Identification of influential function modules within complex products and systems based on weighted and directed complex networks

Abstract

As a cost saving and profit-making strategy, a modular design is being employed in developing complex products and systems (CoPS) in recent decades. At the early stage of design, the reliability of a product can be improved by identifying the influential function modules based on the modular function architecture. In this study, the weighted LeaderRank algorithm and susceptible-infected-recovered (SIR) model of weighted and directed complex networks (WDCNs) are employed to identify the influential function modules of modular CoPS at the conceptual design stage. First, the structure of the function module is obtained and is mapped into a WDCN. Second, based on the similarity between the behaviors of nodes in the WDCN and function modules in the CoPS, a node-identification approach based on the weighted LeaderRank algorithm is employed to identify the influential function modules, whose influences are then verified through the SIR model. The influential function modules of a modular large tonnage crawler crane are determined as a case study to demonstrate the effectiveness and validity of the developed method.

Conceptual Scheme Decision Model for Mechatronic Products Driven by Risk of Function Failure Propagation

“…Then, it can be seen that the cutting speed regulating scheme driven by hydraulic pressure is more suitable for the coal mining environment, which can reduce the possibility of the risk of the scheme. Thus, the proposed risk value integration model of the principle Finally, the risk value and the evaluation data of the scheme under PO and CO are integrated, and the data is preprocessed using Equations (34)- (36) to form a decision matrix N, as shown in Equation (44).…”

“…Step A2: The propagation path of function failure is analyzed, and the function structure model is transformed into a directed weighted functional network model. Then, the influence (LR) of subfunction nodes in the directed graph is calculated by using the LeaderRank algorithm [44]. Next, the importance ( ̅ ) of function is transformed into the weight ( ̅) of principle solution through the house of quality (HOQ) [32] and fuzzy number [23].…”

“…The random propagation events of each node will change the influence of the surrounding node failure, which is easy to form large calculation data. In this paper, the LeaderRank algorithm of web page ranking [44] is used to calculate the influence of function failure after a random walk. Thus, the Leaderank algorithm in DFND can be used to ensure the realization of core subfunctions to reduce the risk of design.…”

“…Step C2: Obtain the failure probability of failure mode. From the failure mode distribution database of FMD-97 [44], the failure proportion of each failure mode for the principle solution is found. Then the failure rate ξ kn of the failure mode is calculated by multiplying the Equation (25) with the principle solution of the interval failure rate λ k .…”

“…After that, the relationship between the roller speed and time for the CS simulation model is analyzed, then the time taken from the fluctuation state of the drum speed to the steady-state is obtained and used as the evaluation value of the speed control performance, as shown in Figure 17. Finally, the risk value and the evaluation data of the scheme under PO and CO are integrated, and the data is preprocessed using Equations (34)- (36) to form a decision matrix N, as shown in Equation (44).…”