基于全生命周期设备管理的预测性维护技术研究

Research on Predictive Maintenance Technology Based on Full Lifecycle Equipment Management

  • 摘要: 随着大数据、云计算技术日趋成熟,设备设施预测性维护成为传统制造业数字化转型的核心切入点。事后维护是在设备发生故障之后再进行修理、维护,存在着可靠性低、维护成本高、生产损失大等缺点,对企业的稳定生产、成本控制和经济效益造成不利影响。在漫长的 技术迭代过程中,企业意识到维护性工作需要前置,因此预防性维护(设备年检、季度检修、月度检修等)管理手段十分必要。根据设备使用寿命、运行状态和环境变化等因素制定维护计划,提高设备可靠性、减少生产损失、延长设备使用寿命。但由于缺少实时状态数据支撑,无法做到精准运维。“十四五”规划及新质生产力的提出,有效助力了“两化融合”工作。企业逐渐意识到需要利用传感器、数据分析、人工智能等技术进行主动性维护工作,即从设备设计开始到报废整个生命周期内监控故障的诱发因素,从而避免故障的发生,降低设备出现故障的概率。此项技术的难点在于需要整合企业系统孤岛,同时利用新一代信息技术,物联网、大数据、云计算及人工智能等手段,对生产线进行数字化、网络化、智能化改造。

     

    Abstract: Amid the surging wave of informatization and the maturing digital technologies, traditional manufacturing industries face transformational challenges. This paper introduces a predictive maintenance methodology grounded in full lifecycle equipment management, advocating a paradigm shift from passive maintenance to proactive predictive prevention as a new productive paradigm for manufacturing. The proposed technology adopts a cloud-edge-device collaborative architecture, integrating two core themes: smart production and intelligent operations and maintenance, while requiring foundational data standardization infrastructure. This study demonstrates the technical feasibility of predictive maintenance for equipment and facilities. Using an anonymized marine equipment smart manufacturing base as a case study, it showcases the advanced application outcomes of this technology in intelligent manufacturing facilities.

     

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