数据驱动的设备风险动态预警与精准管控机制的创新

Innovation in Data-Driven Device Risk Dynamic Early Warning and Precise Control Mechanisms

摘要: 针对高温、高压、连续生产工况下化工旋转设备风险感知滞后、运维决策缺乏数据支撑、静态阈值难以捕捉早期微弱劣化的问题，文章提出了一种数据驱动的设备风险动态预警与精准管控机制，并以机理–数据混合驱动的故障诊断作为技术核心。该机制以振动在线监测为核心、融合多源运行数据，构建“感知–诊断–预警–管控–反馈”的闭环：在特征提取环节提出“机理频带引导 + 包络解调”的优化策略，以故障特征频率为先验进行自适应频带聚焦，抑制强背景噪声与工艺扰动；在诊断环节构建机理模型与机器学习并行、由证据加权融合的双通道模型，兼顾早期微弱故障的检出率与结论的可解释性；在此基础上完成风险动态分级预警，并将预警与故障原因、剩余寿命及分级维修建议绑定，实现由被动维修向主动预控的精准管控转变。以某大型化工企业572台关键设备、1463个测点的实测数据验证表明：故障诊断准确率由基线的76.2%提升至93.8%，早期预警平均提前量由不足24小时提升至约168小时，非计划停机率下降62.5%，年度维修成本下降28.4%。研究可为流程工业设备的预测性维护提供可复用的方法与量化依据。

Abstract: To address the issues of delayed risk perception, lack of data-driven support for operation and maintenance decisions, and the inability of static thresholds to capture early-stage weak degradation in chemical rotating equipment under high-temperature, high-pressure, and continuous production conditions, this paper proposes a data-driven mechanism for dynamic risk early warning and precise management and control, with a mechanism-data hybrid-driven fault diagnosis as its technical core. Centered on online vibration monitoring and integrating multi-source operational data, the mechanism establishes a closed-loop framework of “perception-diagnosis-early warning-control-feedback”. In the feature extraction stage, an optimized strategy of “mechanism-informed frequency band guidance + envelope demodulation” is proposed, which utilizes fault characteristic frequencies as prior knowledge to achieve adaptive frequency band focusing, thereby suppressing strong background noise and process disturbances. In the diagnosis stage, a dual-channel model is constructed, in which a mechanism-based model and a machine learning model operate in parallel and are integrated through evidence-weighted fusion, thereby reconciling the detection rate of early-stage weak faults with the interpretability of diagnostic conclusions. On this basis, dynamic risk grading and early warning are accomplished, with warning information linked to fault causes, remaining useful life prediction, and graded maintenance recommendations, thus enabling the transition from passive maintenance to active pre-control and precise management. Validation using measured data from 572 key equipment units and 1,463 measurement points at a large chemical enterprise demonstrates that the fault diagnosis accuracy improved from a baseline of 76.2% to 93.8%, the average advance time of early warning increased from less than 24 hours to approximately 168 hours, the unplanned downtime rate was reduced by 62.5%, and the annual maintenance cost decreased by 28.4%. This research provides a replicable methodology and quantitative basis for the predictive maintenance of process industry equipment.