Surge monitoring method based on incremental nonlinear manifold learning

Abstract

The invention relates to a surge monitoring method based on incremental nonlinear manifold learning, high-dimensional characteristic information is constructed according to multipath dynamic characteristics expressed in the operating process of a compressor, a one-dimensional main manifold is extracted by utilizing a local tangent space algorithm, the one-dimensional main manifold is updated in real time by an incremental manifold learning method, and whether a surge occurs or not under the varying duty condition of the compressor is judged by monitoring the change of a geometrical structure of a main manifold time sequence. The monitoring method extracts a concealed nonlinear change rule from complete high-dimensional characteristic data in the self operation of compressor equipment, thereby avoiding a phenomenon of missing report; meanwhile, the main manifold has the characteristic of enlarging diversity, and on the basis of ensuring real-time performance, an incremental algorithm simplifies the setting of a warning line and achieves low false alarm rate, and lays a foundation for effectively realizing surge early warning.

Description

technical field [0001] The invention belongs to the technical field of equipment state monitoring and diagnosis, and relates to a method for real-time monitoring of the operating state of a compressor, in particular to a surge monitoring method based on incremental nonlinear manifold learning. Background technique [0002] Compressors widely used in the industrial field, during the working process, due to the change of air flow and other reasons, the compressor will surge, causing the compressor to not work normally, and even causing damage to the parts. Therefore, in order to prevent the surge of the compressor phenomenon, it is necessary to monitor the mechanical operation status of the compressor, and the surge process of the compressor is a dynamic continuous development and change process. At present, the on-line monitoring technology of compressor mechanical operation status with signal processing technology as the core is used to monitor the operation status of the co...

AI Technical Summary

Problems solved by technology

At present, the on-line monitoring technology of compressor mechanical operation status with signal processing technology as the core is used to monitor the operation status of the compressor in real time, but this online monitoring technology has the following problems: First, from the form of expression, once the surge , the interior of the compressor system pipe network will suddenly produce severe periodic axial low-frequency and large-scale airflow oscillations. The existing signal processing technology is limited by the real-time performance of the algorithm, and it is difficult to capture the precursory characteristics of surge in time; secondly, the development of surge The process is a dynamic and nonlinear process, and its evolution law is directly reflected in the changes of aerothermal parameters such as the temperature, flow rate and pressure of the compressor gas. The existing monitoring technology cannot comprehensively consider the working condition information and the changes of multiple signal sources. Accurately extract the surge characteristics that occur when the compressor is running

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