改进LSTM-AE算法的电梯知识库故障征兆预测

doi:10.3778/j.issn.1002-8331.2111-0134

摘要/Abstract

摘要： 针对运维知识库系统中故障征兆预测问题，提出面向电梯设备的改进LSTM-AE算法。使用属性子集选择（ACDR）方法筛选特征向量组，剔除电梯运行参数中的冗余特征。同时，针对运行速度特征序列的非平稳性问题，使用变分模态分解（VMD）算法作降噪平稳化处理。在LSTM-AE模型中引入融合BILSTM的滑动窗口注意力机制，提高模型的时序特征提取能力，并通过softmax分类器融合各特征序列的重构误差实现电梯故障征兆预测。实验结果表明，相较经典LSTM-AE算法，提出的改进LSTM-AE算法正常样本判准率提高13%，异常样本误判率降低11%，能够对常见电梯故障进行准确预测，适于构建可靠的电梯运维知识库故障征兆预测模型。

关键词: 故障征兆预测, 特征冗余, 变分模态分解, 降噪平稳化, 时序特征提取

Abstract: To solve the problem of fault symptom prediction in the operation and maintenance knowledge base system, an improved LSTM-AE algorithm for elevator equipment is proposed. Firstly, in view of redundancy problem in the elevator operating parameter sequence, the attribute correlation density ranking（ACDR） method is used to filter the feature vector group. Moreover, aiming at the non-stationary problem of the running speed characteristic sequence, variational mode decomposition（VMD） algorithm is used for noise reduction and smoothing. Finally, a sliding window attention mechanism fused with BILSTM is introduced into the LSTM-AE model to improve the ability of time-series feature extraction, and softmax classifier is used to fuse the reconstruction error of each feature sequence to realize prediction of the elevator fault symptom. The experimental results show that compared with traditional LSTM-AE algorithm, the proposed improved LSTM-AE algorithm has a 13% increase in the normal sample accuracy rate and a 11% reduction in the abnormal sample error rate. It can predict common elevator faults more precisely, and be suitable for constructing a reliable fault symptom prediction model of elevator operation and maintenance knowledge base.

Key words: fault symptom prediction, feature redundancy, variational mode decomposition, noise reduction and smoothing, time-series feature extraction

孙庆港, 王呈. 改进LSTM-AE算法的电梯知识库故障征兆预测[J]. 计算机工程与应用, 2023, 59(7): 311-318.

SUN Qinggang, WANG Cheng. Prediction of Fault Symptoms in Elevator Knowledge Base Based on Improved LSTM-AE Algorithm[J]. Computer Engineering and Applications, 2023, 59(7): 311-318.

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