In order to detect faults of nonlinear systems, an approach based on improved Locally Linear Embedding (LLE) was proposed. Firstly, the raw data was projected to lower dimensional space by LLE. In this step, tangent space distance was introduced to LLE and certain enhancement had also been made to intrinsic dimension estimation to make the approach more efficient and robust. Secondly, the inner class distance of data was calculated as an index of fault detection. To demonstrate the effectiveness of the improved LLE method, it is applied to Tennessee Eastman (TE) process and compared with kernel principle component analysis (KPCA) method. By simulation analysis, the false negative rate of the proposed approach achieves 4.498% in average, which is much better than 77.53% of KPCA, certifying the effectiveness of the approach to nonlinear fault detection.