How KONE Uses Data Analytics with Event-Driven Compute

5 min read

By: Fareed Ahmed

From streaming data to data analytics

As a global leader in the elevator and escalator industry, KONE produces elevators, escalators, and automatic doors for buildings. Given the potential impact of faulty equipment on business complexes, residences, or even a whole city, KONE’s 450,000 customers need to keep their equipment in optimal condition at all times.

KONE is investing in cloud and Internet of Things (IoT) technologies to power a data analytics and predictive maintenance solution for city infrastructure used by more than one billion people daily. Gathering and analyzing usage data from KONE equipment enables customers to fix the root causes of potential failures before they happen.

As part of providing the highest level of service, KONE launched a new maintenance program: 24/7 Connected Services. The goal of 24/7 Connected Services is to eliminate or reduce the time that KONE equipment goes out of service.

24/7 Connected Services connects customer equipment to the cloud so that we can monitor it, analyze usage data to predict future faults, and keep detailed information on maintenance history. Sensor data from the equipment can reveal potential issues and risks before human eyes and ears can detect them. Being able to address these before they grow into problems improves user safety.

KONE uses the Watson IoT Platform running on IBM Cloud to collect streams of incoming data about movement, vibration, loading, and other factors that have an impact on our equipment. We need a highly scalable way to handle the large volume of streaming data, and that’s where IBM Cloud Functions fits in perfectly. IBM Cloud Message Hub and IBM Cloud Functions together provide a serverless, event-driven architecture that allocates the compute resources required to handle each incoming stream of data, automatically scaling as needed.

Guide on serverless microservices

From data analytics to event-driven action

We use IBM Predictive Maintenance and Quality (PMQ) to perform real-time analysis on operations data, identifying potential problems and generating predictions. We expose the results of data analytics to both field technicians and customers through our Cloud View app. Cloud View enables users to monitor and visualize the current behavior of the equipment within a timeline of past maintenance. Using this information, and our event-driven application architecture, we can perform better-quality maintenance on site, often automatically identifying and scheduling a fix before potential faults impact service for our customers.

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