Abstract
Rockfall is one of the most serious geological hazards in mountain regions. During the rescue situations after rockfall, the wheel loader, a vital type of modern engineering mechanism, plays an important role in relieving the obstruction of the catastrophic site. Increasing the reliability of the wheel loader during the rescue situation is quite important. This study aims to build a fault diagnosis model based on Bayesian network (BN) to diagnose the probability and path of the fault occurrence in the wheel loader during a rockfall disaster. Meanwhile, to reduce the influence of subjective factors, the fuzzy set theory is introduced into BN. The result showed that the probability of failure of the wheel loader under rockfall disaster is 13.11%. In addition, the key cause of the failure of the wheel loader under the rockfall disaster is the malfunction of mechanical parts. The probability of mechanical component failures in this case is as high as 88%, while the probability of human error is 6%. The research results not only show the ability of the BN to incorporate subjective judgment but also can provide a reference for fault diagnosis and risk assessment of wheel loaders under rockfall disaster conditions.
Highlights
Rockfall is one of the most serious geological disasters in the mountain regions
BN Fault Diagnosis Modeling Process. e modeling of Bayesian network fault diagnosis is mainly divided into the following stages [11]: (i) Determine the node variables and their corresponding states (ii) Determine structure modeling of BN according to the dependency relationship among nodes (iii) Determine parameter modeling of BN which consists of prior probability of root nodes and conditional probability of leaf nodes by using expert knowledge and experience, statistical results of historical simulated, or experimental data (iv) Determine posterior probability by BN inference for system faults identification
For a predictive study of the failure of wheel loader investigated in detail, the Bayesian network model of wheel loader under rockfall disaster has been established, and the prior distribution of each node and conditional probability table (CPT) are determined based on expert experience and fault tree (FT) model. e probability of failure of the wheel loader under the condition of rockfall is obtained through the forward reasoning of the Bayesian network [27]
Summary
Rockfall is one of the most serious geological disasters in the mountain regions. Rockfall disaster refers to the phenomenon of irregular loose rocks falling to the foot of hill slopes or low-lying areas along the vertical or subvertical cliff under the gravity condition, which can lead to severe damage to infrastructure, personnel, and so on [1, 2]. E research on the failure probability of the wheel loader under the condition of rockfall disaster is helpful to determine the fault causes as soon as possible, reduce the rescue risk, and avoid unnecessary loss. E reality is that loaders often work in harsh environments, such as mudslides, rockfalls, landslides, and forest areas with slippery terrain, which make it difficult to find faults and Mathematical Problems in Engineering diagnose faults in time [9] In this context, it is urgent to study fault diagnosis methods for the loaders under specific disaster situations so as to provide a theoretical basis for the environmental stability of the loader in the future [10]. Through the analysis of the posterior probability, the influence degree of each causative factor on the fault occurrence was quantified, and the maximum causal chain of the wheel loader was obtained
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
