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

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非常宝贵。虽然近年来数字技术的增强迅速加快了从这些列车中提取有价值数据的能力，但这些数据仍然可以帮助您更好地利用旧资产。这就提出了一个新的挑战，即如何理解从许多不同系统产生的大量数据。该项目将把联合收割机资产知识、操作专业知识数据科学能力和航空航天领域的机器学习工具的应用，以测试使用人工智能和机器学习工具从大量接近真实的数据中提取见解的可行性。火车引擎的时间数据。Chrome Angel Solutions和Amygda Labs正在与Angel Trains和Grand Central Trains合作，探索应用Amygda的创新机器学习工具，从发动机数据中获得见解。Amygda Labs使用无监督学习技术构建机器学习模型的独特方法，与现有方法相比，可以更快地交付洞察力，通常可以将模型构建时间从数月缩短到数天，而无需依赖昂贵且耗时的领域知识。这项可行性研究将测试AI和机器学习工具是否可以获得比当前数据科学方法更快更深入的洞察力，通过检测数据中的关系，实现更快、更主动的决策，从而实现更好的规划和更高的资产可用性。

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.