【摘要】 针对传感器的故障诊断与故障数据重构问题,提出一种基于改进型长短期记忆网络(LSTM)和随机森林(RF)的混合算法。首先,运用改进型LSTM算法对传感器的输出序列进行预测,将预测值与实际值作差得到残差序列。然后,通过RF算法对残差序列进行分类,识别出传感器的故障状态。当传感器诊断的结果为故障工作状态时,利用改进型LSTM的预测值重构故障数据。所提的改进LSTM-RF算法在功能上既可以对传感器故障类型进行诊断,又可以对故障数据进行重构。实验结果表明,改进的LSTM-RF算法的传感器故障识别准确率在不同的数据集上均能大于97%,故障数据重构的均方根误差小于4%;相比标准的LSTM-RF算法,改进的LSTM-RF算法在收敛速度提高的同时故障数据重构的精度提高了0.4%。更多还原

【Abstract】 Aiming at the problem of sensor fault diagnosis and fault data reconstruction, a hybrid algorithm model based on improved Long Short-Term Memory(LSTM) and Random Forest(RF) is proposed. Firstly, the improved LSTM is used to predict the output sequence of the sensor, and the residual sequence is obtained by the difference between the predicted value and the actual value. Secondly, the residual sequence is classified by the RF algorithm to identify the fault state of the sensor. When the sensor is in fault state after diagnosis, the fault data is reconstructed by using the prediction value of the improved LSTM. The improved LSTM-RF algorithm cannot only diagnose the sensor fault, but also reconstruct the fault data. The experimental results show that the accuracy of the proposed algorithm is more than 97% on different data sets, and the RMSE of fault data reconstruction is less than 4%. Compared with the standard LSTM-RF, the improved LSTM-RF algorithm improves the convergence speed and the accuracy of fault data reconstruction by 0.4%.更多还原