Weakness Ranking Method for Subsystems of Heavy-Duty Machine Tools Based on FMECA Information

Yang, Zhaojun; Guo, Jinyan; Tian, Hailong; Chen, Chuanhai; Zhu, Yongfu; Liu, Jia

doi:10.1186/s10033-021-00539-6

Cited by 9 publications

(5 citation statements)

References 27 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, the weight of risk factors is considered in the risk analysis. Subjective weight methods such as analytic hierarchy process (AHP) [7], objective weight methods such as entropy weight method [17], or comprehensive weight methods [18][19][20] to give different weights to risk factors. On the other hand, many approaches have been applied to increase the reliability and rationality of risk prioritization.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined With Machine Learning

Duan

Zhu

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears increasingly important. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Therefore, this paper devises a method based on improved FMEA model combined with machine learning for complex systems and applies it to the reliability management of intelligent manufacturing systems. The structured network of failure modes is constructed based on the knowledge graph for the intelligent manufacturing systems. The grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes, hereafter the clustering analysis is employed to extract the features of failure modes. The results show that the proposed method can more accurately reflect the coupling relationship between the failure modes compared with the conventional FMEA method. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined With Machine Learning

Duan

Zhu

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…On the one hand, the weight of risk factors is considered in the risk analysis. Subjective weight methods such as analytic hierarchy process (AHP) [7], objective weight methods such as entropy weight method [17], or comprehensive weight methods [18][19][20] give different weights to risk factors. On the other hand, many approaches have been applied to increase the reliability and rationality of risk prioritization.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined with Machine Learning

Duan

Zhu

Wang

2023

Preprint

View full text Add to dashboard Cite

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears increasingly important. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Therefore, this paper devises a method based on an improved FMEA model combined with machine learning for complex systems and applies it to the reliability management of intelligent manufacturing systems. The structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. The grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes, hereafter the clustering analysis is employed to extract the features of failure modes. The results show that the proposed method can more accurately reflect the coupling relationship between the failure modes compared with the conventional FMEA method. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

show abstract

“…For instance. Yang et al 8 proposed a vulnerability ranking method for reliability analysis of heavy machine tool subsystems based on generalized failure mode, effects and criticality analysis (FMECA), which considers eight reliability indexes related to maintainability and cost. Zhou et al 9 introduced trapezoidal fuzzy number and analytic hierarchy process to solve the impact of subjective judgment in traditional RPN analysis which can better solve the hazard ranking problem.…”

Section: Introductionmentioning

confidence: 99%

“…For instance. Yang et al 8 . proposed a vulnerability ranking method for reliability analysis of heavy machine tool subsystems based on generalized failure mode, effects and criticality analysis (FMECA), which considers eight reliability indexes related to maintainability and cost.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

Guo

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

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.

show abstract

Weakness Ranking Method for Subsystems of Heavy-Duty Machine Tools Based on FMECA Information

Cited by 9 publications

References 27 publications

RETRACTED: Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined With Machine Learning

RETRACTED: Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined With Machine Learning

Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined with Machine Learning

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

Contact Info

Product

Resources

About