Batch process online fault detection method of dynamic multi-direction local outlier factor algorithm

Abstract

The invention provides a batch process online fault detection method of a dynamic multi-direction local outlier factor algorithm, relating to a batch process fault detection method. Firstly, three-dimensional data is expanded into two-dimensional in the sliding window of a training sample, and the standard processing is carried out. Then k neighbors of a training set (i) are found in each window, and a local outlier factor algorithm is used to calculate a reachability distance and a local reachability density to obtain an LOF statistical amount. The control limit of the LOF statistical amount at that time is calculated through nuclear density estimation. K neighbors of new time data are founded in the training set, and the LOF statistical amount at that time is calculated by using a local outlier factor algorithm. If the statistical amount exceeds a control limit, the data sample at that time is failed, otherwise, the data sample is normal. If a test indicates that a system is failed, the staff needs to identify a situation timely and eliminate danger. According to the method, the process monitoring can be carried out effectively, and a fault detection effect is improved.

Description

technical field [0001] The invention relates to a fault detection method for intermittent processes, in particular to an online fault detection method for intermittent processes using Dynamic Multiway Local Outlier Factor (DMLOF). Background technique [0002] In recent years, due to the continuous progress and development of science and technology, the batch process has been widely used in the production of high-quality, high value-added products. Therefore, the detection and fault diagnosis of batch processes have always attracted much attention. However, the signals collected in the intermittent process often have non-Gaussian, nonlinear, and multi-modal characteristics, which put forward high requirements on the performance of fault detection. Compared with the traditional batch process, the multi-mode batch process is more complicated, and has the characteristics of severe nonlinearity, time-varying and multi-working conditions, which makes the fault diagnosis of the m...

AI Technical Summary

Problems solved by technology

These methods assume that the data obeys a single distribution, and reduce the data from high-dimensional space to low-dimensional space by retaining the principal components, changing the data distribution and losing local information, while the actual data is often a mixture of Gaussian and non-Gaussian...

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