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.