一种污水处理厂设备预测及辅助维护智能平台

An Intelligent Platform for Prediction and Auxiliary Maintenance of WWTP Equipments

污水处理工艺复杂,导致设备故障频发,维护人员采用事后维修的方式,不利于及早发现设备故障隐患,现提出一种数据驱动的预测性设备维护管理策略,设计了基于建筑信息模型(BIM)和设备维护管理(FMM)的预测性维护管理平台。通过采集设备属性数据和历史维护数据,利用支持向量机(SVM)算法生成预测模型,实现对污水处理设备健康状态的预测以及设备维护次序的规划,设备维护人员根据预测结果可提前生成维护预案以提早采取维护措施。最后,以东北某污水处理厂项目验证了预测维护管理平台的可行性和有效性,预测结果的误差率保持在5%以内。

The complexity of wastewater treatment process(WWTP)leads to frequent equipment failures.Post personnel maintenance is not conducive to the early detection of potential equipment failures.A data-driven predictive strategy for equipment maintenance management is proposed,and a predictive platform for maintenance management based on Building Information Modeling(BIM)and Facility Maintenance Management(FMM)is designed.By collecting the equipment attribute data and historical maintenance data,the Support Vector Machine(SVM)algorithm is used to generate the prediction model for predicting the health status of sewage treatment equipments and the planning of equipment maintenance order.The equipment maintenance personnel can prepare maintenance plan in advance according to the prediction results to take maintenance measures in advance.Finally,the feasibility and effectiveness of the prediction maintenance management platform are verified by a WWTP in northeast China,and the error rate of the prediction results is kept within 5%.