Degradation Modeling in Reliability Engineering

Degradation modeling in reliability engineering characterises the progressive deterioration of a system’s performance over time to predict failure and optimise maintenance. Central to this discipline are stochastic process models—including the gamma process, Wiener process and inverse Gaussian process—which capture monotonic or non-monotonic wear, corrosion and fatigue phenomena. Multivariate approaches employ copula functions or mixed-effects frameworks to account for dependencies among multiple degradation indicators, while Bayesian hierarchical and particle-filter techniques enable fusion of sparse or noisy data from heterogeneous sources. Recent advances address bounded degradation behaviour, time- and state-dependent increments, and the integration of sensor measurements with historical records. Applications span aerospace engines, power-train components, electronic devices and infrastructure assets, where remaining useful life estimates guide condition-based maintenance, reduce downtime and improve safety.

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Research from all publishers

Researchers have developed an improved Gibbs sampling framework for noisy transformed gamma processes to predict degradation growth and estimate remaining useful life. By modelling measurement error and combining Expectation–Maximisation with Markov chain Monte Carlo, this method yields robust parameter estimates and quantifies false-alarm probabilities in applications such as valve erosion monitoring. A complementary study introduces a bounded transformed gamma process, treating the physical upper bound as an estimable parameter. This model better reflects finite-size constraints in wear phenomena and demonstrates superior fit over unbounded counterparts for cylinder-liner wear in marine engines. For complex assets with multiple indicators, a joint remaining useful life prediction method employs kernel-smoothing particle filters augmented by parameter correlation. By capturing dependencies among degradation parameters, this approach enhances predictive accuracy on benchmark aero-propulsion datasets and supports more reliable prognostics.

Degradation Modeling in Reliability Engineering publication trend

The graph below shows the total number of articles in degradation modeling in reliability engineering across all publications each year (not limited to Nature Index journals).

Technical terms

Degradation process: Cumulative deterioration over time of a performance characteristic leading to failure.

Gamma process: Monotonic stochastic process widely used to model non-decreasing wear or corrosion.

Wiener process: Continuous stochastic process with independent Gaussian increments, applied to diffusion-type degradation.

Copula: Mathematical function linking marginal distributions to model dependence in multivariate degradation data.

Remaining useful life (RUL): Estimated time until a component’s degradation crosses a failure threshold.

References

1. Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas. Mathematical Problems in Engineering (2014).
2. Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation. Reliability Engineering & System Safety (2022).
3. Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation. IEEE Access (2020).
4. Reliability assessment for systems with two performance characteristics based on gamma processes with marginal heterogeneous random effects. Eksploatacja i Niezawodnosc - Maintenance and Reliability (2016).
5. Bayesian Hierarchical Model-Based Information Fusion for Degradation Analysis Considering Non-Competing Relationship. IEEE Access (2019).
6. Multivariate Storage Degradation Modeling Based on Copula Function. Advances in Mechanical Engineering (2014).
7. A transformed gamma process for bounded degradation phenomena. Quality and Reliability Engineering International (2022).