针对传统的时频域故障诊断方法无法对故障实现自适应识别和分类,且准确率较低的问题,提出一种基于改进信息熵(improved information entropy,IIE)的长短时记忆网络(long-short time memory network,LSTM)方法.首先对原始信号分别进行集合经验模态分解(ensemble empirical mode decomposition,EEMD)和变分模态分解(variational mode decomposition,VMD);将包含故障信息的所有本征模式分量(intrinsic mode functions,IMF)进行信息熵的求取;通过信息熵反映IMF的信息量和峭度指标对描述冲击成分的优势改进信息熵,构成特征向量;最后结合LSTM处理非线性数据的优势,利用组合特征训练LSTM网络建立诊断模型.实验结果表明:该方法能准确、高效地识别多种故障,准确率要比单一的EEMD-LSTM、VMD-LSTM、人工神经网络等传统方法更高.
Bearing Fault Diagnosis Based on Improved Information Entropy and LSTM Network
Aiming at the traditional time-frequency domain fault diagnosis methods that cannot realize adaptive identification and classification of faults with low accuracy,a long-short time memory network(LSTM)method based on improved information entropy(IIE)was proposed.Firstly,the original signal was subjected to ensemble empirical mode decomposition(EEMD)and variational mode decomposition(VMD).All the intrinsic mode functions(IMF)containing fault information were subjected to information entropy.The information entropy was used to reflect the information amount and kurtosis index of IMF to describe the advantages of impact components,and the information entropy was improved to form the feature vector.Finally,combining with the advantages of LSTM in dealing with nonlinear data,the combination of features were used to train the LSTM network to establish a diagnostic model.The experimental results show that the method can identify multiple faults accurately and efficiently,and the accuracy rate is higher than the single EEMD-LSTM,VMD-LSTM,artificial neural network and other traditional methods.
bearingfault diagnosislong short term memory network(LSTM)improved information entropy(IIE)
万方数据
维普
