iPredicta RIOT

该项目涉及两个政府优先事项.提高制造业中小企业的生产力，减少碳足迹，到2050年实现净零排放。制造业中小企业的主要资本支出成本是新工厂和设备，如CNC或AM机器。然而，由于缺乏实时的机器数据、商业智能应用和规划系统，许多中小企业的工厂开工率很低，这不仅影响生产率，还浪费能源。机器监控解决方案已经上市20多年了，尽管对生产率有潜在的影响，它们的采用受到限制，特别是在中小企业部门。我们调查了我们400多个代表性样本英国制造业客户了解为什么他们以前没有采用机器监控技术，他们的保留意见如下：* 硬件和软件成本 * 工厂集成和实施服务的复杂性和成本 * 缺乏系统灵活性Fitfactory已经开始通过开发超低成本的振动检测传感器来解决这些挑战，该传感器可以连接到任何使用磁铁。我们的Fitfactory商业智能应用程序- Insights -与不同的ERP系统和我们的Rapid IOT振动传感器无缝连接，不仅可以提供利用率数据和自动警报，还可以为生产团队生成可操作的见解和风险，以监控和添加他们的经验教训。然而，要创建一个改变游戏规则的平台，我们必须部署可以轻松应用于多个用例的机器学习AI，通过获取传感器振动数据，并分析经验教训，创建一个智能预测工具，使运营商能够做出更明智的车间规划决策，以最大限度地减少停机时间和能源使用。与领先的英国RTO合作，我们相信，我们能够克服复杂的分析挑战，创建一个直观的平台，可以快速配置和大规模部署，以加速优化中小企业制造工厂的利用率至少10%，并减少5%的能源使用.STFC将提供专家支持，以创建一个灵活的机器学习解决方案，应用于所有制造工厂和设备，使用简单的用户界面，以最低的成本快速配置，让生产操作员捕捉停机时间的各种定性根本原因分析，结合定量工厂利用率分析，预测机床磨损，并通过改进的规划决策支持来减少停机时间。

This project addresses 2 Government priorities...improving productivity of manufacturing SMEs and reducing carbon footprint to achieve net zero by 2050\.A major capex cost for manufacturing SMEs is new plant & equipment such as CNC or AM machines. However due to a lack of real-time machine data, business intelligence applications and planning systems, many SMEs operate under utilised plant which not only impacts productivity but also wastes energy.Machine monitoring solutions have been on the market for more than 20 years however, despite the potential impact on productivity, their adoption has been limited especially within the SME sector.We surveyed a representative sample of our 400+ UK manufacturing clients to understand why they had not adopted machine monitoring technologies previously and their reservations were as follows:* cost of hardware and software* complexity and cost of plant integration and implementation services* lack of systems flexibilityFitfactory have started to address these challenges by developing a super low cost vibration detection sensor that can be attached to the outside of any machine using magnets.Our Fitfactory Business Intelligence application -- Insights - connects seamlessly with disparate ERP systems and our Rapid IOT vibration sensor to not only present utilisation data and automate alerts but also generates actionable insights and risks for production teams to monitor and add their lessons learned.However, to create a game-changing platform, we must deploy machine learning AI that can be easily applied for multiple use cases, by taking the sensor vibration data, and analysing the lessons learned, to create an intelligent prediction tool that empowers operators so that they can make more informed shop floor planning decisions to minimise downtime and energy usage.By collaborating with a leading UK RTO, we are confident that we can overcome the complex analytical challenges to create an intuitive platform that can be rapidly configured and deployed at scale to accelerate the optimisation of SME manufacturing plant utilisation by at least 10% and reduce energy usage by 5%.STFC will provide specialist support to create a flexible machine learning solution that be applied to all manufacturing plant and equipment that can be quickly configured, at minimum cost, using a simple user interface for production operators to capture various qualitative root cause analysis reasons for downtime, combined with quantitative plant utilisation analysis to predict machine tool wear and to mitigate downtime through improved planning decision support.