Air compressor monitoring diagnosis system and method adopting adaptive kernel Gaussian hybrid model

Abstract

The invention discloses an air compressor monitoring diagnosis system and method adopting an adaptive kernel Gaussian hybrid model, and relates to the field of air compressor control technologies. The system comprises a site equipment layer, an equipment control layer and a management and monitoring layer. The site equipment layer is composed of PLCs200, sensors, air compressors, actuators and a water pump, and with the PLCs200 as slave stations, control over the site equipment layer is completed. The equipment control layer comprises an upper computer and a PLC300, with the PLC300 as a master station, the whole air compressor system is controlled through a variable-structure adaptive PID controller based on a support vector machine, and the upper computer monitors the air compressor system. The equipment control layer is in communication with the management and monitoring layer through the industrial Ethernet, and then remote monitoring and data transmission of the upper computer are achieved. The Gaussian hybrid model and the kernel principal component analysis method are integrated in the fault diagnosis method adopted in the upper computer, optimal kernel function parameters are solved through the iterative optimization method, and the purpose of distinguishing different mode data is achieved. The air compressor monitoring diagnosis system and method have higher diagnosis precision and higher practical value.

Description

technical field [0001] The invention relates to the technical field of air compressor control, in particular to an air compressor monitoring and diagnosis system and method of an adaptive kernel Gaussian mixture model. Background technique [0002] Air compressors are important large-scale equipment in coal mines and are also high-energy-consuming equipment. With the application of new technology, power equipment and new technologies in coal mines, not only more air volume is required, but also the air compressor is required to be adaptively adjusted as the load changes. However, the existing old-fashioned air compressors have problems such as poor control quality, low reliability, and difficulty in monitoring the working status of the system in real time, resulting in huge energy waste and a huge burden on operators. Traditional control systems use local decentralized manual operations. The air compressor makes a lot of noise when working, and working in this environment ...

AI Technical Summary

Problems solved by technology

However, the PCA method cannot detect the nonlinear structure of the input data

Aiming at the shortcomings of the traditional air compressor monitoring method, such as low

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