A dust removal fan fault prediction system based on a time series prediction model

Abstract

The present application belongs to the technical field of industrial equipment operation state monitoring, and discloses a dust removal fan fault prediction system based on a time series prediction model, which comprises a multi-source sensing module, a data preprocessing module, an edge computing module, an early warning output module and a feedback optimization module. The multi-source sensing module collects mechanical, performance and environmental multi-dimensional parameters in a distributed manner. The data preprocessing module realizes data synchronization optimization through a DTW algorithm and a filter circuit. The edge computing module takes an LSTM-ARIMA hybrid architecture as the core and combines a gate weight adjustment circuit to adapt to working condition fluctuations. Precise positioning is realized through Grad-CAM visualization and a fault mode library. The early warning output module realizes hierarchical alarm and equipment linkage. The feedback optimization module ensures the long-term adaptability of the device. The system solves the problems of single monitoring dimension, high false alarm rate and response lag in the prior art, has small wind pressure difference prediction error, high filter bag damage fault detection rate and low false alarm rate, can greatly reduce filter material loss, and is suitable for high dust industrial scenes.

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