The invention relates to a
coal mill fault early warning method fusing
quantum weighting GRU and multi-
modal classification, and the method comprises the steps: firstly, proposing a data fusion architecture based on multi-equipment cooperative
perception, integrating
coal mill body parameters, associated equipment operation indexes and environment
monitoring data, constructing a
dynamic coupling feature
library, and strengthening the representation capability of complex working conditions of a
production area; secondly, a
quantum weighted GRU neural network is designed, a
quantum information processing module is embedded in a gating mechanism, the extraction precision of nonlinear
time sequence features is remarkably improved, and
signal noise in a high-dust environment is suppressed in combination with an adaptive filtering technology; an environmental parameter dynamic correction mechanism is further introduced, a residual threshold is adaptively adjusted according to real-time temperature and
humidity and dust concentration, and the adaptability of the model to extreme working conditions is enhanced; and finally, accurate discrimination of
multiple fault types such as
coal interruption, coal blockage,
spontaneous combustion and the like is realized through a
hybrid architecture of a multi-classification model, and high-robustness and high-precision fault early warning support is provided for a thermal power
generating unit production area.