基于LSTM的交流接触器剩余寿命预测

Residual Life Prediction of AC Contactor Based on Long Short-term Memory

交流接触器在各种低压控制线路中应用极为频繁,因此对其进行剩余寿命预测可以大幅提高电力控制系统的运行稳定性。针对目前交流接触器剩余寿命预测没有充分利用其退化过程前后状态之间联系的问题,提出了一种基于长短期记忆神经网络(long short-term memory,LSTM)的交流接触器剩余寿命预测方法。首先,通过交流接触器全寿命试验平台获取其整个生命周期的退化数据,从中提取出能够反映其运行状态的特征参数;其次,采用灰色关联分析(grey relation analysis,GRA)法和皮尔逊相关系数(Pearson correlation coefficient,PCC)法剔除多维参量的冗余信息,进行特征选择,并将其结果作为预测模型的输入样本;最后进行LSTM预测模型训练。试验结果表明,相比传统循环神经网络(recurrent neural network,RNN),基于LSTM的剩余寿命预测模型能够充分利用全寿命周期时序序列数据的前后关联信息,对交流接触器剩余寿命预测有更高的准确性。

AC contactor is widely used in various low-voltage control lines,thus the residual life prediction can greatly improve the operation stability of power control system.In order to solve the problem that the residual life of AC contactor is determined by the current state and the previous state,and the relationship between the state before and after the degradation process cannot be effectively used,a residual life prediction method of AC contactor based on long-short term memory neural network(LSTM)is proposed.Firstly,the degradation data of AC contactor in its whole life cycle are obtained through a life cycle test platform,and the characteristic parameters which can reflect its operation state are extracted.Secondly,gray correlation analysis(GRA)and Pearson correlation coefficient(PCC)are used to eliminate the redundant information of multi-dimensional parameters,and feature selection is used as the input sample of the prediction model.Finally,the LSTM prediction model is trained.The experimental results show that,compared with the traditional recurrent neural network(RNN),the residual life prediction model based on the improved LSTM can make full use of the correlation information of the whole life cycle time series data,and has higher accuracy for the residual life prediction of AC contactor.