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网络研讨会
网络研讨会
预测性维护——面向远程操作的自动化工厂
maintAI分析您的运营数据，以增加正常运行时间并降低运营成本.
工业运营商面临着管理成本的压力, 满足生产的风险和排放, 可靠性和安全性目标.
今天的资产产生了大量的数据, 但让它有意义并充分利用它可能是一项挑战. 信息通常分散在不同的系统中, 使其难以获得工作流的全面视图和优化操作.
木材结合尖端技术, 机器学习和行业专业知识，优化资产管理. Our maintAI solution solves the problem of varied and diverse data to provide decision-makers with timely and actionable 的见解, enabling you to respond quickly to changing circumstances and identify opportunities for efficiencies.
提高设备可靠性
目标关键系统, equipment and failure modes through prescriptive recommendation models powered by our vast industry database
减少排放
预测和优化发电和燃气系统, 避免与行程相关的燃烧和甲烷泄漏
提升安全
Apply smart strategies and monitoring technologies to remotely diagnose and predict failures while reducing operational cost
在需要的时间和地点关注维护活动
保持你今天的决策能力, 简化您明天的任务，降低现在和将来的维护成本.
维护模型应用最新的行业全球最佳实践, 经验教训和数字技术, combining engineering first principles and AI models to rapidly optimise your maintenance operating model.
我们的方法 lets you make better decisions with your data and create an improvement roadmap to achieve and exceed your performance targets.
优化维护
节省10%的年度维修工时目标.
维护优化是实现运营效率的灵丹妙药.
拥有优化的维护计划, asset owners and operators can reduce costs and extend asset lifespan to unlock the full productivity of their assets.
Wood will partner with you to optimise your operations by eliminating unnecessary maintenance tasks, 优先释放产能，提高可靠性和安全性.
maintAI provides detailed recommendations to optimise your maintenance plans by analysing your CMMS data, 包括纠正维护历史, 计划的工单和资产数据库信息.
减少积压，释放资源
减少20%的积压，对设施风险的变化很小.
产能和现场人员配备是影响成本和盈利能力的持续挑战, 特别是在远程或海上设施. Wood has helped numerous operators tackle this problem by assessing maintenance and asset data at a granular level to release capacity rapidly and safely.
Wood will work with you to analyse your maintenance backlog and identify maintenance spend that can be removed or re-prioritised.
优化备件，减少浪费
节省30%的库存成本，减少停机时间.
有效的库存管理对于成功的维护计划至关重要. 备件哲学等因素, 过时, 供应链洞察, 保存, 消耗率和可用性都有助于有效地执行维护.
伍德将与您一起优化您的供应链, ensuring the right materials are available to complete the work or maintain critical equipment in the event of a failure. 我们与您合作，以减少停机时间和管理库存成本.
将材料信息与使用率联系起来, 我们丰富的经验库和行业标准, maintAI就储存要求和减少废物提供建议.
功能
待办事项列表优化
利用人工智能, data processing and domain k现在ledge to recommend backlog removal and prioritise reliability improvements.
预见性维护
Uses advanced data analysis and machine learning to identify equipment failures before they occur, 使维护团队能够主动执行维修并避免计划外停机
备件优化
提供关于保持要求的建议, 将材料数据与使用率联系起来, 我们的经验库和行业标准.
维护策略优化
利用CMMS数据，如纠正维修历史, 计划维护工作订单和资产数据库信息, 我们的模型提供建议，以优化维护计划.
maintAI将AI和领域专业知识应用于您的数据和流程
从端到端优化设备维护和操作
这个过程
1
获得对齐
We understand your requirements and align with your processes to deliver solutions quickly and accurately, 不影响您当前的操作.
2
配置模型
Models are optimised for performance and scalability and validated against industry standards.
我们根据您的独特数据定制模型，以推进您的组织目标.
3
应用建议
maintAI outputs provide actionable 的见解 with a data-driven foundation to help stakeholders make informed decisions.
