This study solves the reliability analysis problem of mixed uncertainty in the state probability information of each unit in a multistate system under the condition of common-cause failure. The accuracy and feasibility of the method are demonstrated using an example of the steering hydraulic system of a pipelayer. Then, the β-factor method was used to analyze reliability digital characteristic values when there was common-cause failure between the system units and when each unit failed independently. First, the qualitative language for each unit state performance level in the multistate system was converted into quantitative values through the cloud, and cloud theory was then used to express the uncertainty of the probability of each state of the root node. The cloud model and Bayesian network were combined to form a reliable cloud Bayesian network analysis method. This study combined a cloud model, Bayesian network, and common-cause failure theory to expand a Bayesian network by incorporating cloud model theory. This study focused on mixed uncertainty of the state information in each unit caused by a lack of data, complex structures, and insufficient understanding in a complex multistate system as well as common-cause failure between units. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real‐time results. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Second, the dynamic model parameters inspired by the time‐varying fault characteristics were determined using real‐time operation data analysis. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. A novel physics‐data‐fusion‐based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. Moreover, it provides a foundation for the comprehensive and dynamic reliability analysis and the failure mechanism mining of complex equipment, and it can be used in other engineering applications as well.Ĭoupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Therefore, the proposed method can be used flexibly in the reliability modelling of coupled faults. Exploiting the advantages of conventional models, the coupling relations are quantified, and the false relations are detected based on functional constraints. As a graphical modelling method, it handles the coupling faults by integrating the system functional and fault information. Lastly, the rigorous modelling rules and computing processes are explained based on an actual case. Furthermore, the functional hierarchy of fault determined by IDEF0 is appended. Next, an initial fault model is constructed based on typical fault-relations and coupling forms. First, the meta models are defined to normalize all the atomic faults, coupling relations and coupling forms in modelling. A down-top, deductive modelling method, named as fault-function graph (F2G), is proposed. However, reliability analysis of complex equipment with coupled faults still corresponds to a challenging task, due to unclear coupling mechanism and unsuitable analysis model. Reliability analysis plays a crucial role in revealing the failure causes and determining the improvement measures for reliability growth.
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