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利用人工智能驱动的预测性维护彻底改变日常生产.pdf

上传人: 拾亿 编号:751879 2025-07-29 17页 2.48MB

1、Unrestricted|Siemens 2025|Siemens F&B Technology Summit 2025 I ThailandRevolutionizing daily production with AI Driven Predictive MaintenanceShalabh BakshiVice President ASEANSiemens Digital IndustriesShaping the future of F&B manufacturingUnrestricted|Siemens 2025|Siemens F&B Technology Summit 2025

2、 I ThailandProduction Evolution with“Standardization”and“Technology Implementation”AdaptiveproductionAutomatedproductionAutonomousproductionaccelerated totowardsUnrestricted|Siemens 2025|Siemens F&B Technology Summit 2025 I ThailandUnrestricted|Siemens 2025|Siemens F&B Technology Summit 2025 I Thail

3、andOur own KPIs/Goals holds the key to Smart ProductionTimeQualityCostAdaptabilityAgilityCircularityEnergy EfficiencyResource EfficiencyDecarbonizationFlexibilityKPIsUnrestricted|Siemens 2025|Siemens F&B Technology Summit 2025 I ThailandFaster ideation and implementationIntegrate IT methodologies li

4、ke the ability to deploy functionalities wherever needed and increased openness to any communication standards,protocols and APIs.Intuitive interdisciplinary collaborationReinforce your workforce with IT-minded engineers and foster a creative co-creation environment between OT and IT departments wit

5、h parallel,interdisciplinary workflows.Improved operational decision makingBy bringing together IT and OT data,new patterns emerge and reveal new insights that allow for data-driven operations decisions in near real-time.Easier scaling of operationsSeamlessly scale computing resources on demand and

6、flexibly exchange software modules during operations to quickly respond to changing requirements.Key Pillars to drive Smart productionfrom Automated to Adaptive productionUnrestricted|Siemens 2025|Siemens F&B Technology Summit 2025 I ThailandBreak the silos to enable“operational decision making and

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本文主要介绍了如何通过AI驱动的预测性维护来革新日常生产,实现智能生产。关键点如下: 1. 生产演变:通过标准化和技术实施,实现从自动化到自适应生产的转变。 2. 数据整合:将IT和OT数据结合,为实时数据驱动的运营决策提供新见解。 3. 预测性维护:利用人工智能分析各种机器,提前发现潜在故障,降低计划外停机时间和维护成本。 4. Senseye软件:提供自动化资产智能和预测性维护,可轻松扩展至成千上万的设备。 5. 业务成果:实现高达50%的计划外停机时间减少,40%的维护成本降低,以及55%的维护人员生产力提升。 引用核心数据:全球汽车制造商在不到3个月内实现投资回报,单一站点避免数千万美元的停机时间;全球食品饮料生产商在6个月内实现投资回报,提高效率并减少手动劳动。
"如何实现智能生产转型?" "预测性维护真的有效吗?" "AI如何助力工业运营决策?"
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