Evaluation for machine tool components importance based on improved LeaderRank

Zhang, Yingzhi; Liang, Shubin; Liu, Jialin; Cao, Peilong; Luan, Lan

doi:10.1177/1748006x20979437

Cited by 5 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameter search range during the final optimisation process was determined based on this interval range. The search range for the penalty factor α was defined as [3000, 8000], whereas the range for the number of decomposition layers K was established as [3,15]. The number of searched individuals is 10, the maximum iteration number is 20, and the migration coefficient is 40.…”

Section: Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

“…efficiency [1]. A CNC machine tool comprises functional components and foundational elements, with the well-being of the former exerting a direct influence on production efficiency [2,3]. Evaluating the health status of the functional components in CNC machine tools can ameliorate their production efficiency [4].…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Section: Comparison Analysismentioning

confidence: 99%

“…The proposed method in this paper is compared with the importance evaluation method proposed in Reference [49]. When evaluating the importance of machine tool system components, the method in Reference [49] only evaluated the importance of components from the perspective of the fault propagation mechanism and did not consider the structural characteristics of the model. Combining the application examples in this paper, when the model structure characteristics are not considered and only the fault propagation mechanism is considered, the fault propagation probability value of each directed edge at 1500 h can be calculated according to Equations (10)- (13).…”

Section: Comparison Analysismentioning

confidence: 99%

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

Zhang

Liu

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Fault propagation behaviour analysis is the basis of fault diagnosis and health maintenance. Traditional fault propagation studies are mostly based on a priori knowledge of a causality model combined with rule-based reasoning, disregarding the limitations of experience and the dynamic characteristics of the system that cause deviations in the identification of critical fault sources. Thus, this paper proposes a dynamic analysis method for fault propagation behaviour of machining centres that combines fault propagation mechanisms with model structure characteristics. This paper uses the design structure matrix (DSM) to establish the fault propagation hierarchy structure model. Considering the correlation of fault time, the fault probability function of a component is obtained and the fault influence degree of nodes are calculated. By introducing the Copula and Coupling degree functions, the fault influence degree of the edges between the same level and different levels are calculated, respectively. This paper constructs a fault propagation intensity model by integrating the edge betweenness and uses it as an index to analyze real-time fault propagation behaviour. Finally, a certain type of machining centre is taken as an example for specific application. This study can provide as a reference for the fault maintenance and reliability growth of a machining centre.

show abstract

“…Affected by the internal structure and the external working conditions, its use process is more and more complicated. Minor damage to the components will cause a series of cascade reactions in the whole machine [2]. If the component fails and cannot be handled in time, it will affect the whole production schedule.…”

Section: Introductionmentioning

confidence: 99%

Risk Evaluation Method Based on Fault Propagation and Diffusion

Mu,

Zhang,

Zhang

2023

Mathematics

View full text Add to dashboard Cite

The high reliability demand of the machining center emphasizes the accuracy of the fault risk evaluation. In the traditional fault risk evaluation research of the machining center, the influence of fault mode is mostly based on subjective recommendation or does not consider the propagation and diffusion of fault, which makes the risk evaluation results different from the real situation. Therefore, this paper presents a framework to evaluate the fault risk for machining center components. A certain type of machining center is considered as a case study. The fault mode frequency ratio of components is calculated by fault mode analysis. The fault rate calculation is conducted based on the Johnson method. Considering that different fault modes have different influences on fault propagation breadth and depth, the hypergraph theory is used to build a hypernetwork model. The propagation and diffusion influence degree are defined to describe the propagation and diffusion process of faults. Then, the comprehensive influence degree of fault mode is calculated. The risk evaluation is realized by considering the component fault rate, fault mode frequency ratio, and the comprehensive influence degree of fault mode. The method proposed in this paper can provide a reference for the formulation of risk strategies for the machining center.

show abstract

Evaluation for machine tool components importance based on improved LeaderRank

Cited by 5 publications

References 19 publications

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

Risk Evaluation Method Based on Fault Propagation and Diffusion

Contact Info

Product

Resources

About