Image evolution analysis method based on deep learning

Abstract

The invention belongs to the field of image processing, and particularly relates to an evolution analysis method of an ash deposition state image of an air pre-heater rotor. The method comprises the following steps: after acquiring an infrared image of the air pre-heater rotor, transmitting the infrared image to an image processing module by an infrared thermal imager; carrying out image preprocessing after receiving the image, and converting the preprocessed infrared image into a gray scale curve image; enhancing the grayscale curve image to highlight a target area; establishing a gray-levelco-occurrence matrix according to the enhanced gray-level curve image; analyzing gray level co-occurrence matrix related statistics, and extracting texture feature parameters; establishing a deep belief network analysis model and carrying out training and testing; enabling the trained model to analyze the ash deposition state of the rotor of the air pre-heater according to the texture feature parameters; effectively detecting and monitoring the soot formation degree, and analyzing images to obtain the specific soot formation state. According to the invention, the blockage possibility of the air preheater can be predicted in advance, an operator is guided to optimally operate a soot blowing system, and therefore the working efficiency of the air preheater in the normal operation period is improved.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for analyzing the evolution of the dust accumulation state image of an air preheater rotor. Background technique [0002] At present, rotary air preheaters (air preheaters for short) are widely used in large power plant boilers. Air preheaters generally have clogging problems, and clogging of air preheaters will lead to a series of problems. When the inside of the rotary air preheater begins to accumulate dust to a certain extent, it is difficult for the steam soot blower to clean the dust deeply. When the channel is completely blocked by dust, it is difficult to deal with it online. In order to reduce the hazards of dust accumulation and corrosion, sootblowers are generally used for air preheater purging. However, due to the lack of effective detection and observation methods, the problem of blown damage is common. Therefore, it is very necessary to monitor...

AI Technical Summary

Problems solved by technology

Although great achievements have been made in recent years, the principle is still to detect the dust accumulation degree of air preheating by using the converted pressure difference of air preheating exchange, especially the measurement and sensing part is only numerical display or qualitative judgment, which cannot allow operators Intuitively see the specific state of dust accumulation on the rotor of the air preheater

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