用聚类特征提升压缩机故障预测准确率
Predictive Maintenance Study for High-Pressure Industrial Compressors: Hybrid Clustering Models
- 从传感器数据中提取聚类特征，增强分类模型
- 故障检测准确率平均提升4.87%
- 适合工业设备运维与故障预警场景
本研究提出一种针对高压工业压缩机的预测性维护策略，利用传感器数据和由无监督聚类生成的特征，融入分类模型以提升故障检测的准确性与效率。数据预处理后，通过调优敏感聚类参数，筛选出最能捕捉数据时空特性的算法。采用归一化互信息（NMI）和调整兰德指数（ARI）等质量指标评估聚类效果，选取在区分正常与异常状态方面表现最优的算法。这些聚类特征被用于增强回归模型，使故障检测准确率平均提升4.87%。尽管训练时间平均减少22.96%，但该降幅未达统计显著性，且不同算法间存在差异。交叉验证及关键性能指标验证了基于聚类特征的预测性维护模型的有效性。
原文摘要 · Abstract (English)
This study introduces a predictive maintenance strategy for high pressure industrial compressors using sensor data and features derived from unsupervised clustering integrated into classification models. The goal is to enhance model accuracy and efficiency in detecting compressor failures. After data pre processing, sensitive clustering parameters were tuned to identify algorithms that best capture the dataset's temporal and operational characteristics. Clustering algorithms were evaluated using quality metrics like Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI), selecting those most effective at distinguishing between normal and non normal conditions. These features enriched regression models, improving failure detection accuracy by 4.87 percent on average. Although training time was reduced by 22.96 percent, the decrease was not statistically significant, varying across algorithms. Cross validation and key performance metrics confirmed the benefits of clustering based features in predictive maintenance models.
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