Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially for semi-supervised learning which utilizes unsupervised learning as a feature extraction mechanism to aid the supervised part when only a small number of labels are available. This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems. Firstly, thorough data analysis and feature learning were carried out to understand the open-sourced hydraulic condition monitoring dataset. Then, various methods were implemented and evaluated including traditional stand-alone semi-supervised learning models (e.g., one-class SVM, Robust Covariance), ensemble models (e.g., Isolation Forest), and deep neural network based models (e.g., autoencoder, Hierarchical Extreme Learning Machine (HELM)). Typically, this study customized and implemented an extreme learning machine based semi-supervised HELM model and verified its superiority over other semi-supervised methods. Extensive experiments show that the customized HELM model obtained state-of-the-art performance with the highest accuracy (99.5%), the lowest false positive rate (0.015), and the best F1-score (0.985) beating other semi-supervised methods.

翻译：基于状态的维护在液压系统中日益重要。然而，这些系统的异常检测仍具挑战性，尤其是由于异常数据稀缺且标注这类数据既繁琐甚至危险。因此，利用无监督或半监督方法尤为可取，特别是半监督学习，它借助无监督学习作为特征提取机制，在仅有少量标签时辅助监督部分。本研究系统比较了应用于液压状态监测系统异常检测的半监督学习方法。首先，通过深入的数据分析和特征学习理解开源液压状态监测数据集。随后，实施并评估了多种方法，包括传统独立半监督学习模型（如一类支持向量机、鲁棒协方差）、集成模型（如孤立森林）以及基于深度神经网络的模型（如自编码器、分层极限学习机）。特别是，本研究定制并实现了一种基于极限学习机的半监督HELM模型，并验证了其相对于其他半监督方法的优越性。大量实验表明，定制的HELM模型获得了最优性能，准确率最高（99.5%）、假阳性率最低（0.015）且F1分数最佳（0.985），优于其他半监督方法。