工矿自动化2024，Vol.50Issue(2)：138-146,9.DOI:10.13272/j.issn.1671-251x.2023110024
基于自适应多尺度注意力机制的CNN-GRU矿用电动机健康状态评估
Health status evaluation of CNN-GRU mine motor based on adaptive multi-scale attention mechanism
摘要
Abstract
When using multi-sensor information fusion technology to evaluate the health status of motors,there are outliers and missing values in the monitoring data of mine motors.However,deep learning models such as convolutional neural networks and recurrent neural networks find it difficult to effectively extract data features and update network weights when the data quality is severely degraded,resulting in problems such as vanishing or exploding gradients.In order to solve the above problems,A CNN-GRU(CNN-GRU-AMSA)model based on adaptive multi-scale attention mechanism is proposed to evaluate the health status of mine motors.Firstly,the model fills in,removes,and standardizes the motor operation data collected by sensors,and classifies the operating conditions of mine motors based on environmental temperature changes.Secondly,based on the Mahalanobis distance,the health index(HI)of health evaluation indicators such as motor current,three-phase temperature of motor winding,front bearing temperature of motor,and rear bearing temperature of motor are calculated.The Savitzky Golay filter is used to denoise,smooth,and normalize the HI indicator.Combining the contribution of different indicators calculated by principal component analysis method to mine motors,the weighted fusion of indicator HI is used to obtain the mine motor HI.Finally,the mine motor HI is input into the CNN-GRU-AMSA model,which dynamically adjusts attention weights to achieve information fusion of features at different scales,thereby accurately outputting the health status evaluation results of the motor.The experimental results show that compared with other common deep learning models such as CNN,CNN-GRU,CNN-LSTM,and CNN-LSTM Attention,the CNN-GRU-AMSA model performs better in evaluation metrics such as root mean square error,mean absolute error,accuracy,Macro F1,and MicroF1.The model has a smaller fluctuation range and better stability in predicting residuals.关键词
电动机健康状态评估/自适应多尺度注意力机制/CNN-GRU/多传感器信息融合/主成分分析Key words
health status evaluation of motors/adaptive multi-scale attention mechanism/CNN-GRU/multi sensor information fusion/principal component analysis分类
矿业与冶金引用本文复制引用
谭东贵,袁逸萍,樊盼盼..基于自适应多尺度注意力机制的CNN-GRU矿用电动机健康状态评估[J].工矿自动化,2024,50(2):138-146,9.基金项目
国家自然科学基金资助项目(72361032) （72361032）
新疆维吾尔自治区重点研发资助项目(2021B01003). （2021B01003）
