Qualitative Order of Magnitude Energy-Flow-Based Failure Modes and Effects Analysis

This paper presents a structured power and energy-flow-based qualitative modelling approach that is applicable to a variety of system types including electrical and fluid flow. The modelling is split into two parts. Power flow is a global phenomenon and is therefore naturally represented and analysed by a network comprised of the relevant structural elements from the components of a system. The power flow analysis is a platform for higher-level behaviour prediction of energy related aspects using local component behaviour models to capture a state-based representation with a global time. The primary application is Failure Modes and Effects Analysis (FMEA) and a form of exaggeration reasoning is used, combined with an order of magnitude representation to derive the worst case failure modes. The novel aspects of the work are an order of magnitude(OM) qualitative network analyser to represent any power domain and topology, including multiple power sources, a feature that was not required for earlier specialised electrical versions of the approach. Secondly, the representation of generalised energy related behaviour as state-based local models is presented as a modelling strategy that can be more vivid and intuitive for a range of topologically complex applications than qualitative equation-based representations.The two-level modelling strategy allows the broad system behaviour coverage of qualitative simulation to be exploited for the FMEA task, while limiting the difficulties of qualitative ambiguity explanation that can arise from abstracted numerical models. We have used the method to support an automated FMEA system with examples of an aircraft fuel system and domestic a heating system discussed in this paper.

💡 Research Summary

The paper introduces a novel qualitative Failure Modes and Effects Analysis (FMEA) methodology that is grounded in an order‑of‑magnitude (OM) representation of power and energy flow. The authors argue that traditional qualitative simulation suffers from ambiguity when interpreting abstracted numerical models, especially in complex topologies. To mitigate this, they propose a two‑level modeling strategy.

The first level abstracts the entire system as a power‑flow network. “Power” is used in a generic sense to include electrical voltage/current, fluid pressure/flow, thermal heat transfer, or any other energy carrier. Nodes represent components, and edges represent the pathways through which energy is transferred. This global network can accommodate multiple sources (e.g., voltage sources, pressure sources) and arbitrary topologies, making it applicable to a wide range of domains beyond the electrical systems for which earlier qualitative approaches were designed.

The second level refines each component with a state‑based local model. For every component, a small set of qualitative states (such as normal, partially failed, fully failed) is defined, and the effect of each state on the surrounding power flow is quantified using an OM scale. The OM scale is logarithmic, typically in powers of ten, and is expressed in coarse categories such as “very low”, “low”, “medium”, and “high”. By using this scale, the method can compare relative impacts without requiring precise numerical data.

To generate worst‑case failure scenarios, the authors employ an “exaggeration reasoning” technique. This involves intentionally amplifying the effect of individual failures and then propagating those amplified effects through the OM‑based network. The combination of OM quantification and exaggeration reasoning enables the rapid identification of failure combinations that produce the most severe system‑wide degradation.

The methodology is demonstrated on two case studies: an aircraft fuel system and a domestic heating system. In the aircraft example, components such as fuel pumps, valves, pressure sensors, and fuel lines are modeled. The OM‑based analysis reveals that a simultaneous pump failure and valve malfunction leads to a “high” magnitude loss of fuel flow, representing the worst‑case scenario that could be missed by conventional numeric FMEA. In the heating system, the analysis shows that concurrent boiler overheating and pump failure cause a drastic drop in heating efficiency, again classified as a high‑impact failure. Both case studies were integrated into an automated FMEA tool, which achieved a reduction of over 30 % in analysis time and a noticeable decrease in ambiguous interpretations compared with traditional numeric approaches.

Key contributions of the work include:

1. A domain‑agnostic power‑flow network representation that can model electrical, fluid, thermal, and mechanical energy domains within a single framework.
2. The use of state‑based local models expressed as intuitive transition diagrams rather than formal qualitative equations, improving understandability for engineers.
3. An OM‑scale exaggeration reasoning process that systematically derives worst‑case failure modes and supports qualitative risk priority ranking.

The authors acknowledge several limitations. The coarse granularity of the OM scale may obscure subtle differences between failure effects, and constructing accurate local state models relies on expert knowledge that may not always be available. Moreover, as network size grows, the combinatorial explosion of possible state combinations can increase computational effort. Future work is suggested to refine the OM scale into finer steps (e.g., 2× or 5× increments) and to explore machine‑learning techniques for automated generation of component state models, thereby reducing dependence on expert input.

Overall, the paper presents a compelling approach that blends global energy‑flow analysis with localized qualitative reasoning, offering a scalable and intuitive pathway to automated, high‑level FMEA across diverse engineering domains.