关键词: 多元状态评估, 大数据技术, 故障诊断, 贡献率分析, 制粉系统

Abstract: In order to avoid the increase of maintenance cost in power plants directly or indirectly resulting from the abnormal conditions and shutdown of equipment during operation, a state assessment and early warning application for power plant equipment based on big data technology is proposed. Multivariate state assessment is one of the feasible technologies to realize equipment Prognostic and Health Management(PHM), and its implementation relies on training and learning of massive health data. Offline training of historical status data is made to establish health state assessment model based on big data technology. Making real-time analysis on the changes of related parameter residual values of the targeted equipment and taking automatic detection by sliding window residual statistics method can realize online monitoring on abnormal status of targeted equipment. Taking the state assessment and health diagnosis of the pulverizing system in a thermal power plant as an example,the parameter contribution rate is introduced to characterize the strength and weakness of factors leading to the anomalies, which is helpful in making further analysis on equipment status and fault. The experimental results show that this method can effectively evaluate the state and make fault warning for power plant equipment.

Key words: multivariate state estimation, big data technology, fault diagnosis, contribution rate analysis, pulverizer system