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Anomaly detection method based on hybrid hidden naive Bayesian model

A Bayesian model and anomaly detection technology, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as information loss and achieve good results

Active Publication Date: 2020-07-14
SHANDONG UNIV OF SCI & TECH
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Problems solved by technology

Obviously, binary variables also contain certain industrial process information, and being directly deleted in the data preprocessing stage will inevitably lead to the loss of part of the information

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  • Anomaly detection method based on hybrid hidden naive Bayesian model
  • Anomaly detection method based on hybrid hidden naive Bayesian model
  • Anomaly detection method based on hybrid hidden naive Bayesian model

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Embodiment Construction

[0065] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0066] An anomaly detection method based on a hybrid hidden naive Bayesian model, the process is as follows figure 2 shown, including the following steps:

[0067] Step 1: variable selection, which specifically includes the following steps:

[0068] Step 1.1: For n times sampled historical data set Where i represents the sampling time, X is the historical data, y is the corresponding label, and x i is the value of X at the i-th moment, y i is the value of y at the i-th moment, y i ∈{1,2,…,K}, K is the total number of categories of X, x i Contains p-dimensional features, Indicates the dimension, divides x into x according to the characteristics of continuous variables and binary variables c and x b ;x is x i The actual value of the continuous variable set x c contains p 1 features, binary variable set x b contains p 2 features;

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Abstract

The invention discloses an anomaly detection method based on a hybrid hidden naive Bayesian model, and belongs to the field of fault diagnosis. According to the method, through selection of continuousvariables and binary variables, correlations between the continuous variables, between the binary variables and between the binary variables and between the binary variables are considered, and a hybrid hidden naive Bayesian model containing information of the continuous variables and the binary variables at the same time is constructed. Compared with a traditional method, due to the fact that the information of the binary variable is added, the method has higher performance on detection of anomalies in the process industry, the fault false alarm rate can be remarkably reduced, and the faultdetection rate is effectively increased.

Description

technical field [0001] The invention belongs to the field of fault diagnosis, and in particular relates to an abnormality detection method based on a mixed hidden naive Bayesian model. Background technique [0002] With the advent of the era of big data, the exponential growth of a large amount of industrial data has brought new great challenges to fault detection. One of the challenges is how to efficiently utilize different types of data for fault detection. In the industrial process, there are a large number of variables representing states or value ranges, which are usually stored in the form of two values, 0 and 1. We call this type of variables binary variables (or switch variables). Traditional fault detection methods are basically based on continuous variables, while binary variables are removed in the data preprocessing stage. Obviously, binary variables also contain certain industrial process information, and being directly deleted in the data preprocessing stage...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/29
Inventor 周东华陈茂银王敏徐晓滨纪洪泉高明
Owner SHANDONG UNIV OF SCI & TECH
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