A Token-FCM based risk assessment method for complex engineering designs

TL;DR

This work tackles the challenge of risk assessment in complex engineering designs where causal effects are bidirectional and time-delayed. It introduces Token-FCM, a token-augmented fuzzy cognitive map that enforces a chronological update of nodes to capture dynamic, two-way risk relations, and it couples this with a fuzzy-group initialization workflow using probabilistic linguistic terms. The method yields a systematic pipeline: initialize with PLTs and GDM, simulate with time-delayed token dynamics to obtain dynamic risk priorities (DRPN), and synthesize results into actionable design decisions; validated on a diesel engine design for horizontal directional drilling machines. The findings demonstrate that Token-FCM can reveal dynamic interdependencies and time-delay effects that static methods like FMEA may miss, guiding targeted reliability improvements and system-level risk mitigation.

Abstract

Engineering design risks could cause unaffordable losses, and thus risk assessment plays a critical role in engineering design. On the other hand, the high complexity of modern engineering designs makes it difficult to assess risks effectively and accurately due to the complex two-way, dynamic causal-effect risk relations in engineering designs. To address this problem, this paper proposes a new risk assessment method called token fuzzy cognitive map (Token-FCM). Its basic idea is to model the two-way causal-risk relations with the FCM method, and then augment FCM with a token mechanism to model the dynamics in causal-effect risk relations. Furthermore, the fuzzy sets and the group decision-making method are introduced to initialize the Token-FCM method so that comprehensive and accurate risk assessments can be attained. The effectiveness of the proposed method has been demonstrated by a real example of engine design for a horizontal directional drilling machine.

Figures (11)

• Figure 1: Process flow diagram of the emergency core cooling system.
• Figure 2: Internal block diagram of spacecraft power subsystem using the SysML.
• Figure 3: An example of FCM with five nodes and seven arcs.
• Figure 4: Two states of the nodes: (a) activated and (b) inactivated. The black dots represent tokens, and the same for the following figures.
• Figure 5: Tokens behavior under one-to-one causal-effect relation: (a) One-way one-to-one causal-effect relation; (b) Two-way one-to-one causal-effect relation.