CAJ | 학술논문

针对传统BP神经网络在滚动轴承故障诊断中存在收敛速度慢且易陷入局部极小等问题，提出一种GA和LM组合优化BP神经网络的故障诊断方法。利用小波包变换对不同故障类型的振动信号进行软阈值消噪处理，然后进行三层小波包分解及重构，并成功提取了8个频带构建的故障能量特征向量。利用GA优化了BP神经网络的隐含层层数及节点数、初始权值和阈值的网络参数，采用LM算法改进网络的搜索空间。以美国凯斯西储大学提供的滚动轴承实验数据进行诊断，结果表明，与GA优化的诊断结果相比，组合优化后的BP神经网络具有更高的诊断效率和精度。
침대전통BP신경망락재곤동축승고장진단중존재수렴속도만차역함입국부겁소등문제，제출일충GA화LM조합우화BP신경망락적고장진단방법。이용소파포변환대불동고장류형적진동신호진행연역치소조처리，연후진행삼층소파포분해급중구，병성공제취료8개빈대구건적고장능량특정향량。이용GA우화료BP신경망락적은함층층수급절점수、초시권치화역치적망락삼수，채용LM산법개진망락적수색공간。이미국개사서저대학제공적곤동축승실험수거진행진단，결과표명，여GA우화적진단결과상비，조합우화후적BP신경망락구유경고적진단효솔화정도。
Aim to the problem of slow convergence and easy to fall into local minimum for traditional BP neural network in rolling bearing fault diagnosis, a fault diagnosis method is proposed based on GA and LM combined-optimization BP neural network. Vibration signal of different failure types were used wavelet packet transform to carry out soft threshold de-noising, and then were taken advantage of three wavelet pack-et for decomposition and reconstruction, and were successfully extracted fault energy eigenvectors of eight bands. Use GA to optimize the hidden layers and the number of nodes, the network parameters of initial weights and thresholds for BP neural network, and use LM algorithm to improve the search space of the net-work. Rolling bearing experimental dates that were provided Case Western Reserve University were diag-nosed, the results show that, compared with the diagnostic results before GA optimization, the combined-optimization BP neural network has a higher diagnostic efficiency and accuracy.