Demonstrating the feasibility of applying machine learning models to railway condition data: Engine condition monitoring and failure prediction

展示将机器学习模型应用于铁路状况数据的可行性：发动机状况监测和故障预测

• 批准号: 10080979
• 金额: $ 6.37万
• 依托单位: CHROME ANGEL SOLUTIONS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10080979
• 关键词: Demonstrating; feasibility; applying; machine; learning

The rail industry is undergoing a digital revolution, which has created opportunities to transform the way we are able to interact with older "mid-life" trains. Historically it has been technically and economically challenging to harness data from older assets - which is invaluable for understanding asset condition, optimising maintenance and predicting emerging failures.Whilst digital enhancements in recent years have rapidly accelerated the ability to extract valuable data from these trains, this presents a new challenge to make sense of the mass of data produced from many disparate systems.This project will combine asset knowledge, operational expertise, Data Science capability and the application of Machine Learning tools borne from the Aerospace sector to test the feasibility of using AI and Machine learning tools to extract insights from large amounts of near real-time data from train engines.Chrome Angel Solutions and Amygda Labs are working in collaboration with Angel Trains and Grand Central Trains to explore the feasibility of applying Amygda's innovative machine learning tools to derive insights from engine data. Amygda Labs' unique approach to building ML models using unsupervised learning techniques enables faster of delivery of insights when compared to established approaches, typically reducing the model build time from months to days without relying on domain knowledge, which can be costly and time-consuming.This feasibility study will test whether AI and Machine Learning tools can derive faster and deeper insights compared to current Data Science methods, by detecting relationships in the data and enabling faster and more proactive decision making, leading to better planning and improved asset availability.

铁路行业正在进行数字革命，这创造了机会改变我们能够与较旧的“中年”火车互动的方式。从历史上看，它在技术和经济上在利用较旧资产的数据上一直充满挑战 - 这对于了解资产状况，优化维护，预测新出现的失败是无价的。近年来数字增强功能迅速加速了从这些火车中提取有价值数据的能力，从这些火车中提取有价值的数据，这会带来一项新的挑战，从而使自己的数据构成了多个数据的质量，并将其构成数据范围的范围，并构成了许多差异，并将其组合到许多范围内，并将其组合成众多的范围。机器学习工具的应用从航空航天部门承担，以测试使用AI和机器学习工具从火车引擎中提取大量近实时数据的见解的可行性。ChromeAngel Solutions和Amygda Labs正在与Angel Trains和Grand Central Trains合作进行合作，以探索Amygda Innov Annerove Innerogative Machine Woods的可行性，以启用amyygda的工具，以启用amyygda的工具来启用启发工具。 Amygda Labs' unique approach to building ML models using unsupervised learning techniques enables faster of delivery of insights when compared to established approaches, typically reducing the model build time from months to days without relying on domain knowledge, which can be costly and time-consuming.This feasibility study will test whether AI and Machine Learning tools can derive faster and deeper insights compared to current Data Science methods, by detecting relationships in the data并实现更快，更积极的决策，从而更好地计划和改善资产可用性。