我们的模型是灵活的，可以根据设施的变化进行调整.
4
监测结果
Wood will work with you to extract value from the 的见解 maintAI generates and create a plan for ongoing maintenance optimisation.
5
得到保证
We provide transparency so you can trust the outputs from our models as a layer of validation and risk mitigation.
结果
中可见的结果
8-16
周
维护成本降低
20-40%
平均
计划外停机
10%
典型客户减值
通过应用我们的维护模型，您将得到:
- 一条通往智能维护的清晰道路，将节约与维护和设施健康联系起来
- 优化的“低接触”、智能维护策略结果
- 增加关键资产的可用性
- 降低维修成本，不造成生产损失
- 减少生产损失和起下钻次数，以及与起下钻相关的排放
- 减少了对船上人员的需求，并专注于可靠性优化
- 实时更新策略，设备维护和积压改进建议
常见问题
是的. 我们知道每个设施都是不同的, maintAI被设计成可以跨各种各样的数据集工作.
我们会分析你的数据, technology and systems upfront to make sure to deliver results in a way that works for you.
maintAI与您现有的流程和系统一起工作. Our industry standard models and assumptions will be tailored to your existing processes and ways of working. If there is a specific feature you are looking for, we will work with you to develop and deploy it.
maintAI旨在与您现有的工具和过程集成. We will work with you to identify the data sources required and will then connect the data feeds to our models.
Reporting and visualisations can be calibrated to combine inputs and output from our models into a single visualisation.
是的，维护可以帮助在生产受到影响之前预测故障. Our predictive maintenance solution looks for leading indicators of declining equipment health and uses advanced data analysis and machine learning to identify potential failures before they occur.
Your maintenance teams will be alerted to perform repairs proactively and avoid unplanned downtime.
maintAI可以利用您的资产可以提供的任何相关数据集. 计算机化维修管理系统(CMMS)数据，如工作订单历史记录, 物料清单, 资产层次结构, 等, 都可以使用.
所需的输入根据您的行业和资产类型而有所不同. Our team will work with you to identify the right data to generate useful 的见解 for your workflow.
一旦确定了所有相关数据, it will be cleansed and analysed so any missing data is identified and accounted for in the model outputs.
maintAI评估当前的维护策略、设备健康状况和库存水平.
就在何处集中注意力和优先安排开支提供建议. 它还确定了可以减少支出的领域, 例如不增值的维护任务或超额持有.
通过转移重点和预算来实现回报最大化, 我们的客户可以看到维护操作的显著节省.
而不是依赖于行业标准和最佳实践假设, maintAI直接从资产中获取CMMS数据, 因此，建议和见解是基于您当前的设施数据.
We can identify differences in equipment at your facilities and use this k现在ledge to evaluate where the maintenance spend can be optimised.
是的, reports and 的见解 from maintAI support decision-making from high-level strategic planning down to on-site tasks and technical recommendations.
By connecting operational data from various sources and extracting 的见解 from current asset health, recommendations are designed to align with your strategic vision and will generate a roadmap to help you achieve them.
在Wood，数据安全和隐私是最重要的. We have implemented a secure cloud architecture and work 关闭ly with our customers to guarantee the safety and security of their data.
联系我们 了解更多信息.
伍德一直在优化设备的维护和可靠性
资产密集型产业25年以上.
- 技术支持的独立工程师 -将领域专业知识与数据驱动模型相结合
- 在我们的能力提供优越 -充分补充内部专家知识和经验
- 问题解决者 -结合桌面模拟和现场专家的最佳解决方案开发
- 思想领袖 -基于我们对研究的坚定承诺, 创新与知识共享, 凭借数十年的经验
- 服务一流 – in-house software tools, resulting in improved accuracy, faster service and lower costs
- 与我们的交付一致 -检验、模拟和风险评估的标准流程和质量控制
相关专业知识
相关的见解
保持联系
为了我们的共同目标团结起来，为世界上最严峻的挑战找到解决方案, 我们为未来做好了准备, 现在.
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