Abstract: An improved Weighted One Class Support Vector Machine (WOCSVM) algorithm—Local Density WOCSVM (LD-WOCSVM) was proposed to solve the problems of poor classification performance and weak robustness of the data-based turboshaft engine fault detection algorithm. Firstly, for each training sample, k nearest neighbor samples contained in the body of the ball were selected, and the ball was centered on this sample with a radius of 2% of the Mahalanobis distance from the center of all training samples to the farthest samples. Secondly, the distance from this sample to the center of selected k training samples was used to evaluate the probability that this sample is a fault sample, and based on this, the normalized distance was used to calculate the weight of the corresponding sample. An algorithm of weight calculation based on rapid clustering namely FCLD-WOCSVM was proposed to deal with the problem that the present algorithms were not able to reflect the characteristics of sample distribution very well. In this algorithm, by obtaining two parameters of the local density of each training sample and the distance from the sample to the high local density, the distribution position of this sample was determined, and the weight of the sample was calculated by using the two obtained parameters. The classification performance of both algorithms was improved by assigning small weights to the possible fault samples. In order to verify the effectiveness of the two algorithms, simulation experiments were carried out on 4 UCI datasets and T700 turboshaft engines respectively. Experimental results show that, compared with Adaptive WOCSVM (A-WOCSVM) algorithm, LD-WOCSVM algorithm improves the AUC （Area Under the Curve） value by 0.5%, and FCLD-WOCSVM algorithm improves the G-mean （Geometric mean） by 12.1%. These two algorithms can be used as candidate algorithms for turboshaft engine fault detection.

Key words: turboshaft engine, fault detection, One-Class Support Vector Machine (OCSVM), local density, Mahalanobis distance

摘要： 针对基于数据的涡轴发动机故障检测算法的分类性能较差、鲁棒性不强的问题，提出一种改进的加权一类支持向量机（WOCSVM）算法——基于局部密度的WOCSVM （LD-WOCSVM）算法。首先，对于每个训练样本，选取以该样本为中心，以全体训练样本中心到距离最远样本之间马氏距离的百分之二为半径的球体内所包含的k个近邻样本；其次，以该样本到选定的k个训练样本的中心的距离大小来评估该样本为故障样本的可能性，并以此为依据，使用经过归一化的距离来计算对应样本的权重。针对目前算法不能很好地反映样本分布特点的问题，提出了一种基于快速聚类的权重计算方法并将其命名为FCLD-WOCSVM。该算法通过求取每个训练样本的局部密度和该样本到高局部密度的距离两个参数，来确定该样本的分布位置，并利用求得的两个参数来计算该样本的权重。两种算法都是通过对可能的故障样本分配较小的权重来增强算法的分类性能。为了验证算法的有效性，分别在4个UCI数据集和T700涡轴发动机上进行仿真实验。实验结果表明，与自适应WOCSVM （A-WOCSVM）算法相比，LD-WOCSVM算法在AUC值上提高了0.5%，FCLD-WOCSVM算法在G-mean上提高了12.1%，两种算法可以作为涡轴发动机故障检测候选算法。

关键词: 涡轴发动机, 故障检测, 一类支持向量机, 局部密度, 马氏距离