基于非线性整数规划模型的发电厂设备预防性维护方法

Preventive maintenance method of power plant equipment based on nonlinear integer programming model

为了避免发电厂设备出现性能恶化,需对其进行预防性维护。文中提出了基于非线性整数规划模型的发电厂设备预防性维护方法,同时结合双向长短期记忆网络,用于对制造过程中时间序列进行异常检测和预防性维护,该方法能够有效地从时间序列数据中提取特征并进行检测异常。文中将联邦学习框架、非线性整数规划模型、一维卷积神经网络和双向长短期记忆网络结合起来,考虑时间序列数据的分布变化,并在此基础上进行预防性维护。利用发电厂设备的数据集来评估提出方法的性能。实验结果表明,该模型的测试准确率达到了97.2%,显示了其在设备预防性维护方面的潜力。

In order to avoid performance degradation of power plant equipment,preventive maintenance is necessary.This study proposes a preventive maintenance method for power plant equipment based on one-Dimensional Convolutional Neural Network(1DCNN)and bidirectional long short-term memory network,which is used for anomaly detection and preventive maintenance of time series in the manufacturing process.This method can effectively extract features from time series data and detect anomalies.This study combines the federated learning framework,one-dimensional convolutional neural network,and bidirectional long short-term memory network,considering the distribution changes of time series data,and conducting preventive maintenance on this basis.Evaluate the performance of the proposed method using a dataset of power plant equipment.The experimental results show that the testing accuracy of the model reaches 97.2%,demonstrating its potential in equipment preventive maintenance.