Driven by the market demand, industrial processes need to switch between multiple modes, resulting in multimodal and complex distributed data. Therefore, the study of multimodal fault detection technology is of great significance to ensure safe operation of industrial processes. Aiming at the fact, a fault detection algorithm based on local neighborhood standardization(LNS) and directional entropy weighted kernel entropy component analysis (DEWKECA) is proposed. Firstly, multimodal data are normalized with LNS, which can eliminate multimodal characteristics efficiently when compared with global standardization. Considering the differences in changing trends between fault samples and normal samples, the information entropy of samples' changing direction is defined as the directional entropy, which is used to describe the disorder degree of samples' changing direction. So DEWKECA is used to realize dimensionality reduction, with which the characteristics of data's changing direction can be extracted effectively. Given that multimodal data often obey non-Gaussian distribution, the local outlier factor(LOF) algorithm is adopted to establish monitoring statistics, whose control limit can be determined by kernel density estimation. Numerical example and the TE process simulation verify the effectiveness of the proposed algorithm.