摘要
针对实际中滚动轴承正常和故障状态下的振动数据不平衡,且故障诊断准确率不高的问题,基于深度强化学习,提出一种改进深度Q网络(DQN)的滚动轴承故障诊断方法。该方法将振动信号进行短时傅里叶变换,构建时频图样本集;提出把K-means算法中样本到中心点的距离作为回报值的偏置,以不平衡比为基准,为训练集构建具有个性化的回报函数,同时引入残差网络(Resnet-18)实现特征的深层提取;智能体将新的回报函数和时频图作为输入,在每个时间步长执行诊断动作,判断并返回回报值;最终,智能体学会不平衡数据下的故障诊断策略。实验表明,所提改进的诊断模型相比本文对比的其他方法在不平衡下提高了5%~8%;同时不平衡且变负载情况下也表现突出,不平衡指标得分达到了0.982左右,具有较好的泛化性。
Under normal and fault states in practice,rolling bearing vibration data are imbalanced and the fault diagnosis accuracy is low.Based on the deep reinforcement learning,an improved deep Q network(DQN)fault diagnosis method for rolling bearing is proposed.The short time Fourier transform is performed on the vibration data to establish sample sets of time-frequency graph.The distance between the sample and the center point in the K-means algorithm is used as the bias of the return value.The imbalance ratio is utilized as the benchmark to formulate a personalized reward function for the training set.Meanwhile,the residual network(Resnet-18)is used to realize the deep extraction of features.In which,the agent takes the new reward function and time-frequency graph as input.The diagnosis action is executed at each time step.And the reward is judged and returned.Finally,the agent learns the fault diagnosis strategy under imbalanced data.Compared with other methods,experimental results show that the improved diagnostic model is improved by 5%to 8%under imbalanced conditions.At the same time,it also performs outstandingly under imbalanced and variable load conditions.The imbalanced index score can reach about 0.982,which shows better generalization.
作者
康守强
刘哲
王玉静
王庆岩
兰朝凤
Kang Shouqiang;Liu Zhe;Wang Yujing;Wang Qingyan;Lan Chaofeng(School of Electrical and Electronic Engineering,Harbin University of Science and Technology,Harbin 150080,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第3期201-212,共12页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51805120)
黑龙江省自然科学基金(LH2019E058)
黑龙江省普通高校基本科研业务专项资金(LGYC2018JC022)资助
作者简介
康守强,2011年于白俄罗斯国立大学获得博士学位,现为哈尔滨理工大学教授、博士生导师,主要研究方向为非平稳信号处理,机械故障诊断、状态评估与预测技术。E-mail:kangshouqiang@163.com;通信作者:王玉静,2016年于哈尔滨工业大学获得博士学位,现为哈尔滨理工大学副教授、硕士生导师,主要研究方向为非平稳信号处理,机械故障诊断、状态评估与预测技术。E-mail:mirrorwyj@163.com
