurately detecting early faults.",
keywords = "APCAS, ARMA, Dedicated health indicator, EFD",
author = "Yichao Li and Yanfang Liu and Xiangyang Xu and Yongze Lang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; International Conference on Mechanical Transmission, ICMT 2025 ; Conference date: 17-04-2025 Through 20-04-2025",
year = "2026",
doi = "10.1007/978-981-95-3650-4\_109",
language = "英语",
isbn = "9789819536498",
series = "Lecture Notes in Mechanical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1211--1218",
editor = "Shuxin Wang and Datong Qin and Fei Liu",
booktitle = "Advances in Mechanical Transmission",
address = "德国",
}