Reliability Allocation Methods for Mechanical Systems

Reliability allocation methods assign an overall system reliability target to individual components or subsystems, ensuring that a mechanical system meets performance and safety requirements with optimal cost and design effort. Traditional approaches include statistical techniques such as Weibull or exponential analysis and rule-based schemes like modified Aeronautical Radio Inc. (ARINC) methods and failure mode and effects analysis (FMEA). Recent advances embrace probabilistic models to capture failure correlations, multi-criteria decision making to balance technical, environmental and economic factors, and fuzzy or grey-based frameworks to address uncertainty and expert judgement. These methods apply to diverse mechanical domains—from CNC machining tools and hydraulic excavators to wind turbines—enhancing design robustness, reducing maintenance costs and supporting sustainable manufacturing. Configurations spanning series, parallel and series–parallel arrangements are accommodated, while hierarchical and cost-oriented allocations enable systematic prioritisation of reliability improvements at the subsystem level.

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Researchers have developed a reliability allocation method for CNC lathes that accounts for statistical dependence among subsystem failures by employing Copula theory. By modelling failure times with Weibull or exponential distributions and selecting a Gumbel Copula to capture correlation, the allocation algorithm adjusts failure‐rate targets to reflect real operational conditions, leading to more accurate reliability forecasting and reduced design costs.

For hydraulic excavators, a hybrid allocation approach integrates Weibull analysis, a modified ARINC method and systematic FMEA to set subsystem reliability goals in early design stages. Expert panels validate feasibility via FMEA, ensuring that allocated targets align with technical capabilities and historical performance. This method yields practical reliability benchmarks for excavator subsystems and is adaptable to other heavy-machinery applications.

A model for wind turbine generator systems combines a fuzzy analytic hierarchy process with an entropy weighting scheme to derive reliability weights across six influencing factors, including technical level, working environment and component importance. This multi-factor framework produces lower allocation demands than conventional series‐system methods, optimising reliability distribution and supporting the design of more resilient renewable-energy machinery.

Reliability Allocation Methods for Mechanical Systems publication trend

The graph below shows the total number of articles in reliability allocation methods for mechanical systems across all publications each year (not limited to Nature Index journals).

Technical terms

Reliability allocation: The process of distributing an overall system reliability requirement among subsystems or components to meet a specified performance target.

Subsystem: A distinct functional unit within a larger mechanical system, subject to its own reliability and performance criteria.

Copula model: A statistical tool that links marginal distributions of individual variables to form a joint distribution, capturing dependence among subsystem failures.

Fuzzy Analytic Hierarchy Process (FAHP): A decision-making technique that extends the analytic hierarchy process by incorporating fuzzy logic to handle uncertainty in expert judgements.

Weibull distribution: A continuous probability distribution commonly used to model time-to-failure data in reliability engineering, characterised by scale and shape parameters.

References

1. A Reliability Allocation Method of CNC Lathes Based on Copula Failure Correlation Model. Chinese Journal of Mechanical Engineering (2018).
2. A Comprehensive Method of Apportioning Reliability Goals for New Product of Hydraulic Excavator. Mathematical Problems in Engineering (2019).
3. Research on the Reliability Allocation Method for a Wind Turbine Generator System Based on a Fuzzy Analytic Hierarchy Process Considering Multiple Factors. IEEE Access (2021).