工业设备预测性维护平台在离散制造场景中的应用Application of an Industrial Equipment Predictive Maintenance Platform in Discrete Manufacturing
应用场景Application Scenario
脱敏项目编号 AIET-EAC-2026-02。案例面向多工序离散制造车间，设备类型多、运行节奏不一致，传统维护主要依赖定期巡检和故障后维修。项目聚焦关键设备运行状态采集、异常趋势识别和维护计划协同。Desensitized project code AIET-EAC-2026-02. The case targets a multi-process discrete manufacturing workshop with diverse equipment types and inconsistent operating rhythms, where traditional maintenance relied mainly on scheduled inspection and post-failure repair. The project focused on operating-status collection, abnormal trend recognition, and coordinated maintenance planning for key equipment.
成果摘要Outcome Summary
平台整合振动、温度、电流等运行数据，建立设备状态看板、异常预警规则和维修工单流转机制。脱敏证据材料覆盖数据接入清单、预警规则说明、试运行记录、维护闭环记录和现场负责人确认说明，能够呈现从数据采集到维护决策的完整链条。The platform integrated operating data such as vibration, temperature, and current, and established equipment status dashboards, warning rules, and maintenance work-order workflows. The desensitized evidence materials cover data access lists, warning-rule descriptions, pilot operation records, maintenance closure records, and confirmation notes from the site lead, presenting a complete chain from data collection to maintenance decision-making.
应用价值Application Value
该案例帮助应用单位将维护模式从被动响应逐步转向主动预警，降低突发停机对生产排程的影响，并为设备管理制度、备件管理和维修人员协作提供了可执行的数字化依据。The case helped the application unit gradually shift its maintenance model from passive response to proactive warning, reduce the impact of unexpected downtime on production scheduling, and provide actionable digital evidence for equipment management procedures, spare-parts management, and maintenance team collaboration.
专家评价说明Expert Evaluation
评议小组认为，该案例能够体现工业互联网技术在设备健康管理中的落地价值，证据材料与应用流程相互对应，适合作为预测性维护方向的脱敏展示案例。The review panel found that the case demonstrates the practical value of industrial IoT technology in equipment health management. The evidence materials correspond well with the application process, making it suitable as a desensitized showcase case for predictive maintenance.