CNN-based power equipment fault judgment and early warning method, terminal and readable storage medium

Abstract

The invention provides a CNN-based power equipment fault judgment and early warning method, a terminal and a readable storage medium. The method comprises the steps of obtaining test data; preprocessing the data; processing the data by using an offline model; and performing fault prediction of data. According to the method, the coal mill data is modeled through a deep learning method, fault prediction is achieved, mass historical data of coal mill equipment are fully mined through an existing data mining and machine learning modeling method, and an efficient and practical model is establishedto conduct detection and early warning on the real-time state of the coal mill. Knowledge and experience of experts and operating personnel are combined with data mining and machine learning methods and complemented with each other. The data can be automatically analyzed and modeled according to the data characteristics, and the threshold of operating personnel is lowered. The fault prediction model of the coal mill established by the invention can contain more complex causal relationships implicit among the indexes, so that the possibility of loss of a large amount of effective information isavoided, and the result is relatively reasonable and accurate.

Description

technical field [0001] The invention relates to the field of thermal power plant equipment failure analysis, in particular to a CNN-based power equipment failure judgment and early warning method, a terminal and a computer-readable storage medium. Background technique [0002] Coal mill is an important equipment in coal-fired units of thermal power plants in my country. The safety and stability of its operation are directly related to the overall working efficiency of the entire thermal power generation unit, and play a very important role in the normal operation of the entire power generation system. important role. Due to the complex operating conditions of thermal power plants, coal mills often have failures such as low loading pressure, insufficient output, powder leakage from the coal mill, unstable current of the coal mill, and impurities in the hydraulic oil, which seriously threatens Safe operation and economic efficiency of power plants. At present, most of the ope...

AI Technical Summary

Problems solved by technology

It is necessary to extract the characteristics of the data from a large amount of data for algorithm learning. If the dimension of the characteristics is very high, it is necessary to use algorithms to fit very complex functions. It is usually difficult for ordinary machine learning algorithms to do this well.

Moreover, the current traditional machine learning algorithm can only solve a specific problem, and cannot be extended or solve general problems. If the problem changes, the traditional machine learning algorithm will need to be adjusted or even rewritten.

Based on this situation, traditional machine learning algorithms lack flexibility and are difficult to adapt to changing needs at any time

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