关键词: 锅炉设备, 在线监测数据, 数据清洗, 深度学习, 堆栈降噪自编码器, 特征提取

Abstract: The data cleaning process is an important aspect of data preprocessing for online monitoring data of boiler equipment.Aiming at the problems of cumbersome data cleaning steps that easily lead to the destruction of continuous data,a data cleaning method of stacked denoising auto-encoder based on hybrid Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD) algorithm optimization was proposed.A hybrid algorithm of Adam and SGD was introduced to continuously adjust the network parameters of the stacked denoising auto-encoder model.The model was utilized to train the normal status data to obtain the hidden features,and the reconstruction error of the data was obtained.The model was used to detect the abnormal data,and its reconstruction error was applied to analyze the influence of various types of data on the model.Thus the “dirty data” and the abnormal data reflecting the equipment failure were classified and repaired quickly.The cleaning and repairing experiments of the boiler monitoring data of a power plant proved that the proposed method could accurately identify the “dirty data”,and the repaired data could also follow the distribution law of the data,meet the data cleaning requirements,laying a good foundation for subsequent data analysis and mining and equipment fault diagnosis.

Key words: boiler equipment, online monitoring, data cleaning, deep learning, stacked denoising auto-encoder, feature extraction