Industrial process fault detection method based on data neighborhood feature preservation

An industrial process and fault detection technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve the loss of useful information, incomplete potential information, and the inability to better and fully describe the state of normal process data, etc. problem, to achieve the effect of reducing the possibility, reducing the risk of data feature loss, and improving the effect

Active Publication Date: 2017-02-22
NINGBO UNIV
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Problems solved by technology

If only the distance between data points is simply considered, the potential information mined is not comprehensive, and

Method used

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  • Industrial process fault detection method based on data neighborhood feature preservation
  • Industrial process fault detection method based on data neighborhood feature preservation
  • Industrial process fault detection method based on data neighborhood feature preservation

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Embodiment approach

[0031] Step 3: According to the neighborhood set, construct the corresponding neighbor feature matrix W∈R n×n , and calculate the matrix L=D-W, where the matrix D∈R n×n It is a diagonal matrix, and the elements on the diagonal are the sum of the elements of each column in the matrix W. The specific implementation is as follows:

[0032] First, initialize the matrix W 0 is an n×n-dimensional identity matrix.

[0033] Second, according to the i-th sample The corresponding neighborhood set will matrix W 0 The corresponding element in row i in is updated to 1, and this operation is repeated until W is updated 0 The elements of all n rows in the new matrix W 1 .

[0034] Then, according to the i-th sample The corresponding neighborhood set will be the matrix W obtained in the previous step 1The corresponding element in column i in is updated to 1, and this operation is repeated until W is updated 1 The elements of all n columns in , get the neighbor feature matrix W.

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Abstract

The invention provides an industrial process fault detection method based on data neighborhood feature preservation and aims at solving the main technical problem of comprehensively considering the distance, time and angle neighborhood features of process data in a process of establishing a fault detection model. The method comprises the following steps: defining a corresponding neighborhood set for each sampling data point, wherein the neighborhood set comprises data samples similar to the data point in distance, time and angle; then, constructing a feature value problem to solve a projection transformation vector, and establishing a corresponding fault detection model on the basis; and finally, implementing online fault detection by use of the model. Compared with the traditional method, the fault detection model established by the method minimizes the risk of information loss, and a more reliable and accurate result can be obtained.

Description

technical field [0001] The invention relates to an industrial process fault detection method, in particular to an industrial process fault detection method based on data neighborhood feature preservation. Background technique [0002] The increasingly fierce market competition has put forward higher and higher requirements for the safety of the production process and the stability of product quality. Reliable and effective fault detection methods occupy a pivotal position in the entire production process control system. In recent years, research on fault detection methods, especially data-driven fault detection methods, has become a research hotspot in this field. Its core idea is to extract potentially useful information from normal process data that can reflect its operating status. However, considering the gradual development of modern industrial processes, the characteristics of the collected industrial data are often very complex. How to more effectively dig out usefu...

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0218
Inventor 童楚东蓝艇史旭华
Owner NINGBO UNIV
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