Komatsu deploys Cloudera’s IIoT platform

20 November 2017 (Last Updated November 20th, 2017 11:36)

Heavy equipment manufacturer Komatsu has implemented machine learning and analytics company Cloudera’s Cloud-based industrial internet of things (IIoT) analytics platform in an effort to ensure improved performance for mining firms.

Heavy equipment manufacturer Komatsu has implemented machine learning and analytics company Cloudera’s Cloud-based industrial internet of things (IIoT) analytics platform in an effort to ensure improved performance for mining firms.

Powered by Cloudera Enterprise and Microsoft Azure, the platform is aimed at enabling Komatsu’s customers in the mining industry to continuously monitor the performance of equipment used in surface and underground mining.

Furthermore, the platform helps companies increase asset utilisation and productivity, as well as delivering essential resources, including energy and industrial minerals.

Komatsu analytics senior manager Anthony Reid said: “With Cloudera’s modern platform, we use advanced data analytics and machine learning to power our IIoT success.

“We now provide customers with better recommendations on machine utilisation and deliver services faster.

“We use advanced data analytics and machine learning to power our IIoT success.”

“By deploying Cloudera Enterprise on Microsoft Azure, our teams make the invisible visible, gaining valuable insights to help customers optimise productivity and mining efficiencies.”

As an IIoT-based service, Komatsu’s JoySmart Solutions is focused on helping customers optimise equipment performance using machine data and analytics.

The platform stores and processes data collected from mining equipment operating worldwide.

With the implementation of the analytics platform, data scientists at Komatsu can now build and deploy machine learning models to understand equipment operation, as well as offer insights for customers.

The company uses components, including Apache Kudu and Apache Spark to drive real-time processing, machine learning, and analytics on all IoT data.