Fuzzy Logical Approaches for Reliability Assessment in Industrial Systems

Fuzzy logical approaches have emerged as a powerful means to quantify and manage the inherent uncertainty in industrial reliability engineering. Unlike conventional probabilistic methods that rely on precise failure rates and repair times, fuzzy logic accommodates imprecise, linguistic and incomplete information drawn from expert judgement, historical records and sensor outputs. In industrial contexts ranging from power generation to mining and manufacturing, fuzzy models map input variables such as component degradation, environmental conditions and maintenance interventions into fuzzy sets with graded membership functions. These sets are then processed through rule-based inference engines or combined with optimisation routines to yield reliability indices, availability forecasts and risk priorities. Extensions such as intuitionistic fuzzy sets introduce separate membership and non-membership grades to capture hesitation, while hybrid neuro-fuzzy systems leverage learning algorithms to refine rule bases and membership parameters. Multi-criteria decision-making techniques are often integrated with fuzzy inference to rank weak points, allocate maintenance resources and support life-cycle management. Taken together, these methods bolster resilience in complex systems by enabling data-driven, yet flexible, reliability assessments that reflect real-world ambiguity and guide proactive maintenance strategies.

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Fuzzy Logical Approaches for Reliability Assessment in Industrial Systems publication trend

The graph below shows the total number of articles in fuzzy logical approaches for reliability assessment in industrial systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fuzzy logic: A mathematical framework in which variables may have degrees of truth between 0 and 1, enabling the representation of vague or imprecise information.

Intuitionistic fuzzy set: An extension of fuzzy sets characterised by a membership function, a non-membership function and a hesitation margin to capture uncertainty more comprehensively.

Neuro-fuzzy inference system: A hybrid approach that combines neural network learning with fuzzy rule-based reasoning to adaptively refine membership functions and rules from data.

Lambda–Tau methodology: A fuzzy reliability technique that employs fuzzy failure rates (“lambda”) and repair times (“tau”) within series and parallel system expressions.

Availability: The probability or proportion of time a system is operational and capable of performing its intended function, often assessed under fuzzy conditions to account for imprecision.

References

1. Reliability analysis of turbine unit using Intuitionistic Fuzzy Lambda-Tau approach. Reports in Mechanical Engineering (2023).
2. A Model for Determining Fuzzy Evaluations of Partial Indicators of Availability for High-Capacity Continuous Systems at Coal Open Pits Using a Neuro-Fuzzy Inference System. Energies (2023).
3. Statistical Fuzzy Reliability Assessment of a Blended System. Axioms (2023).