Aiming at the complex data distribution characteristics of industrial processes, this paper proposes a probabilistic principal component analysis fault detection method based on local neighborhood standardization ( LNSPPCA ) to solve the problem of unsatisfactory fault detection effect caused by multi-modal characteristics and uncertainty of process data. Firstly, LNS is used to solve the data multi-modal problem, so that the standardized data obey a single Gaussian distribution as much as possible. Then, the PPCA method is used to analyze the data from the perspective of probability, which can take into account the randomness of the data, so as to describe the data more realistically, extract more comprehensive and valuable information, and effectively detect faults in the complex data distribution process. Therefore, the LNSPPCA method can effectively improve the industrial process fault detection capability in multi-modal process complex data distribution. Numerical examples and TE process were used to conduct application experiments, and the test results are compared with those of principal component analysis (PCA) and PPCA methods, which verifies the effectiveness of the LNSPPCA method.