Author(s)

Rui Guo ¹

Affiliation(s)

¹ China Academy of Machinery Science and Technology Group, Beijing, China

Corresponding Author

Rui Guo

ABSTRACT

The hoisting trolley system of an urban bridge erecting machine is a strongly coupled electro-mechanical-hydraulic subsystem with very few field samples for its critical components. In real-time reliability assessment, two issues are normally treated separately: low-level multi-source condition monitoring and system-level probabilistic reasoning. To bridge this gap, this paper proposes a real-time reliability assessment method based on a three-state Noisy-OR Bayesian network (BN) coupled with XGBoost evidence fusion. First, the state space of the bottom-layer Bayesian network nodes is extended from the conventional Normal/Failed binary form to a Normal/Degraded/ Failed three-state form, with a hybrid modelling strategy in which mechanical/hydraulic components are described by three states and electrical/logical components remain binary. Second, a simplified three-state Noisy-OR risk-propagation rule is adopted to aggregate the Normal, Degraded and Failed probabilities of bottom-layer nodes upward into system-level state probabilities. Finally, XGBoost is used to convert multi-source monitoring features into an initial SoftMax three-class probability vector; after soft-evidence stabilization, the corresponding prototype evidence vector is injected into the leaf nodes of the Bayesian network, closing the loop from data perception to topological reasoning. Taking a hoisting-trolley fault-diagnosis test platform as the case study, 200 days of operating data were obtained from its 3-D model and the corresponding scaled test rig. Results show that the proposed framework triggers a yellow alarm on day 74, i.e. 25 days after the first soft-fault injection and 14 days after the second. After excluding the interpretable ∼10-day con- formation delay introduced by the soft-evidence stabilization module, the effective risk-response time is reduced to several days, and the framework escalates to a red alarm on day 78, providing a quantitative "when-to-intervene" decision basis for on-site predictive maintenance.

KEYWORDS

Urban bridge erecting machine; Bayesian network; three-state Noisy-OR; XGBoost; Real-time reliability assessment

CITE THIS PAPER

Rui Guo. A Real-Time Reliability Assessment Method for Urban Bridge Erecting Machines Based on a Three-State Noisy-OR Bayesian Network and XGBoost Evidence Fusion. Journal of Civil Engineering and Urban Planning (2026). Vol. 8, No.2, 15-24. DOI: http://dx.doi.org/10.23977/jceup.2026.080202.

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Sponsors, Associates, and Links

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• Journal of Sustainable Development and Green Buildings
  
  
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• Journal of Municipal Engineering
  
  
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• Computer Aided Architecture Design