AI elevates predictive maintenance for Kone and ThyssenKrupp
Summary
But to make a success of using IoT and machine learning to improve predictive maintenance, organisations have to break down some internal boundaries between enterprise IT and operational and service engineering, as well as other departments, says Cho.Kone, a fellow giant in elevator, escalator and travellator manufacturing and operation, has developed its own offer in partnership with IBM, based on its AI Watson IoT system (see case study below).PTC, a software company with a history in CAD/CAM technology and control systems, is now offering IoT, augmented reality and predictive maintenance to a much smaller set of businesses than global manufacturing or asset-intensive industries.Neil Ward-Dutton, research director at MWD Advisors, says the application of AI and machine learning, together with cloud computing, has the potential to make predictive maintenance affordable to a wider pool of users.Kone and ThyssenKrupp are showing that asset-intensive industries can use AI and machine learning technologies hosted in the cloud to start to build predictive models that help plan maintenance more effectively, potentially avoiding failures before they happen.