Research on Bearing Fault Diagnosis Method of the FPSO Soft Yoke Mooring System Based on Minimum Entropy Deconvolution

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value.

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Publication Details

Journal
Journal of Marine Science and Engineering
Published
2026-01-22
DOI
https://doi.org/10.3390/jmse14020235
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Bearing Fault Diagnosis Method of the FPSO Soft Yoke Mooring System Based on Minimum Entropy Deconvolution

Jiaxi Zhang, Ziguang Jia, Da‐Yong Zhang, T. Sun et al.
Journal of Marine Science and Engineering
Machine Fault Diagnosis Techniques
article

Research on Bearing Fault Diagnosis Method of the FPSO Soft Yoke Mooring System Based on Minimum Entropy Deconvolution

Jiaxi Zhang, Ziguang Jia, Da‐Yong Zhang, T. Sun, Zheliang Fan, Peng Zhang, Yi Huang
article en

Abstract

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value.

Journal of Marine Science and EngineeringVol. 14(2)
Dalian University of Technology (CN), Dalian Maritime University (CN)
Ministry of Industry and Information Technology of the People's Republic of China
Life below water
Openalex Percentile: Top 80%
Machine Fault Diagnosis Techniques
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