用可解释的相似性推理预测超滤膜寿命，提升运维可信度。
Predictive Maintenance for Ultrafiltration Membranes Using Explainable Similarity-Based Prognostics
- 基于物理健康指数与模糊相似度匹配历史数据
- 平均误差仅4.50个运行周期，精度高
- 规则透明可解释，适合工业现场信任部署
在反渗透海水淡化中，超滤（UF）膜因污染退化导致性能下降和高昂停机成本。多数工厂依赖定期维护，因现有预测模型多采用不可解释的机器学习方法，缺乏可解释性与操作员信任。本研究提出一种基于模糊相似性推理的可解释性寿命预测框架，用于估算UF膜剩余使用寿命（RUL）。通过跨膜压差、通量和阻力构建物理启发的健康指数，并用高斯隶属函数进行模糊化处理。利用相似性度量识别与当前状态相似的历史退化轨迹，将RUL预测建模为Takagi-Sugeno模糊规则，每条规则对应一个历史实例，形成基于相似性的加权估计。在工业级UF系统12,528个运行周期上测试，均方绝对误差达4.50个周期，且生成的规则库与专家认知一致，具备高度可解释性。
原文摘要 · Abstract (English)
In reverse osmosis desalination, ultrafiltration (UF) membranes degrade due to fouling, leading to performance loss and costly downtime. Most plants rely on scheduled preventive maintenance, since existing predictive maintenance models, often based on opaque machine learning methods, lack interpretability and operator trust. This study proposes an explainable prognostic framework for UF membrane remaining useful life (RUL) estimation using fuzzy similarity reasoning. A physics-informed Health Index, derived from transmembrane pressure, flux, and resistance, captures degradation dynamics, which are then fuzzified via Gaussian membership functions. Using a similarity measure, the model identifies historical degradation trajectories resembling the current state and formulates RUL predictions as Takagi-Sugeno fuzzy rules. Each rule corresponds to a historical exemplar and contributes to a transparent, similarity-weighted RUL estimate. Tested on 12,528 operational cycles from an industrial-scale UF system, the framework achieved a mean absolute error of 4.50 cycles, while generating interpretable rule bases consistent with expert understanding.
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