Health State Evaluation and Prediction of Switch Equipment Using SOM and LSTM
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摘要: 针对道岔转辙设备故障频发，且工作人员无法准确评估及预测其健康状态的问题，进行结合SOM-LSTM混合神经网络的道岔转辙设备健康状态评估及预测方法研究。首先，依据道岔动作功率曲线特点分三段提取其时域特征参数，利用自组织映射神经网络（Self organizing map，SOM）中最小量化误差求解道岔转辙设备健康因子（Health index，HI）；其次，运用长短时记忆神经网络（Long short term memory，LSTM）算法预测道岔转辙设备后续健康因子曲线；最后，利用现场采集数据，对算法的有效性进行验证。实验结果表明：SOM方法可有效追踪道岔转辙设备健康状态变化规律，实现对健康因子的快速准确计算；相较于误差反向传播神经网络（Back propagation，BP神经网络）和循环神经网络（Recurrent neural network，RNN神经网络），LSTM算法预测效果较好，准确度提升，对道岔转辙设备的健康管理具有一定的指导意义。Abstract: Aiming at the problem that the fault of switch equipment occurs frequently and the staff can not accurately evaluate and predict its health state, the health state evaluation and prediction method of switch equipment combined with SOM-LSTM hybrid neural network is studied. Firstly, according to the characteristics of switch action power curve, the time-domain characteristic parameters are extracted in three segments, and the Health Index (HI) of switch equipment is solved by using the minimum quantization error in Self Organizing Map (SOM); Then the Long Short-term Memory Networks (LSTM) is used to predict the subsequent Health Index curve of switch equipment; Finally, the effectiveness of the algorithm is verified by using the field data. The experimental results show that SOM algorithm can effectively track the change law of health state of switch equipment and realize the rapid and accurate calculation of Health Index; Compared with Back Propagation (BP) neural network and Recurrent Neural Network (RNN) neural network, LSTM algorithm has better prediction effect and improved accuracy, which has certain guiding significance for the health management of switch equipment.
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Key words:
- switch equipment /
- SOM /
- LSTM /
- health state /
- prediction
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图 1 结合SOM-LSTM混合神经网络道岔健康状态评估与预测流程
Figure 1. Assessment and prediction of switch equipment health status combined withSOM-LSTM hybrid neural network algorithm
表 1 8种时域特征计算公式表
Table 1. Calculation formulas for 8 types of domain features
项目 公式 标准差（STD） $\sqrt{\dfrac{1}{C}{\displaystyle {\sum }_{c=1}^{C}{\left(F(c)-\frac{1}{C}{\displaystyle {\sum }_{c=1}^{C}F(c)}\right)^2}} }$ 峰度（KT） $\dfrac{ {\dfrac{1}{C}\displaystyle\sum\nolimits_{i = 1}^C {F{ {(i)}^4} } } }{ { {\sigma ^4} } }$ 峰峰值（P2P） $\max (F) - {\text{min} }(F)$ 偏斜度（SK） $\dfrac{ {\dfrac{1}{C}\displaystyle\sum\nolimits_{i = 1}^C {F{ {(i)}^2} } } }{ { {\sigma ^2} } }$ 均方根（RMS） $\sqrt {\dfrac{1}{C}\displaystyle\sum\nolimits_{c = 1}^C {F(c)} {}^2}$ 波峰因子（CF） $\dfrac{ {{\rm{MAX}}(F)} }{ {{\rm{RMS}}} }$ 均值（Mean） $\dfrac{1}{C}\displaystyle\sum\nolimits_{c = 1}^C {F(c)}$ 方差（Var） $\dfrac{1}{C}\displaystyle\sum\nolimits_{c = 1}^C { { {\left(F(c) - \frac{1}{C}\displaystyle\sum\nolimits_{c = 1}^C {F(c)} \right)}^2} }$ 
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表 2 不同预测算法实验结果对比
Table 2. Comparison of experimental results of differentprediction algorithms
算法平均相对误差
MRE/%均方根误差
RMSE/%决定系数R2 LSTM 0.473 5.048 0.703 BP 1.071 5.822 0.614 RNN 0.583 5.631 0.587
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