Multi-working-condition process monitoring method with continuous learning capability and improved PCA

Abstract

The invention discloses a multi-working-condition process monitoring method with continuous learning capability and improved PCA, and relates to the field of industrial monitoring and fault diagnosis.The method comprises the following steps: sequentially collecting process data of an industrial system under normal working conditions to form a training set; training the initial working condition by utilizing principal component analysis, and calculating an initial projection matrix; constructing an optimization function according to an elastic weight consolidation method and a principal component analysis principle, and training subsequent working conditions to obtain an optimal projection matrix; constructing monitoring statistics and calculating a threshold value; collecting process dataunder the real-time working condition of the system as a test sample, calculating statistics of the sample by utilizing the current training model, comparing the statistics with a threshold value, and judging whether a fault occurs or not. The weight matrix is determined by combining the system principle and priori knowledge, the interpretability of the method is improved, the algorithm is simple, the calculated amount is small, implementation is easy, and the method can be widely applied to the fields of chemical engineering, processing and manufacturing, large thermal power plants and the like.

Description

technical field [0001] The invention relates to the field of industrial monitoring and fault diagnosis, in particular to a multi-working-condition process monitoring method with continuous learning ability and improved PCA. Background technique [0002] In industrial systems, due to factors such as product quality, economic costs, environmental protection requirements, and raw materials, the working conditions of the operating process will change, so that the system is often in a process of multiple working conditions. It is of great significance to study the process monitoring of multi-working conditions to improve the safety and reliability of the system. For example, in large thermal power generating units, factories often use replacement coal. The combustion characteristics of different coals vary greatly, so the requirements for the fineness of coal powder and the temperature of air-powder mixture vary greatly. This leads to the fact that the pulverizing system is oft...

AI Technical Summary

Problems solved by technology

It can be seen that the traditional multi-condition process monitoring method based on the PCA method has poor real-time performance, and when a new working condition appears, the previously learned working condition knowledge is often forgotten.

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