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...
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...
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), ...
2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - ProceedingsLiao, Zhiqiang Jia, Baozhu Kong, Defeng Ji, Ran Li, Xiaoyu Hao, Kang 出版年：2022
In the bearing remaining useful life (RUL) prediction, constructing a health indicator to reflect the running bearings health status is one of the most critical parts, because it determines the performance of the RUL pre...
2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021Liao, Zhiqiang Song, Xuewei Jia, Baozhu Zuo, Dunwen Sheng, Yi Chen, Peng 出版年：2021
The vibration signal with non-stationary, strong noise, and weak fault feature is inevitably acquired in practical marine propulsion shaft bearing fault diagnosis due to harsh environment. These obstacles lead to diagnos...
15th Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024Liao, Zhiqiang Liang, Guanlong Song, Xuewei Li, Xiaoyu Huang, Zhende Jia, Baozhu 出版年：2024
In order to solve the problem of the limited ability of a single signal to characterize the fault state of AC motors, this paper proposes an AC motor fault diagnosis method based on multi-source signal fusion SDP (Symmet...
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...
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)...
2024 International Conference on Intelligent Ships and Electromechanical System, ICISES 2024Jia, Baozhu Huang, Zhende Liang, Guanlong Song, Xuewei Li, Kai Liao, Zhiqiang 出版年：2024
Aiming to address the issue of high-dimensional nonlinear characteristics in fault data due to the complex structure of marine diesel engines and the variability of their operating conditions, this paper proposes a fault...
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...
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...
ICSMD 2024 - 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial IntelligenceLiao, Zhiqiang Huang, Zhende Song, Xuewei Jia, Baozhu Liang, Guanlong Li, Xiaoyu 出版年：2024
The fault features of shaft misalignment in stator currents are often suppressed and masked by interference and noise, leading to weak fault features and affecting the accuracy of fault diagnosis. This paper proposes a f...
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...
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...
2024 International Conference on Intelligent Ships and Electromechanical System, ICISES 2024Jia, Baozhu Liang, Guanlong Huang, Zhende Niu, Jinzhang Song, Xuewei Liao, Zhiqiang 出版年：2024
Aiming at the problems of complex structure of marine diesel engines, strong background noise in collected signals, and limited fault information in single-dimensional signals, this paper proposes a fault diagnosis metho...
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 ...
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...
To address the challenges posed by the difficulty of extracting fault features from rotating machinery with weak fault features, this paper proposes a rotating machinery structural faults feature enhancement and diagnosi...
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...
JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICSJin, Yan Xin, Wang Dapeng, Zhang Zhiqiang, Liao Ximing, Wu 出版年：2022
The remaining useful life forecast (RUL) of rolling bearings, a crucial part of offshore equipment, is one of the most troublesome equipment because it may avoid equipment failure and lessen equipment failure loss. This ...
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