Failure analysis of floating offshore wind turbines considering working condition correlations

This paper presents an analysis of the fault correlation between CNC machine tool subsystems using decision laboratory analysis and the House of Reliability (HoR) methodology. Initially, the maintenance information of the machine tool and other hazard indicators is comprehensively analyzed based on the cognitive best worst method (CBWM) to assign weights of hazard indicators. The HoR method is then employed to perform fault correlation fusion, which results in comprehensive hazard analysis results of the machine tool subsystems considering the fault correlation. Finally, the results obtained from the fusion fault correlation and the traditional hazard analysis are compared. The results indicate that the proposed method can highlight the relatively important subsystems and the most vulnerable parts of the machine tools. Overall, the hazard analysis results of this paper contribute to the safe and reliable operational and maintenance optimization of CNC machine tools.

Section: Comprehensive Hazard Analysis Of Subsystem Considering Multi...mentioning

confidence: 99%

“…The conventional FMECA analysis mainly focuses on the impact of failure on system operation 37,38 . However, for heavy CNC machine tools, maintenance often requires significant costs and personnel time, and machine tool downtime can lead to significant cost waste 39 .…”

Section: Hazard Analysis Of the Heavy Cnc Machine Toolsmentioning

confidence: 99%

A hazard analysis of heavy CNC machine tools based on fusion fault correlation

Chen

Zhang

Guo

et al. 2023

“…Liu 7 analyzed the reliability of wind turbine blades based on Bayesian and finite element analysis. Sun 8 proposed a failure analysis method of the D-vine copula Bayesian Network, which quantifies the probability of risk.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine gear reliability analysis considering dependent competing failure

Liu,

Gao,

Yuan

et al. 2023

To more precisely assess the reliability of wind turbine gears, a competing failure model is established with consideration given to the degradation of internal fatigue strength and external random shocks. Firstly, the nonlinear Gamma process is adopted to depict the degradation of fatigue strength, and the strength‐stress interference theory is used to establish the threshold of degradation failure that changes over time. Also, the non‐homogeneous Poisson process is employed to depict the arrival of the shock, and classification of the shocks. Secondly, the correlation between degradation and shock is described, while the effect of shocks on degradation is demonstrated as a sudden increase in the extent of degradation. Besides, the decline rate of residual strength curve is introduced to describe the change in intensity parameters of the Poisson process, and then the effect of degradation on shocks is described. Finally, the sun gear in the wind turbine gearbox is an example. Monte Carlo simulation is used to calculate the reliability of the dependent competing failure model. This is then compared with the conventional stress‐strength interference model. The results verify that when the relationship between degradation and the shock process is disregarded, the reliability of gears may be overestimated.

Failure analysis of floating offshore wind turbines based on a fuzzy failure mode and effect analysis model

Feng,

Xia,

Wen

et al. 2024

The surging global demand for renewable energy has fueled the expansion of offshore wind energy, leveraging superior wind profiles and technological advancements in offshore wind turbine technology. Despite this growth, the operational experience of floating offshore wind turbines still needs to be improved as an emerging technology. Addressing this knowledge gap, our study compiles a comprehensive database of failure events associated with these turbines. Employing a rigorous analytical approach through the Fuzzy Failure Mode and Effect Analysis (FMEA) methodology, we conduct an in‐depth failure analysis of floating offshore wind turbines. This investigation allows us to identify the most critical failure modes and pinpoint components vulnerable to failures. The paper delves into the root causes of these significant failure modes, proposing preventive and corrective measures based on our findings. Our recommendations serve as a strategic guide for stakeholders, offering insights to enhance offshore wind turbines’ design, operation, and maintenance practices and the broader wind farm infrastructure. Despite this, further clarification on the application and highlights of the fuzzy FMEA model is warranted in the abstract, a point we address in the revised conclusion to enhance our study's overall clarity and completeness.