Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault fe...
Advances in deep learning methods have demonstrated remarkable development in diagnosing faults of rotating machinery. The currently popular deep neural networks suffer from design flaws in their network structure, leadi...
Raw vibration signals poorly perform in industrial bearing fault diagnosis because impulse features are damped and masked by disturbances and noises. Fault diagnosis is more challenging due to weak features. This work pr...
To improve the accuracy of the fault diagnosis of a ship propulsion shaft bearing in a harsh working environment, a visual diagnosis method based on the incrementally accumulated holographic symmetrical dot pattern (SDP)...
JOURNAL OF MARINE SCIENCE AND ENGINEERINGLi, Bo Xu, Jin Pan, Xinxiang Chen, Rong Ma, Long Yin, Jianchuan Liao, Zhiqiang Chu, Lilin Zhao, Zhiqiang Lian, Jingjing Wang, Haixia 出版年：2023
Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal...
Bearing failures are the most frequent causes of breakdowns in rotating machinery. Different levels of severity in these failures exhibit distinct fault characteristics in the vibration signal. This paper presents a bear...
Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault informa...
Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, thi...
In actual engineering production, bearings typically operate in harsh environments. The fault features of bearing vibration signals are often submerged by background noise, making it difficult to extract the fault signal...
To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem ...
In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and ...
Aiming to address the issue of the complex and harsh working environment of rotating machinery, the features of vibration signals associated with structural faults are often obscured by noise, resulting in low accuracy i...
ISA TransactionsTao, Yukun Ge, Chun Feng, Han Xue, Hongtao Yao, Mingyu Tang, Haihong Liao, Zhiqiang Chen, Peng 出版年：2025
For compound fault detection of in-wheel motor bearings, this paper proposes a novel approach to adaptively separate multi-source signals and extract compound fault features. Building upon blind source separation (BSS), ...
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