Fault diagnosis method for attached lifting scaffold based on support vector machine

Si, Luo; Qiao, Aike; Tang, Qingguo

doi:10.1049/joe.2019.1177

Cited by 1 publication

(2 citation statements)

References 10 publications

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“…The hierarchical digraph model D 2 of failure propagation for B-axis turntable in Table 4. Then, according to Equations ( 5) to (11), the estimation values α and β, and the test statistic D i can be obtained. Set the significance level 𝜌 = 0.05, and then the critical value 𝐷 𝑖,𝜌 can be obtained by querying the critical value table.…”

Section: Establish Failure Probability Functionmentioning

confidence: 99%

“…For instance, Ma, Chu 10 proposed an integrated deep learning model based on multi‐objective optimization to detect and diagnose the failures of rotor‐bearing systems. Luo, Qiao, Tang 11 developed a new failure diagnosis method based on improved particle swarm optimization least squares‐support vector machine (LS‐SVM) for diagnosing the failures of scaffolding jamming automatically. Pang, Yu, Song 12 improved the Bayesian network and further applied it to construct a failure diagnosis strategy for a lock mechanism system.…”

Section: Introductionmentioning

confidence: 99%

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A failure tracing method with hierarchical digraph and meta action for complex electromechanical systems

Xiao

Huang

Zhang

et al. 2022

Quality & Reliability Eng

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As an important method for analyzing the failure pervasion mechanism, failure tracing is a powerful tool for locating the failure causes in reliability analysis of complex electromechanical systems (EMS). However, most of the previous studies mainly focus on the failure tracing and identifying of part levels, which are often complicated. Besides, they cannot reflect the product feature that motion determines product function. To fill these gaps, this paper develops a combined dynamic and static failure tracing model based on meta action and hierarchy digraph model. First, the failure propagation directed digraph is constructed based on the structure of the EMS and the interactions of action signals, and the interpretative structural model (ISM) is further applied to obtain the hierarchy failure propagation model based on the directed digraph. Second, the combined dynamic and static failure propagation intensity between meta actions is measured based on the failure propagation probability and edge betweenness, which can effectively extract the crucial pathway and crucial meta actions to facilitate failure tracing. Third, a failure mode and effect analysis (FMEA) method considering the weights of risk elements and experts is proposed to estimate the risks of the crucial meta actions’ failure modes, which can help engineers develop countermeasures for preventing or eliminating failures. Finally, a case study and several comparisons are presented to illustrate the feasibility and benefits of the developed model.

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Section: Establish Failure Probability Functionmentioning

confidence: 99%