design durability tests

Quality engineers lack systematic degradation modeling methodologies, hindering the accuracy and practical implementation of reliability assessment. Method: This study establishes an industrially oriented degradation analysis framework that unifies diverse degradation data sources—including repeated measurements and accelerated destructive testing—and integrates path models (e.g., general path models) with stochastic process models (e.g., Wiener processes), augmented by Bayesian and likelihood-based statistical inference techniques. A standardized modeling workflow and lifetime prediction toolkit are implemented in R/Python. Contribution/Results: The framework bridges the gap between theoretical degradation modeling and engineering practice, significantly improving the accuracy and reproducibility of reliability predictions for complex systems. It delivers an actionable guideline and open-source software support for industry-standardized deployment, enabling robust, traceable, and scalable reliability engineering.

The PHM (Prognostics and Health Management) community has long suffered from a lack of systematically evaluated, freely accessible degradation datasets. Method: This work establishes the first multi-dimensional unified evaluation framework for PHM datasets, incorporating critical dimensions—data provenance, equipment types, sensor configurations, failure modes, and annotation completeness—integrated with structured metadata analysis, cross-dataset comparative assessment, task-specific PHM mapping, and physics-of-failure-informed semantic annotation. Contribution/Results: We systematically curate and analyze 32 high-quality public datasets, identifying 11 recurrent deficiencies. Based on this analysis, we provide task-oriented data selection guidelines and benchmarking recommendations. This study fills a critical gap in systematic surveys of PHM public data resources, explicitly delineates the applicability boundaries and modeling limitations of existing datasets, and has been widely cited and adopted within the PHM research community.

Existing mission profile modeling approaches for electric and autonomous vehicles provide only aggregated histograms of single stress parameters (e.g., temperature or voltage), lacking multidimensional coupling, temporal evolution, and full-lifecycle characterization. To address this, we propose a novel mission profile modeling framework grounded in functional temporal modeling, which jointly captures the dynamic evolution of multiple stress parameters—including temperature, humidity, and voltage. The framework supports configurable time-granularity sampling and user-defined quantile analysis, and embeds anomaly detection and data integrity protection mechanisms to ensure compliant, trustworthy data sharing across the supply chain (suppliers–OEMs–end users). For the first time, our framework enables high-fidelity, quantifiable, multi-stress-coupled mission profile modeling with built-in security and collaborative capabilities.

Safety-critical small Unmanned Aircraft Systems (sUAS) lack systematic, standardized testing processes that are tightly integrated with safety analysis. Method: This paper proposes a requirement-driven coupled testing framework, introducing the novel triadic paradigm of “requirements–simulation testing–safety analysis.” It employs formal requirement modeling with bidirectional traceability, a simulation–hardware-in-the-loop cooperative testing architecture, scenario-driven test case generation, and deep integration of safety analysis methods (e.g., Fault Tree Analysis and System-Theoretic Process Analysis). Contribution/Results: Evaluated on an sUAS case study, the framework significantly improves simulation fidelity coverage and requirement coverage, enables end-to-end safety evidence generation, fills the gap in standardized sUAS testing procedures, and delivers reproducible, verifiable testing assets to support airworthiness certification.

To address the modeling challenge of degradation processes in high-reliability systems—characterized by both non-Markovian memory effects and unit-to-unit heterogeneity—this paper proposes an accelerated degradation statistical model integrating fractional stochastic processes with random effects. Departing from the conventional independent-increment assumption, the model employs fractional Brownian motion to capture temporal dependence in degradation paths and incorporates a mixed-effects structure to account for individual variability. Parameter estimation is performed via a hybrid approach combining the EM algorithm and Bayesian inference. Experimental validation on turbine blade and electrolytic capacitor datasets demonstrates a 32% reduction in remaining useful life prediction error and an average R² of 0.91 for individual degradation trajectory fitting, significantly improving early fault detection accuracy. This work establishes the first unified framework jointly incorporating fractional-order processes and random effects, offering a novel paradigm for reliability assessment of non-Markovian degradation systems.

This study addresses optimal design for simple step-stress accelerated life tests involving two independent competing failure modes. Within a Bayesian framework, it integrates the cumulative exposure model with a log-linear stress–life relationship and employs a pre-posterior variance minimization criterion to achieve exact small-sample optimization without relying on large-sample approximations. The work innovatively extends the quantile reparameterization approach—previously limited to single-failure-mode settings—to the competing risks context, enabling priors to be directly elicited from engineering knowledge. Posterior inference is conducted via Stan’s No-U-Turn Sampler, and Monte Carlo search over a candidate design grid identifies the optimal test plan. Validation using real data from solar lighting devices demonstrates that the optimal low-stress level consistently aligns closely with normal use conditions, yielding robust results.

This study addresses the challenge that conventional static testing fails to account for device-to-device variability and the dynamic interplay among multiple degradation mechanisms—such as bias temperature instability (BTI), electromigration (EM), and time-dependent dielectric breakdown (TDDB)—in semiconductor reliability assessment. To overcome this limitation, the work formulates reliability qualification as a partially observable sequential decision-making problem and introduces an adaptive testing framework that integrates Monte Carlo tree search with simulated annealing (MCTS-SA) and an extended Kalman filter (EKF). This approach enables closed-loop optimization of stress conditions through real-time belief-state estimation, dynamically balancing accurate degradation characterization against the risk of catastrophic failure while maintaining required safety margins. Experimental results demonstrate that after 5,000 iterations, the characterization success rate improves from 20% to 54%, yielding a cumulative gain of 39%; at termination of the optimal test sequence, the EM and TDDB damage fractions reach 0.564 and 0.537, respectively.

This study addresses the limitations of traditional Markovian approaches in availability analysis of repairable systems, which rely on the restrictive assumption of exponential distributions and thus fail to accurately capture real-world failure and repair time characteristics. To overcome this constraint, the work introduces the Lindley distribution—represented via phase-type approximation—into availability modeling for the first time, establishing a general analytical framework applicable to both single-component and n-component series-parallel systems. Closed-form expressions for time-dependent and steady-state availability are derived, along with an exact computation of mean time to repair. Numerical experiments demonstrate that incorporating non-exponential repair times significantly influences system reliability metrics, thereby underscoring the practical relevance and theoretical contribution of the proposed methodology.

This study addresses the challenge of effectively monitoring early-stage agent systems, where structural flaws often obscure task-level errors. The authors propose a three-dimensional (quality, suitability, efficiency) and three-granularity (intra-run, inter-run, structural) monitoring and triaging framework tailored for low-maturity agent systems. They introduce a novel system maturity staging model based on the coefficient of variation and monitoring granularity, integrated with a severity classification adapted from FMEA to guide human review. The resulting transferable monitoring architecture supports document-driven, multi-stage workflows, enhanced by a synthetic testbed with controlled error injection. Experimental results demonstrate that structural defects significantly mask task-level signals; 97% of issues can be automatically traced, with only 2% requiring human intervention, and each granularity level precisely identifies its corresponding defect type (coefficients of variation: 0.02, 1.25, and 0.00, respectively).