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Digital Twin-based Predictive Maintenance with TEKNOPAR’s TIA Platform
技术
- 分析与建模 - 数字孪生/模拟
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 分析与建模 - 过程分析
- 平台即服务 (PaaS) - 数据管理平台
- 传感器 - 化学传感器
- 传感器 - 压力传感器
- 传感器 - 温度传感器
- 传感器 - 电压传感器
适用行业
- 设备与机械
- 金属
适用功能
- 流程制造
用例
- 数字孪生
- 机器状态监测
- 预测性维护
服务
- 软件设计与工程服务
挑战
预测性维护是一种先进的设备管理方法，它利用机器学习不断监测和评估机械状况。这种方法可帮助制造商预测潜在故障，大幅降低生产成本，优化设备使用率并提高生产率。预测性维护的关键活动包括持续监测设备健康状况、数据驱动的状况评估以及使用高级算法预测潜在故障。数字孪生（物理对象的数字副本，与其环境相关）在此过程中发挥着至关重要的作用。
土耳其一家领先的螺旋焊接钢管制造商面临着巨大的生产挑战。该工厂的生产过程依赖于一系列相互依赖的机器，极易受到干扰。任何机器故障都会导致整条生产线停工，从而导致不可预测的长期停机。此外，由于缺乏足够的故障数据，因此需要使用高保真混合模型生成合成数据。
关于客户
土耳其领先的螺旋焊接钢管制造商
解决方案
TEKNOPAR 在工厂内实施了其 TIA 平台，在机器上部署了一系列传感器来收集全面的数据。其中包括 26 个温度传感器、11 个振动传感器、10 个电流传感器、12 个压力传感器和 1 个油污染传感器。开发了认知数字孪生，以实时监控生产过程和机器状态。
系统启动后，检测到机器内温度异常高。最初，操作员和维护团队对这些读数表示怀疑。然而，使用热像仪进行验证证实了数字孪生数据的准确性，因此立即关闭机器以防止进一步损坏。数字孪生促成的这种快速反应避免了一场潜在的灾难。
收集的数据
Current, Oil Contamination, Pressure, Temperature, Vibration
运营影响
数量效益
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