Nonlinear industrial process fault detection method based on kernel principal component analysis
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A nuclear principal component analysis and industrial process technology, applied in electrical testing/monitoring, etc., can solve problems such as inability to guarantee nuclear components, obey Gaussian distribution, and failure to effectively reflect fault information
Active Publication Date: 2018-01-05
CHINA UNIV OF PETROLEUM (EAST CHINA)
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However, in practical applications, traditional KPCA still has deficiencies: on the one hand, the above T 2 The two statistics of SPE and SPE treat each core component equally in the calculation process, but in fact only some core components can significantly highlight the fault information, so treating each core component equally cannot reflect the fault information effectively; on the other hand, Traditional KPCA
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Embodiment 1
[0111] Embodiment 1: A nonlinear numerical system is taken as an example for illustration. Simulate a system with six monitored variables [x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ] nonlinear numerical system, its mathematical description is as follows:
[0112] x 1 = r 1 +e 1 (31)
[0113] x 2 = r 2 +e 2
[0114] x 3 = 2r 1 +3r 2 +e 3
[0115] x 4 =5r 1 -2r 2 +e 4
[0116] x 5 = r 1 2 -3r 2 +e 5
[0117] x 6 =-r 1 3 +3r 2 2 +e 6
[0118] In formula (31), e 1 ,e 2 ,e 3 ,e 4 ,e 5 ,e 6 ∈N(0,0.01) represents six independent Gaussian noise variables, r 1 ,r 2 ∈[0,2] is an independent uniformly distributed random variable, the output of the system [x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ] as a process monitoring variable. 400 samples are simulated under normal operating conditions shown in formula (31) as training data for modeling. In order to generate fault data, the variable x is changed at the 201st moment of the simulation 6 Add a sine wave with ...
Embodiment 2
[0201] Implementation example 2: The Tennessee-Eastman (TE) process is an experimental platform established by Downs and Vogel of Eastman Chemical Company in the United States based on an actual chemical process. It is now widely used to verify the optimality of control algorithms and process monitoring methods. inferior. The TE process is mainly composed of five units, including a reactor, a product condenser, a gas-liquid separator, a circulating compressor and a stripping tower. The structure diagram is as follows image 3 shown. The TE process has a total of 53 variables, including 22 continuous measurement variables, 19 component variables and 12 operational variables. This embodiment uses 33 variables in the TE process, as shown in Table 2. The 21 faults used in this embodiment are shown in Table 3, and are used to verify the monitoring performance of each method.
[0202] 33 variables of the TE process that the present embodiment of table 2 adopts
[0203] v...
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Abstract
The invention discloses a nonlinear industrial process fault detection method based on kernel principal component analysis. After conducting normalization processing on training data, a KPCA model isestablished, nonlinear features are extracted from the training data to serve as kernel components, and a normal threshold value is determined for each kernel component; a kernel principal component space (PCS) and a kernel residual error space (RCS) are divided out according to the number of the kernel principal components; a local outlier analysis algorithm is used for calculating correspondinglocal outlier value statistics LOF<PCS> and LOF<RCS>, and control limits are determined; test data is acquired, and a corresponding kernel principal component vector and a kernel residual error vectorare extracted by utilizing the KPCA model; weighting is carried out on the vectors by utilizing the normal threshold values of the kernel components, weighted local outlier value statistics WLOF<PCS>(h) and WLOF<RCS>(h) are calculated, and the control limits are used for monitoring. According to the method, a kernel component weighting technology and a local outlier factor technology are introduced into a KPCA method, nonlinear characteristic information in industrial process data can be measured accurately, and the fault detection rate is increased.
Description
technical field [0001] The invention belongs to the technical field of industrial process fault detection, and relates to a non-linear industrial process fault detection method, in particular to a non-linear industrial process fault detection method based on nuclear principal component analysis. Background technique [0002] Modern industrial processes tend to be larger and more complex, and fault detection and diagnosis technology has become an important technology to ensure the safety of industrial processes. With the wide application of modern computer measurement and control systems, a wealth of process operation data is collected and stored in industrial processes. Therefore, data-driven fault detection and diagnosis technology has gradually become a research hotspot. Typical data-driven fault detection and diagnosis methods include Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), Fisher Discriminant Analysis (FDA),...
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