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Rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA)

A technology of nuclear principal component analysis and fault diagnosis, applied in resources, instruments, manufacturing computing systems, etc., can solve problems such as hidden dangers in the stable operation of precalciner kilns, system monitoring, and limited application of cement rotary kilns, and achieve reasonable and accurate steps Effective monitoring, effectiveness improvement

Inactive Publication Date: 2017-09-12
BEIJING INFORMATION SCI & TECH UNIV
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Problems solved by technology

Principal component analysis has been successfully applied in the application of chemical process, but there are not many applications in the field of cement rotary kiln
Especially when a minor fault occurs in the system, the method based on principal component analysis or nuclear principal component analysis cannot accurately and timely monitor the system, which brings certain hidden dangers to the stable operation of the precalciner kiln

Method used

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  • Rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA)
  • Rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA)
  • Rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA)

Examples

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Effect test

no. 1 example

[0070] Taking a large cement rotary kiln with a daily output of 2500t of clinker as an example to carry out simulation experiments, for the fault detection of the entire precalcining system, the temperature and pressure of each key position should be the focus of detection. Taking the fault of variable 1 kiln tail temperature as an example, fault 1 is a sudden rise in the temperature of the kiln tail, and the fault is set as a step fault in which the temperature of the system rises at the kiln tail from the 101st sampling point. First, the number of pivots is selected according to the criterion that the cumulative variance contribution rate is greater than 0.8.

[0071] The principal component analysis method and the weighted kernel principal component analysis method are used for fault detection respectively, and the SPE and T 2 Control limits and statistics. From image 3 It can be seen that in the process of using the PCA method for fault detection, the abscissa indicates...

no. 2 example

[0075] Taking the temperature drift at the kiln tail as the fault point 2, the variable 1 fault is set as the fault that the temperature changes slowly at the kiln tail at the time k=101, and the fault diagnosis test is carried out by using this method, from Figure 6 It can be seen that the principal component analysis can detect the temperature drift fault, although it can detect that the two statistics exceed the control limit, but the fault is detected at k=115, and the alarm cannot be issued in time.

[0076] but from Figure 7 It can be seen that the detection method after weighting is at 8 moments after the temperature drift, that is, at the moment k=108, the detection statistics completely exceed the control limit, and the fault is detected. Compared with the single PCA method, it is more timely and accurate. The generation of failure.

[0077] The rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA) provided by the present ...

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Abstract

The invention discloses a rotary kiln fault diagnosis method based on weighted kernel principal component analysis (WKPCA). The rotary kiln fault diagnosis method comprises the following steps: collecting and pretreating training sample data in a normal condition, and mapping treated data to a higher dimensional space to obtain a kernel matrix; calculating the characteristic value of the kernel matrix and a characteristic matrix, and establishing a kernel principal component model to obtain kernel principal component variables in the normal condition; using a kernel density estimation function to calculate the density distribution function of each kernel principal component variable in the normal condition; calculating SPE control limit and T2 control limit according to the kernel principal component variables; collecting and pretreating detection sample data in a fault condition in real time; taking each kernel principal component variable of a detection sample into corresponding density distribution functions to obtain the weighed value of each kernel principal component variable, and establishing a weighted matrix; calculating SPE statistical magnitude and T2 statistical magnitude according to the weighed kernel principal component variables, comparing the calculated SPE statistical magnitude and T2 statistical magnitude with the obtained control limits, and judging whether the system breaks down. According to the diagnosis method provided by the invention, the steps of the diagnosis method are reasonable, and the accuracy of fault diagnosis is improved.

Description

technical field [0001] The invention relates to the technical field of process control, in particular to a fault diagnosis method for a rotary kiln based on weighted kernel principal component analysis. Background technique [0002] As one of the basic raw materials for national economic construction, cement is widely used in civil, industrial, water conservancy and transportation projects. The cement industry has become an important symbol of the national economic and social development level and comprehensive strength. The new dry process cement production technology is a comprehensive technology for cement production with suspension preheater and pre-decomposition technology as the core, using modern scientific theory and technology, and computer and networked information technology. It has high quality, high efficiency, energy saving, environmental protection and characteristics of sustainable development. [0003] The precalciner kiln is composed of four subsystems: pr...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06Q10/06G06Q50/04
CPCG06F30/20G06Q10/0639G06Q50/04Y02P90/30
Inventor 艾红张仰森范荫鹏
Owner BEIJING INFORMATION SCI & TECH UNIV
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