变工况下采煤机故障诊断的迁移学习方法

Transfer Learning Method for Shearer Fault Diagnosis under Variable Working Conditions

针对采煤机故障诊断过程中有效故障样本不足问题,提出变工况下采煤机故障诊断的迁移学习方法。对原始振动信号作小波变换得到时频图,利用图像增强原理凸显故障的时频特征,并对图像进行归一化后组成故障样本;利用大量不同工况下的现有故障数据组成源域数据,对卷积神经网络进行训练,以初步获取故障诊断模型;将训练后的模型迁移至采煤机故障诊断实验台,以最大均值差异(MMD)作为优化指标,利用实验台中的样本继续训练模型,实现权值微调。结果表明:振动信号经小波变换和图像增强处理后,能有效凸显故障特征;利用实验台小样本微调权值,能有效实现采煤机故障诊断的模型迁移。

Aiming at the problem of insufficient effective fault samples in shearer fault diagnosis process,a transfer learning method for shearer fault diagnosis under variable working conditions was proposed.The original vibration signals were transformed by wavelet to obtain the time-frequency graph,the time-frequency characteristics of the fault were highlighted by the principle of image enhancement,and the fault samples were formed after the image normalization.A large number of existing fault data under different working conditions were used to form source domain data,and the convolutional neural network was trained to obtain the initial fault diagnosis model.The trained model was transferred to the shearer fault diagnosis test bed,maximum mean discrepancy(MMD)was used as the optimization index,and the samples in the test bed were used to train the model and realize weight fine tuning.The results show that the vibration signals processed by wavelet transform and image enhancement can effectively highlight the fault characteristics,the model transfer of shearer fault diagnosis can be effectively realized by weight fine tuning of small samples on the test bed.