Multi-modal process fault detection method based on local neighbor standardization matrix

Abstract

The invention discloses a multi-modal process fault detection method based on a local neighbor standardization matrix, and relates to an industrial process fault detection method. The method enables historical data at a normal state to serve as a training set of modeling data, and carries out multi-mode process modeling and fault detection through employing a local neighbor standardization matrix method. The method comprises the steps: carrying out the preprocessing of unequal-length batch data through employing a local weighting algorithm, determining the maximum retainable length of the unequal-length batch data in the training set, and reconstructing lost data points of the unequal-length batch data through weighing and k-neighbor information; constructing a main local neighbor standardization matrix for an equal-length training set, carrying out modal clustering through employing a K-means algorithm, and eliminating off-cluster samples at all modals through employing a local off-cluster factor method. The method can prevent information loss from affecting the modal clustering effect of a multi-modal process, eliminates off-cluster points, and enables the fault diagnosis result of a multi-modal intermittent process to be more accurate through the construction of the main local neighbor standardization matrix.

Description

Technical field [0001] The invention relates to an industrial process fault detection method, in particular to a multi-modal process fault detection method based on a local nearest neighbor standardized matrix. Background technique [0002] In order to meet changes in market demand and raw materials, multi-modal processes for producing high-value and diverse products have become more common. Compared with the traditional batch process, the multi-modal batch process is more complicated, with severe nonlinearity, time-varying and multiple operating conditions, which makes the fault diagnosis of the multi-modal batch process more challenging. In recent years, many scholars have analyzed multi-modal industrial processes from different angles and have proposed a variety of fault diagnosis methods. These methods do not need to assume that the data obey a single distribution, and have a good monitoring effect on chemical processes with complex data distributions, opening up a new way f...

AI Technical Summary

Problems solved by technology

[0003] Due to the production characteristics of the batch process, the production cycle of different batches is different, and the problem of unequal length batches will inevitably occur

On the other hand, on-site process data inevitably contain different degrees of error, measurement noise and system noise, etc. These problems will bring certain pollution to the data, making the multi-modal production process data produce local outliers

This kind of outlier will cause the direction of the pivot to shift. Before the fault detection, if this kind of data is not analyzed and preprocessed, it will affect the accuracy of the final fault diagnosis.

How to ensure that the loss of information is avoided, there is currently no method for fault detection of multi-modal unequal-length intermittent processes

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