Nonlinear process fault detection method based on differential locality preserving projection (DLPP)

Abstract

The invention discloses a nonlinear process fault detection method based on differential locality preserving projection (DLPP) and relates to a nonlinear process fault detection method. Batch data acquired in a normal production process is taken as a training set of modeling data, three-dimensional data is expanded to be two-dimensional, a nearest neighbor of each sample in a data set is found, and differential operation is performed on the samples and their nearest neighbors; a locality preserving projection model is established, a projection matrix is found, and a control limit of a square prediction error (SPE) is calculated through kernel density estimation; for a batch sample of a new time point k, data is expanded to form a row of vectors; the data of the new time point is projected to a model of the DLPP, and an SPE statistical amount is calculated; according to whether the statistical amount exceeds the control limit of modeling, whether the data of the time point is normal is determined; if the statistical amount exceeds the control limit, there are faults in data samples of the time point; and otherwise, the data samples are normal. When a test indicates that the faults occur in a system, work personnel needs to examine conditions timely so as to eliminate dangers.

Description

technical field

[0001] The invention relates to a nonlinear process fault detection method, in particular to a nonlinear process fault detection method based on differential local preservation projection. Background technique

[0002] Nonlinear processes exist in real industrial processes and are widely used in the production of high-quality, high value-added products, such as: machinery, papermaking, metallurgy, food processing, etc., and are an important industrial production process. Therefore, fault detection in nonlinear processes has been receiving much attention. Nonlinear data structures often play an important role in fault detection. Therefore, this puts high demands on the performance of nonlinear process fault detection.

[0003] The difference algorithm can effectively eliminate the nonlinear structure of the data, and at the same time maintain the internal structure of the data, optimize the fault detection of the nonlinear process, so as to ensure production...

Examples

Embodiment Construction

[0015] The present invention will be described in detail below in conjunction with examples.

[0016] The present invention preprocesses (expands into two-dimensional and standardized) a large amount of normal historical data, uses a difference algorithm, eliminates the nonlinear structure of the data, and multiplies the distance between the two samples after projection by the corresponding weight. value is the smallest, find the projection matrix A , the control limits of the SPE statistic are calculated by kernel density estimation. The difference operation is performed on the new samples and projected to the low-dimensional space, and the SPE statistics are calculated for fault detection. The technology solves the problem that the traditional algorithm cannot eliminate the non-linearity well while maintaining the internal structure of the data when it is used for the fault diagnosis of the non-linear process. In order to better detect faults in nonlinear processes, it is ...

technical field

[0001] The invention relates to a nonlinear process fault detection method, in particular to a nonlinear process fault detection method based on differential local preservation projection. Background technique

[0002] Nonlinear processes exist in real industrial processes and are widely used in the production of high-quality, high value-added products, such as: machinery, papermaking, metallurgy, food processing, etc., and are an important industrial production process. Therefore, fault detection in nonlinear processes has been receiving much attention. Nonlinear data structures often play an important role in fault detection. Therefore, this puts high demands on the performance of nonlinear process fault detection.

[0003] The difference algorithm can effectively eliminate the nonlinear structure of the data, and at the same time maintain the internal structure of the data, optimize the fault detection of the nonlinear process, so as to ensure production...

Examples

Embodiment Construction

[0015] The present invention will be described in detail below in conjunction with examples.

[0016] The present invention preprocesses (expands into two-dimensional and standardized) a large amount of normal historical data, uses a difference algorithm, eliminates the nonlinear structure of the data, and multiplies the distance between the two samples after projection by the corresponding weight. value is the smallest, find the projection matrix A , the control limits of the SPE statistic are calculated by kernel density estimation. The difference operation is performed on the new samples and projected to the low-dimensional space, and the SPE statistics are calculated for fault detection. The technology solves the problem that the traditional algorithm cannot eliminate the non-linearity well while maintaining the internal structure of the data when it is used for the fault diagnosis of the non-linear process. In order to better detect faults in nonlinear processes, it is ...

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