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To address the challenges of low diagnostic accuracy and poor interpretability for minority fault classes caused by imbalanced data distribution in coal mill pulverizing systems of coal-fired power plants, a fault diagnosis method integrating SMOTE data enhancement, Dirichlet prior smoothing, and Bayesian networks is proposed. The SMOTE technology expands the feature space of minority fault samples to alleviate data scarcity, while Dirichlet prior smoothing optimizes conditional probability estimation in Bayesian networks, resolving zero-probability issues caused by insufficient samples. A hierarchical Bayesian network architecture is constructed by incorporating domain knowledge and data-driven structure learning, enabling a dual-mode diagnosis strategy that combines rapid fault node inference with indirect attribute node analysis. The experimental results based on real industrial data demonstrate that the proposed method achieves high diagnostic accuracy and interpretability under imbalanced data scenarios. The solution provides real-time performance, precision, and transparency for coal mill fault diagnosis, offering significant engineering value.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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