This dataset supports a quantitative reliability modeling method for multi-state elements, developed by Zhi-Qiang Li and published in 2019. It includes data for analyzing systems under perfect repair, imperfect repair, and condition-based maintenance scenarios. The data was used to build a dynamic Bayesian network model from a dynamic fault tree for a control unit example.
Use Cases
- Calculate state probabilities for a multi-state element under different maintenance modes like perfect repair or condition-based maintenance.
- Construct conditional probability tables for series and parallel systems using conditional degradation probabilities from the model.
- Identify weak nodes within a control unit system by analyzing the translated dynamic fault tree and Bayesian network results.
- Verify reliability values for a control unit by referring forward through the dynamic Bayesian network model.
Strengths
- Data is associated with a peer-reviewed methodological paper published in 2019.
- The modeling approach integrates multiple established techniques: Markov processes, dynamic Bayesian networks, and dynamic fault trees.
- The dataset is licensed under CC0-1.0, allowing for unrestricted reuse.
Limitations
- The specific data format, size, row count, and column structure are unknown.
- The dataset is derived from a single illustrative example (a control unit), limiting generalizability.
- The data's age (from 2019) may not reflect current component failure rates or maintenance practices.
Provenance
- Source
- Dryad digital repository.
- Collection Method
- Data generated from a theoretical reliability modeling and analysis method applied to a control unit example.
- Time Range
- null
- Freshness
- null
- Geography
- null