Internet of Things (IoT) approach for predictive maintenance: a manufacturing case study

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The project team are working with Tinsley Bridge, a manufacturing business in Sheffield, to investigate the feasibility of IoT-based predictive maintenance solutions.

Predictive maintenance  anticipates maintenance needs to avoid costs associated with unscheduled downtime. This ability to predict when equipment or assets need maintenance allows us to optimise equipment lifetime and minimise downtime.

Previously, the lack of availability of sensors generating data as well as a lack of computational resources for gathering and analysing the data made it difficult to implement predictive maintenance. Today, advances in the Internet of Things (IoT), cloud computing, data analytics, and machine learning are enabling predictive maintenance to go mainstream.

However, it is still in the early stage to apply predictive maintenance to the general manufacturing industry. For the manufacturers, there are apparent barriers to adopting the new technologies out of their traditional engineering domains; furthermore, it is also difficult to develop reasonable operational cost models without sufficient experience. Applying the technology to real-world industrial case studies will provide invaluable guidance and learning for the wider industry.

Tinsley Bridge has a number of high-value assets that enable key manufacturing processes. These assets incur significant manual costs for maintenance and quality control which introduction of Industrial Internet of Things (IIoT) could avoid. Working with the Digital Manufacturing team from Automatic Control and Systems Engineering (ACSE) at the University of Sheffield, Tinsley Bridge will look to adopt Siemens cloud platform, MindSphere, to develop an industrial use case.

“For Tinsley Bridge, it’s about joining up the all the equipment, people and information systems, creating a smarter factory that will help us manufacture technically advanced products.” Mark Webber, Managing Director at Tinsley Bridge

Project lead: Professor Ashutosh Tiwari, the University of Sheffield

Project partners: 

 

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