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.

铁路行业正在经历一场数字革命，这为改变我们与老式“中年”列车的互动方式创造了机会。从历史上看，利用旧资产的数据在技术上和经济上都具有挑战性，而这些数据对于了解资产状况、优化维护和预测新出现的故障是非常宝贵的。近年来，数字技术的发展迅速提高了从这些列车中提取有价值数据的能力，但这对理解来自许多不同系统产生的大量数据提出了新的挑战。该项目将结合资产知识、运营专业知识、数据科学能力以及来自航空航天领域的机器学习工具的应用，测试使用人工智能和机器学习工具从火车发动机的大量近实时数据中提取见解的可行性。Chrome Angel Solutions和Amygda Labs正在与Angel Trains和Grand Central Trains合作，探索应用Amygda创新的机器学习工具从发动机数据中获取见解的可行性。与已建立的方法相比，Amygda实验室使用无监督学习技术构建ML模型的独特方法可以更快地交付见解，通常可以将模型构建时间从几个月减少到几天，而无需依赖领域知识，这可能是昂贵和耗时的。这项可行性研究将测试人工智能和机器学习工具是否可以通过检测数据中的关系，实现更快、更主动的决策，从而获得比当前数据科学方法更快、更深入的见解，从而更好地规划和提高资产可用性。